diff --git a/1-Introduction/02-ethics/1-fundamentals.md b/1-Introduction/02-ethics/1-fundamentals.md deleted file mode 100644 index f86dccac..00000000 --- a/1-Introduction/02-ethics/1-fundamentals.md +++ /dev/null @@ -1,109 +0,0 @@ - -## 1. Ethics Fundamentals -[Back To Introduction](README.md#introduction) - -### 1.1 What is Ethics? - -The term "ethics" [comes from](https://en.wikipedia.org/wiki/Ethics) the Greek term "ethikos" - and its root "ethos", meaning _character or moral nature_. Think of ethics as the set of **shared values** or **moral principles** that govern our behavior in society. Our code of ethics is based on widely accepted ideas on what is _right vs. wrong_, creating informal rules (or "norms") that we follow voluntarily to ensure the good of the community. - -Ethics is critical for scientific research and technology advancement. The [Research Ethics timeline](https://www.niehs.nih.gov/research/resources/bioethics/timeline/index.cfm) gives examples from the past four centuries - including Charles Babbage's 1830 [Reflections on the Decline of Science in England ..](https://books.google.com/books/about/Reflections_on_the_Decline_of_Science_in.html) where he discusses dishonesty in data science approaches including fabrication of data to support desired outcomes. Ethics became _guardrails_ to prevent data misuse and protect society from unintended consequences or harms. - -_Applied Ethics_ is about the practical adoption of ethical principles and practices when developing new processes or products. It's about asking moral questions ("is this right or wrong?"), evaluating tradeoffs ("does this help or harm society more?") and taking informed actions to ensure compliance at individual and organizational levels. Ethics are *not* laws. But they can influence the creation of legal or social frameworks that support governance such as: - - * **Professional codes of conduct.** | For users or groups e.g., The [Hippocratic Oath](https://en.wikipedia.org/wiki/Hippocratic_Oath) (460-370 BC) for medical ethics defined principles like data confidentiality (led to _doctor-patient privilege_ laws) and non-maleficence (popularly known as _first, do no harm_) that are still adopted today. - * **Regulatory standards** | For organizations or industries e.g., The [1996 Health Insurance Portability and Accountability Act](https://en.wikipedia.org/wiki/Health_Insurance_Portability_and_Accountability_Act) (HIPAA) mandated theft and fraud protections for _personally identifiable information_ (PII) collected by the healthcare industry - and stipulated how that data could be used or disclosed. - -### 1.2 What is Data Ethics? - -Data ethics is the application of ethics considerations to the domain of big data and data-driven algorithms. - - * [Wikipedia](https://en.wikipedia.org/wiki/Big_data_ethics) defines big data ethics as _systemizing, defending, and recommending, concepts of right and wrong conduct in relation to data_ - focusing on implications for **personal data**. - * A [Royal Society article](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360#sec-1) defines data ethics as a new branch of ethics that _studies and evaluates moral problems related to **data, algorithms and corresponding practices** .. to formulate and support morally good solutions (right conduct or values)_. - -The first definition puts it in perspective of users ("personal data") while the second puts it in perspective of operations ("data, algorithms, practices") where: - -* `data` = generation, recording, curation, dissemination, sharing & usage -* `algorithms` = AI, agents, machine learning, bots -* `practices` = responsible innovation, ethical hacking, codes of conduct - -Based on this, we can define data ethics as the study and evaluation of _moral questions_ related to data collection, algorithm development, and industry-wide models for governance. We'll explore these questions in the "how" section, but first let's talk about the "why". - -### 1.3 Why Data Ethics? - -To answer this question, let's look at recent trends in the big data and AI industries: - - * [_Statista_](https://www.statista.com/statistics/871513/worldwide-data-created/) - By 2025, we will be creating and consuming over **180 zettabytes of data**. - * _[Gartner](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/)_ - By 2022, 35% of large orgs will buy & sell data in **online Marketplaces and Exchanges** - * _[Gartner](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/)_ - AI **democratization and industrialization** are the new Hype Cycle megatrends. - -The first trend tells us that _data scientists_ will have unprecedented levels of access to personal data at global scale, building algorithms to fuel an AI-driven economy. The second trend tells us that economies of scale and efficiencies in distribution will make it easier and cheaper for _developers_ to integrate AI into more everyday consumer experiences. - -The potential for harm occurs when algorithms and AI get _weaponized_ against society in unforeseen ways. In [Weapons of Math Destruction](https://www.youtube.com/watch?v=TQHs8SA1qpk) author Cathy O'Neil talks about the three elements of AI algorithms that pose a danger to society: _opacity_, _scale_ and _damage_. - - * **Opacity** refers to the black box nature of many algorithms - do we understand why a specific decision was made, and can we _explain or interpret_ the data reasoning that drove the predictions behind it? - * **Scale** refers to the speed with which algorithms can be deployed and replicated - how quickly can a minor algorithm design flaw get "baked in" with use, leading to irreversible societal harms to affected users? - * **Damage** refers to the social and economic impact of poor algorithmic decision-making - how can bad or unrepresentative data lead to unfair algorithms that disproportionately harm specific user groups? - -So why does data ethics matter? Because democratization of AI can speed up weaponization, creating harms at scale in the absence of ethical guardrails. While industrialization of AI will motivate better governance - giving data ethics an important role in shaping policies and standards for developing responsible AI solutions. - - -### 1.4 How To Apply Ethics? - -We know what data ethics is, and why it matters. But how do we _apply_ ethical principles or practices as data scientists or developers? It starts with us asking the right questions at every step of our data-driven pipelines and processes. These [Six questions about data science ethics](https://halpert3.medium.com/six-questions-about-data-science-ethics-252b5ae31fec) are a good starting point: - - 1. Is the data fair and unbiased? - 2. Is the data being used fairly and ethically? - 3. Is (user) privacy being protected? - 4. To whom does data belong, company or user? - 5. What effects do data and algorithms have on society? - 6. Is the data manipulated or deceiving? - -The [22 questions for ethics in data and AI](https://medium.com/the-organization/22-questions-for-ethics-in-data-and-ai-efb68fd19429) article expands this into a framework, grouping questions by stage of processing: _design_, _implementation & management_, _systems & organization_. The [O'Reilly Ethics and Data Science](https://resources.oreilly.com/examples/0636920203964/) book advocates strongly for _checklists_, asking simple `have we done this? (y/n)` questions that improve ethics oversight without the overheads caused by analysis paralysis. - -And tools like [deon](https://deon.drivendata.org/) -make it frictionless to integrate [ethics checklists](https://deon.drivendata.org/#data-science-ethics-checklist) into your project workflows. Deon builds on [industry practices](https://deon.drivendata.org/#checklist-citations), shares [real-world examples](https://deon.drivendata.org/examples/) that put the ethical challenges in context, and allows practitioners to derive custom checklists from the defaults, to suit specific scenarios or industries. - -### 1.5 Ethics Concepts - -Ethics checklists often revolve around yes/no questions related to core ethics concepts and challenges. Let's look at _a subset_ of these issues - inspired in part by the [deon ethics checklist](https://deon.drivendata.org/#data-science-ethics-checklist) - in two contexts: data (collection and storage) and algorithms (analysis and modeling). - -**Data Collection & Storage** - * _Ownership_: Does the user own the data? Or the organization? Is there an agreement that defines this? - * _Informed Consent_: Did human subjects give permission for data capture & understand purpose/usage? - * _Collection Bias_: Is data representative of audience? Did we identify and mitigate biases? - * _Data Security_: Is data stored and transmitted securely? Are valid access controls enforced? - * _Data Privacy_: Does data contain personally identifiable information? Is anonymity preserved? - * _Right to be Forgotten_: Does user have mechanism to request deletion of their personal information? - -**Data Modeling & Analysis** - - * _Data Validity_: Does data capture relevant features? Is it timeless? Is the data model valid? - * _Misrepresentation_: Does analysis communicate honestly reported data in a deceptive manner? - * _Auditability_: Is the data analysis or algorithm design documented well enough to be reproducible later? - * _Explainability_: Can we explain why the data model or learning algorithm made a specific decision? - * _Fairness_: Is the model fair (e.g., shows similar accuracy) across diverse groups of affected users? - -Finally, let's talk about two abstract concepts that often underlie users' ethics concerns around technology: - - * **Trust**: Can we trust an organization with our personal data? Can we trust that algorithmic decisions are fair and do no harm? Can we trust that information is not misrepresented? - * **Choice**: Do I have free will when I make a choice in a consumer UI/UX? Are data-driven [choice architectures](https://en.wikipedia.org/wiki/Choice_architecture) nudging me towards good choices or are [dark patterns](https://www.darkpatterns.org/) working against my self-interest? - - -### 1.6 Ethics History - -Knowing ethics concepts is one thing - understanding the intent behind them, and the potential harms or societal consequences they bring, is another. Let's look at some case studies that help frame ethics discussions in a more concrete way with real-world examples. - -| Historical Example | Ethics Issues | -|---|---| -| _[Facebook Data Breach](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users)_ exposes data for 530M users. Facebook pays $5B to FTC, does not notify users. | Data Privacy, Data Security, Transparency, Accountability | -| [Tuskegee Syphillis Study](https://en.wikipedia.org/wiki/Tuskegee_syphilis_experiment) - African-American men were enrolled in study without being told its true purpose. Treatments were withheld. | Informed Consent, Fairness, Social / Economic Harms | -| [MIT Gender Shades Study](http://gendershades.org/index.html) - evaluated accuracy of industry AI gender classification models (used by law enforcement), detected bias | Fairness, Social/ Economic Harms, Collection Bias | -| [Learning app ABCmouse pays $10 million to settle FTC complaint it trapped parents in subscription they couldn’t cancel](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/) - user experience masked context, nudged user towards choices with financial harms| Misrepresentation, Free Choice, Dark Patterns, Economic Harms | -| [Netflix Prize Dataset de-anonymized by correlation](https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/) - showed how Netflix prize dataset of 500M users was easily de-anonymized by cross-correlation with public IMDb comments (and other such datasets) | Data Privacy, Anonymity, De-identification | -| [Georgia COVID-19 cases not declining as quickly as data suggested](https://www.vox.com/covid-19-coronavirus-us-response-trump/2020/5/18/21262265/georgia-covid-19-cases-declining-reopening) - graphs released had x-axis not ordered chronologically, misleading viewers| Misrepresentation, Social Harms | - -We covered just a subset of examples, but recommend you explore these resources for more: - - * [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - ethics dilemmas across diverse industries. - * [Data Science Ethics course](https://www.coursera.org/learn/data-science-ethics#syllabus) - landmark case studies in data ethics. - * [Where things have gone wrong](https://deon.drivendata.org/examples/) - deon checklist examples of ethical issues diff --git a/1-Introduction/02-ethics/2-collection.md b/1-Introduction/02-ethics/2-collection.md deleted file mode 100644 index 323b3087..00000000 --- a/1-Introduction/02-ethics/2-collection.md +++ /dev/null @@ -1,3 +0,0 @@ - -## 2. Data Collection -[Back To Introduction](README.md) \ No newline at end of file diff --git a/1-Introduction/02-ethics/3-privacy.md b/1-Introduction/02-ethics/3-privacy.md deleted file mode 100644 index 93b6e22d..00000000 --- a/1-Introduction/02-ethics/3-privacy.md +++ /dev/null @@ -1,3 +0,0 @@ - -## 3. Data Privacy -[Back To Introduction](README.md) \ No newline at end of file diff --git a/1-Introduction/02-ethics/4-fairness.md b/1-Introduction/02-ethics/4-fairness.md deleted file mode 100644 index edd8bf2b..00000000 --- a/1-Introduction/02-ethics/4-fairness.md +++ /dev/null @@ -1,3 +0,0 @@ - -## 4. Algorithm Fairness -[Back To Introduction](README.md) \ No newline at end of file diff --git a/1-Introduction/02-ethics/5-consequences.md b/1-Introduction/02-ethics/5-consequences.md deleted file mode 100644 index 634f3ee5..00000000 --- a/1-Introduction/02-ethics/5-consequences.md +++ /dev/null @@ -1,3 +0,0 @@ - -## 5. Societal Consequences -[Back To Introduction](README.md) \ No newline at end of file diff --git a/1-Introduction/02-ethics/6-summary.md b/1-Introduction/02-ethics/6-summary.md deleted file mode 100644 index 8837cd43..00000000 --- a/1-Introduction/02-ethics/6-summary.md +++ /dev/null @@ -1,3 +0,0 @@ - -## 6. Summary & Resources -[Back To Introduction](README.md) \ No newline at end of file diff --git a/1-Introduction/02-ethics/README.md b/1-Introduction/02-ethics/README.md index b59130ea..a2eda93c 100644 --- a/1-Introduction/02-ethics/README.md +++ b/1-Introduction/02-ethics/README.md @@ -6,35 +6,174 @@ ## Sketchnote 🖼 -| A Visual Guide to Data Ethics by [Nitya Narasimhan](https://twitter.com/nitya) / [(@sketchthedocs)](https://sketchthedocs.dev)| -|---| -|







| +> A Visual Guide to Data Ethics by [Nitya Narasimhan](https://twitter.com/nitya) / [(@sketchthedocs)](https://sketchthedocs.dev) -## Introduction +## 1. Introduction -What is ethics? What does data ethics mean, and how is it relevant to data scientists and developers in the context of big data, machine learning, and artificial intelligence? This lesson explores these ideas under the following sections: +This lesson will look at the field of _data ethics_ - from core concepts (ethical challenges & societal consequences) to applied ethics (ethical principles, practices and culture). Let's start with the basics: definitions and motivations. - * [**Fundamentals**](1-fundamentals) - Understand definitions, motivation and core concepts. - * [**Data Collection**](2-collection) - Explore data ethics issues around data ownership, user consent and control. - * [**Data Privacy**](3-privacy) - Understand degrees of privacy, challenges in anonymity and leakage, and user rights. - * [**Algorithm Fairness**](4-fairness) - Explore consequences & harms of algorithm bias and data misrepresentation. - * [**Societal Consequences**](5-consequences) - Explore socio-economic issues and case studies related to data ethics. - * [**Summary & Resources**](6-summary) - Wrap-up with a review of current data ethics practices and resources. +### 1.1 Definitions ---- +**Ethics** [comes from the Greek word "ethikos" and its root "ethos"](https://en.wikipedia.org/wiki/Ethics). It refers to the set of _shared values and moral principles_ that govern our behavior in society and is based on widely-accepted ideas of _right vs. wrong_. Ethics are not laws! They can't be legally enforced but they can influence corporate initiatives and government regulations that help with compliance and governance. + +**Data Ethics** is [defined as a new branch of ethics](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360#sec-1) that "studies and evaluates moral problems related to _data, algorithms and corresponding practices_ .. to formulate and support morally good solutions" where: + * `data` = generation, recording, curation, dissemination, sharing and usage + * `algorithms` = AI, machine learning, bots + * `practices` = responsible innovation, ethical hacking, codes of conduct + +**Applied Ethics** is the [_practical application of moral considerations_](https://en.wikipedia.org/wiki/Applied_ethics). If focuses on understanding how ethical issues impact real-world actions, products and processes, by asking moral questions - like _"is this fair?"_ and _"how can this harm individuals or society as a whole?"_ when working with big data and AI algorithms. Applied ethics practices can then focus on taking corrective measures - like employing checklists (_"did we test data model accruacy with diverse groups, for fairness?"_) - to minimize or prevent any unintended consequences. + +**Ethics Culture**: Applied ethics focuses on identifying moral questions and adopting ethically-motivated actions with respect to real-world scenarios and projects. Ethics culture is about _operationalizing_ these practices, collaboratively and at scale, to ensure governances at the scale of organizations and industries. [Establishing an ethics culture](https://hbr.org/2019/05/how-to-design-an-ethical-organization) requires identifying and addressing _systemic_ issues (historical or ingrained) and creating norms & incentives htat keep members accountable for adherence to ethical principles. + + +### 1.2 Motivation + +Let's look at some emerging trends in big data and AI: + + * [By 2022](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/) one-in-three large organizations will buy and sell data via online Marketplaces and Exchanges. + * [By 2025](https://www.statista.com/statistics/871513/worldwide-data-created/) we'll be creating and consuming over 180 zettabytes of data. + +**Data scientists** will have unimaginable levels of access to personal and behavioral data, helping them develop the algorithms to fuel an AI-driven economy. This raises data ethics issues around _protection of data privacy_ with implications for individual rights around personal data collection and usage. + +**App developers** will find it easier and cheaper to integrate AI into everday consumer experiences, thanks to the economies of scale and efficiencies of distribution in centralized exchanges. This raises ethical issues around the [_weaponization of AI_](https://www.youtube.com/watch?v=TQHs8SA1qpk) with implications for societal harms caused by unfairness, misrepresentation and systemic biases. + +**Democratization and Industrialization of AI** are seen as the two megatrends in Gartner's 2020 [Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/), shown below. The first positions developers to be a major force in driving increased AI adoption, while the second makes responsible AI and governance a priority for industries. + + +![](https://images-cdn.newscred.com/Zz1mOWJhNzlkNDA2ZTMxMWViYjRiOGFiM2IyMjQ1YmMwZQ==) + +Data ethics are now **necessary guardrails** ensuring developers ask the right moral questions and adopt the right practices (to uphold ethical values). And they influence the regulations and frameworks defined (for governance) by governments and organizations. + + +## 2. Core Concepts + +A data ethics culture requires an understanding of three things: the _shared values_ we embrace as a society, the _moral questions_ we ask (to ensure adherence to those values), and the potential _harms & consequences_ (of non-adherence). + +### 2.1 Ethical AI Values + +Our shared values reflect our ideas of wrong-vs-right when it comes to big data and AI. Different organizations have their own views of what responsible AI and ethical AI principles look like. + +Here is an example - the [Responsible AI Framework](https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png) from Microsoft defines 6 core ethics principles for all products and processes to follow, when implementing AI solutions: + + * **Accountability**: ensure AI designers & developers take _responsibility_ for its operation. + * **Transparency**: make AI operations and decisions _understandable_ to users. + * **Fairness**: understand biases and ensure AI _behaves comparably_ across target groups. + * **Reliability & Safety**: make sure AI behaves consistently, and _without malicious intent_. + * **Security & Privacy**: get _informed consent_ for data collection, provide data privacy controls. + * **Inclusiveness**: adapt AI behaviors to _broad range of human needs_ and capabilities. + +![Elements of an Responsible AI Framework at Microsoft](https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png) + +Note that accountability and transparency are _cross-cutting_ concerns that are foundational to the top 4 values, and can be explored in their contexts. In the next section we'll look at the ethical challenges (moral questions) raised in two core contexts: + + * Data Privacy - focused on **personal data** collection & use, with consequences to individuals. + * Fairness - focused on **algorithm** design & use, with consequences to society at large. + +### 2.2 Ethics of Personal Data + +[Personal data](https://en.wikipedia.org/wiki/Personal_data) or personally-identifiable information (PII) is _any data that relates to an identified or identifiable living individual_. It can also [extend to diverse pieces of non-personal data](https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en) that collectively can lead to the identification of a specific individual. Examples include: participant data from research studies, social media interactions, mobile & web app data, online commerce transactions and more. + +Here are _some_ ethical concepts and moral questions to explore in context: + +* **Data Ownership**. Who owns the data - user or organization? How does this impact users' rights? +* **Informed Consent**. Did users give permissions for data capture? Did they understand purpose? +* **Intellectual Property**. Does data have economic value? What are the users' rights & controls? +* **Data Privacy**. Is data secured from hacks/leaks? Is anonymity preserved on data use or sharing? +* **Right to be Forgotten**. Can user request their data be deleted or removed to reclaim privacy? + +### 2.3 Ethics of Algorithms + +Algorithm design begins with collecting & curating datasets relevant to a specific AI problem or domain, then processing & analyzing it to create models that can help predict outcomes or automate decisions in real-world applications. Moral questions can now arise in various contexts, at any one of these stages. + +Here are _some_ ethical concepts and moral questions to explore in context: + +* **Dataset Bias** - Is data representative of target audience? Have we checked for different [data biases](https://towardsdatascience.com/survey-d4f168791e57)? +* **Data Quality** - Does dataset and feature selection provide the required [data quality assurance](https://lakefs.io/data-quality-testing/)? +* **Algorithm Fairness** - Does the data model [systematically discriminate](https://towardsdatascience.com/what-is-algorithm-fairness-3182e161cf9f) against some subgroups? +* **Misrepresentation** - Are we [communicating honestly reported data in a deceptive manner?](https://www.sciencedirect.com/topics/computer-science/misrepresentation) +* **Explainable AI** - Are the results of AI [understandable by humans](https://en.wikipedia.org/wiki/Explainable_artificial_intelligence)? White-box (vs. black-box) models. +* **Free Choice** - Did user exercise free will or did algorithm nudge them towards a desired outcome? + +### 2.3 Case Studies + +The above are a subset of the core ethical challenges posed for big data and AI. More organizations are defining and adopting _responsible AI_ or _ethical AI_ frameworks that may identify additional shared values and related ethical challenges for specific domains or needs. + +To understand the potential _harms and consequences_ of neglecting or violating these data ethics principles, it helps to explore this in a real-world context. Here are some famous case studies and recent examples to get you started: + + +* `1972`: The [Tuskegee Syphillis Study](https://en.wikipedia.org/wiki/Tuskegee_Syphilis_Study) is a landmark case study for **informed consent** in data science. African American men who participated in the study were promised free medical care _but deceived_ by researchers who failed to inform subjects of their diagnosis or about availability of treatment. Many subjects died; some partners or children were affected by complications. The study lasted 40 years. +* `2007`: The Netflix data prize provided researchers with [_10M anonymized movie rankings from 50K customers_](https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/) to help improve recommendation algorithms. This became a landmark case study in **de-identification (data privacy)** where researchers were able to correlate the anonymized data with _other datasets_ (e.g., IMDb) that had personally identifiable information - helping them "de-anonymize" users. +* `2013`: The City of Boston [developed Street Bump](https://www.boston.gov/transportation/street-bump), an app that let citizens report potholes, giving the city better roadway data to find and fix issues. This became a case study for **collection bias** where [people in lower income groups had less access to cars and phones](https://hbr.org/2013/04/the-hidden-biases-in-big-data), making their roadway issues invisible in this app. Developers worked with academics to _equitable access and digital divides_ issues for fairness. +* `2018`: The MIT [Gender Shades Study](http://gendershades.org/overview.html) evaluated the accuracy of gender classification AI products, exposing gaps in accuracy for women and persons of color. A [2019 Apple Card](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/) seemed to offer less credit to women than men. Both these illustrated issues in **algorithmic fairness** and discrimination. +* `2020`: The [Georgia Department of Public Health released COVID-19 charts](https://www.vox.com/covid-19-coronavirus-us-response-trump/2020/5/18/21262265/georgia-covid-19-cases-declining-reopening) that appeared to mislead citizens about trends in confirmed cases with non-chronological ordering on the x-axis. This illustrates **data misrepresentation** where honest data is presented dishonestly to support a desired narrative. +* `2020`: Learning app [ABCmouse paid $10M to settle an FTC complaint](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/) where parents were trapped into paying for subscriptions they couldn't cancel. This highlights the **illusion of free choice** in algorithmic decision-making, and potential harms from dark patterns that exploit user insights. +* `2021`: Facebook [Data Breach](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users) exposed data from 530M users, resulting in a $5B settlement to the FTC. It however refused to notify users of the breach - raising issues like **data privacy**, **data security** and **accountability**, including user rights to redress for those affected. + +Want to explore more case studies on your own? Check out these resources: + + * [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - ethics dilemmas across diverse industries. + * [Data Science Ethics course](https://www.coursera.org/learn/data-science-ethics#syllabus) - landmark case studies in data ethics. + * [Where things have gone wrong](https://deon.drivendata.org/examples/) - deon checklist examples of ethical issues + +## 3. Applied Ethics + +We've learned about data ethics values, and the ethical challenges (+ moral questions) associated with adherence to these values. But how do we _implement_ these ideas in real-world contexts? Here are some tools & practices that can help. + +### 3.1 Have Professional Codes + +Professional codes are _moral guidelines_ for professional behavior, helping employees or members _make decisions that align with organizational principles_. Codes may not be legally enforceable, making them only as good as the willing compliance of members. An organization may inspire adherence by imposing incentives & penalties accordingly. + +Professional _codes of conduct_ are prescriptive rules and responsibilities that members must follow to remain in good standing with an organization. A professional *code of ethics* is more [_aspirational_](https://keydifferences.com/difference-between-code-of-ethics-and-code-of-conduct.html), defining the shared values and ideas of the organization. The terms are sometimes used interchangeably. + +Examples include: + + * [Oxford Munich](http://www.code-of-ethics.org/code-of-conduct/) Code of Ethics + * [Data Science Association](http://datascienceassn.org/code-of-conduct.html) Code of Conduct (created 2013) + * [ACM Code of Ethics and Professional Conduct](https://www.acm.org/code-of-ethics) (since 1993) + + +### 3.2 Ask Moral Questions + +Assuming you've already identified your shared values or ethical principles at a team or organization level, the next step is to identify the moral questions relevant to your specific use case and operational workflow. + +Here are [6 basic questions about data ethics](https://halpert3.medium.com/six-questions-about-data-science-ethics-252b5ae31fec) that you can build on: + +* Is the data you're collecting fair and unbiased? +* Is the data being used ethically and fairly? +* Is user privacy being protected? +* To whom does data belong - the company or the user? +* What effects do the data and algorithms have on society (individual and collective)? +* Is the data manipulated or deceptive? + +For larger team or project scope, you can choose to expand on questions that reflect a specific stage of the workflow. For example here are [22 questions on ethics in data and AI](https://medium.com/the-organization/22-questions-for-ethics-in-data-and-ai-efb68fd19429) that were grouped into _design_, _implementation & management_, _systems & organization_ categories for convenience. + +### 3.3 Adopt Ethics Checklists + +While professional codes define required _ethical behavior_ from practitioners, they [have known limitations](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md) for implementation, particularly in large-scale projects. In [Ethics and Data Science](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md)), experts instead advocate for ethics checklists that can **connect principles to practices** in more deterministic and actionable ways. + +Checklists convert questions into "yes/no" tasks that can be tracked and validated before product release. Tools like [deon](https://deon.drivendata.org/) make this frictionless, creating default checklists aligned to [industry recommendations](https://deon.drivendata.org/#checklist-citations) and enabling users to customize and integrate them into workflows using a command-line tool. Deon also provides [real-world examples](ttps://deon.drivendata.org/examples/) of ethical challenges to provide context for these decisions. + +### 3.4 Track Ethics Compliance + +**Ethics** is about doing the right thing, even if there are no laws to enforce it. **Compliance** is about following the law, when defined and where applicable. +**Governance** is the broader umbrella that covers all the ways in which an organization (company or government) operates to enforce ethical principles & comply with laws. -[1. Ethics Fundamentals](1-fundamentals.md ':include') +Companies are creating their own ethics frameworks (e.g., [Microsoft](https://www.microsoft.com/en-us/ai/responsible-ai), [IBM](https://www.ibm.com/cloud/learn/ai-ethics), [Google](https://ai.google/principles), [Facebook](https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/), [Accenture](https://www.accenture.com/_acnmedia/PDF-149/Accenture-Responsible-AI-Final.pdf#zoom=50)) for governances, while state and national governments tend to focus on regulations that protect the data privacy and rights of their citizens. -[2. Data Collection](2-collection.md ':include') +Here are some landmark data privacy regulations to know: + * `1974`, [US Privacy Act](https://www.justice.gov/opcl/privacy-act-1974) - regulates _federal govt._ collection, use and disclosure of personal information. + * `1996`, [US Health Insurance Portability & Accountability Act (HIPAA)](https://www.cdc.gov/phlp/publications/topic/hipaa.html) - protects personal health data. + * `1998`, [US Children's Online Privacy Protection Act (COPPA)](https://www.ftc.gov/enforcement/rules/rulemaking-regulatory-reform-proceedings/childrens-online-privacy-protection-rule) - protects data privacy of children under 13. + * `2018`, [General Data Protection Regulation (GDPR)](https://gdpr-info.eu/) - provides user rights, data protection and privacy. + * `2018`, [California Consumer Privacy Act (CCPA)](https://www.oag.ca.gov/privacy/ccpa) gives consumers more _rights_ over their personal data. -[3. Data Privacy](3-privacy.md ':include') +In Aug 2021, China passed the [Personal Information Protection Law](https://www.reuters.com/world/china/china-passes-new-personal-data-privacy-law-take-effect-nov-1-2021-08-20/) (to go into effect Nov 1) which, with its Data Security Law, will create one of the strongest online data privacy regulations in the world. -[4. Algorithm Fairness](4-fairness.md ':include') -[5. Societal Consequences](5-consequences.md ':include') +### 3.5 Establish Ethics Culture -[6. Summary & Resources](6-summary.md ':include') +There remains an intangible gap between compliance ("doing enough to meet the letter of the law") and addressing systemic issues ([like ossification, information asymmetry and distributional unfairness](https://www.coursera.org/learn/data-science-ethics/home/week/4)) that can create self-fulfilling feedback loops to weaponizes AI further. This is motivating calls for [formalizing data ethics cultures](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) in organizations, where everyone is empowered to [pull the Andon cord](https://en.wikipedia.org/wiki/Andon_(manufacturing) to raise ethics concerns early. And exploring [collaborative approaches to defining this culture](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f) that build emotional connections and consistent beliefs across organizations and industries. --- @@ -52,10 +191,10 @@ What is ethics? What does data ethics mean, and how is it relevant to data scien --- # Assignment -[Assignment Title](assignment.md ':include') +[Assignment Title](assignment.md) --- # Resources -[Related Resources](resources.md ':include') \ No newline at end of file +[Related Resources](resources.md) \ No newline at end of file diff --git a/1-Introduction/02-ethics/megan-smith-algorithms.png b/1-Introduction/02-ethics/megan-smith-algorithms.png new file mode 100644 index 00000000..8cc73489 Binary files /dev/null and b/1-Introduction/02-ethics/megan-smith-algorithms.png differ diff --git a/1-Introduction/04-stats-and-probability/README.md b/1-Introduction/04-stats-and-probability/README.md index 504b6c0f..f2fa0c22 100644 --- a/1-Introduction/04-stats-and-probability/README.md +++ b/1-Introduction/04-stats-and-probability/README.md @@ -51,7 +51,7 @@ To help us understand the distribution of data, it is helpful to talk about **qu Graphically we can represent relationship between median and quartiles in a diagram called the **box plot**: -![Box Plot](images/boxplot_explanation.png) + Here we also computer **inter-quartile range** IQR=Q3-Q1, and so-called **outliers** - values, that lie outside the boundaries [Q1-1.5*IQR,Q3+1.5*IQR]. @@ -92,6 +92,82 @@ If we plot the histogram of the generated samples we will see the picture very s *Normal Distribution with mean=0 and std.dev=1* +## Confidence Intervals + +When we talk about weights of baseball players, we assume that there is certain **random variable W** that corresponds to ideal probability distribution of weights of all baseball players. Our sequence of weights corresponds to a subset of all baseball players that we call **population**. An interesting question is, can we know the parameters of distribution of W, i.e. mean and variance? + +The easiest answer would be to calculate mean and variance of our sample. However, it could happen that our random sample does not accurately represent complete population. Thus it makes sense to talk about **confidence interval**. + +Suppose we have a sample X1, ..., Xn from our distribution. Each time we draw a sample from our distribution, we would end up with different mean value μ. Thus μ can be considered to be a random variable. A **confidence interval** with confidence p is a pair of values (Lp,Rp), such that **P**(Lp≤μ≤Rp) = p, i.e. a probability of measured mean value falling within the interval equals to p. + +It does beyond our short intro to discuss how those confidence intervals are calculated. Some more details can be found [on Wikipedia](https://en.wikipedia.org/wiki/Confidence_interval). An example of calculating confidence interval for weights and heights is given in the [accompanying notebooks](notebook.ipynb). + +| p | Weight mean | +|-----|-----------| +| 0.85 | 201.73±0.94 | +| 0.90 | 201.73±1.08 | +| 0.95 | 201.73±1.28 | + +Notice that the higher is the confidence probability, the wider is the confidence interval. + +## Hypothesis Testing + +In our baseball players dataset, there are different player roles, that can be summarized below (look at the [accompanying notebook](notebook.ipynb) to see how this table can be calculated): + +| Role | Height | Weight | Count | +|------|--------|--------|-------| +| Catcher | 72.723684 | 204.328947 | 76 | +| Designated_Hitter | 74.222222 | 220.888889 | 18 | +| First_Baseman | 74.000000 | 213.109091 | 55 | +| Outfielder | 73.010309 | 199.113402 | 194 | +| Relief_Pitcher | 74.374603 | 203.517460 | 315 | +| Second_Baseman | 71.362069 | 184.344828 | 58 | +| Shortstop | 71.903846 | 182.923077 | 52 | +| Starting_Pitcher | 74.719457 | 205.163636 | 221 | +| Third_Baseman | 73.044444 | 200.955556 | 45 | + +We can notice that the mean heights of first basemen is higher that that of second basemen. Thus, we may be tempted to conclude that **first basemen are higher than second basemen**. + +> This statement is called **a hypothesis**, because we do not know whether the fact is actually true or not. + +However, it is not always obvious whether we can make this conclusion. From the discussion above we know that each mean has an associated confidence interval, and thus this difference can just be a statistical error. We need some more formal way to test our hypothesis. + +Let's compute confidence intervals separately for heights of first and second basemen: + +| Confidence | First Basemen | Second Basemen | +|------------|---------------|----------------| +| 0.85 | 73.62..74.38 | 71.04..71.69 | +| 0.90 | 73.56..74.44 | 70.99..71.73 | +| 0.95 | 73.47..74.53 | 70.92..71.81 | + +We can see that under no confidence the intervals overlap. That proves our hypothesis that first basemen are higher than second basemen. + +More formally, the problem we are solving is to see if **two probability distributions are the same**, or at least have the same parameters. Depending on the distribution, we need to use different tests for that. If we know that our distributions are normal, we can apply **[Student t-test](https://en.wikipedia.org/wiki/Student%27s_t-test)**. + +In Student t-test, we compute so-called **t-value**, which indicates the difference between means, taking into account the variance. It is demonstrated that t-value follows **student distribution**, which allows us to get the threshold value for a given confidence level **p** (this can be computed, or looked up in the numerical tables). We then compare t-value to this threshold to approve or reject the hypothesis. + +In Python, we can use **SciPy** package, which includes `ttest_ind` function (in addition to many other useful statistical functions!). It computes the t-value for us, and also does the reverse lookup of confidence p-value, so that we can just look at the confidence to draw the conclusion. + +For example, our comparison between heights of first and second basemen give us the following results: +```python +from scipy.stats import ttest_ind + +tval, pval = ttest_ind(df.loc[df['Role']=='First_Baseman',['Height']], df.loc[df['Role']=='Designated_Hitter',['Height']],equal_var=False) +print(f"T-value = {tval[0]:.2f}\nP-value: {pval[0]}") +``` +``` +T-value = 7.65 +P-value: 9.137321189738925e-12 +``` +In our case, p-value is very low, meaning that there is strong evidence supporting that first basemen are taller. + +> **Challenge**: Use the sample code in the notebook to test other hypothesis that: (1) First basemen and older that second basemen; (2) First basemen and taller than third basemen; (3) Shortstops are taller than second basemen + + +There are different types of hypothesis that we might want to test, for example: +* To prove that a given sample follows some distribution. In our case we have assumed that heights are normally distributed, but that needs formal statistical verification. +* To prove that a mean value of a sample corresponds to some predefined value +* To prove that ## Law of Large Numbers and Central Limit Theorem diff --git a/1-Introduction/04-stats-and-probability/notebook.ipynb b/1-Introduction/04-stats-and-probability/notebook.ipynb index c94d8b3a..8b83ab72 100644 --- a/1-Introduction/04-stats-and-probability/notebook.ipynb +++ b/1-Introduction/04-stats-and-probability/notebook.ipynb @@ -93,9 +93,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 168, "source": [ - "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t',header=None,names=['Name','Team','Rome','Height','Weight','Age'])\r\n", + "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t',header=None,names=['Name','Team','Role','Height','Weight','Age'])\r\n", "df" ], "outputs": [ @@ -103,7 +103,7 @@ "output_type": "execute_result", "data": { "text/plain": [ - " Name Team Rome Height Weight Age\n", + " Name Team Role Height Weight Age\n", "0 Adam_Donachie BAL Catcher 74 180.0 22.99\n", "1 Paul_Bako BAL Catcher 74 215.0 34.69\n", "2 Ramon_Hernandez BAL Catcher 72 210.0 30.78\n", @@ -139,7 +139,7 @@ " \n", " Name\n", " Team\n", - " Rome\n", + " Role\n", " Height\n", " Weight\n", " Age\n", @@ -252,7 +252,7 @@ ] }, "metadata": {}, - "execution_count": 26 + "execution_count": 168 } ], "metadata": {} @@ -298,10 +298,10 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 158, "source": [ "plt.figure(figsize=(10,2))\r\n", - "plt.boxplot(df['Height'],vert=False)\r\n", + "plt.boxplot(df['Height'],vert=False,showmeans=True)\r\n", "plt.grid(color='gray',linestyle='dotted')\r\n", "plt.show()" ], @@ -312,8 +312,8 @@ "text/plain": [ "
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}, "metadata": {} } @@ -499,6 +499,241 @@ ], "metadata": {} }, + { + "cell_type": "markdown", + "source": [ + "## Confidence Intervals\r\n", + "\r\n", + "Let's now calculate confidence intervals for the weights and heights of baseball players. We will use the code [from this stackoverflow discussion](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 181, + "source": [ + "import scipy.stats\r\n", + "\r\n", + "def mean_confidence_interval(data, confidence=0.95):\r\n", + " a = 1.0 * np.array(data)\r\n", + " n = len(a)\r\n", + " m, se = np.mean(a), scipy.stats.sem(a)\r\n", + " h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)\r\n", + " return m, h\r\n", + "\r\n", + "for p in [0.85, 0.9, 0.95]:\r\n", + " m, h = mean_confidence_interval(df['Weight'].fillna(method='pad'),p)\r\n", + " print(f\"p={p:.2f}, mean = {m:.2f}±{h:.2f}\")" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "p=0.85, mean = 201.73±0.94\n", + "p=0.90, mean = 201.73±1.08\n", + "p=0.95, mean = 201.73±1.28\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## Hypothesis Testing\r\n", + "\r\n", + "Let's explore different roles in our baseball players dataset:" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 175, + "source": [ + "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Height Weight Count\n", + "Role \n", + "Catcher 72.723684 204.328947 76\n", + "Designated_Hitter 74.222222 220.888889 18\n", + "First_Baseman 74.000000 213.109091 55\n", + "Outfielder 73.010309 199.113402 194\n", + "Relief_Pitcher 74.374603 203.517460 315\n", + "Second_Baseman 71.362069 184.344828 58\n", + "Shortstop 71.903846 182.923077 52\n", + "Starting_Pitcher 74.719457 205.163636 221\n", + "Third_Baseman 73.044444 200.955556 45" + ], + "text/html": [ + "
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HeightWeightCount
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Catcher72.723684204.32894776
Designated_Hitter74.222222220.88888918
First_Baseman74.000000213.10909155
Outfielder73.010309199.113402194
Relief_Pitcher74.374603203.517460315
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Shortstop71.903846182.92307752
Starting_Pitcher74.719457205.163636221
Third_Baseman73.044444200.95555645
\n", + "
" + ] + }, + "metadata": {}, + "execution_count": 175 + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Let's test the hypothesis that First Basemen are higher then Second Basemen. The simplest way to do it is to test the confidence intervals:" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 188, + "source": [ + "for p in [0.85,0.9,0.95]:\r\n", + " m1, h1 = mean_confidence_interval(df.loc[df['Role']=='First_Baseman',['Height']],p)\r\n", + " m2, h2 = mean_confidence_interval(df.loc[df['Role']=='Second_Baseman',['Height']],p)\r\n", + " print(f'Conf={p:.2f}, 1st basemen height: {m1-h1[0]:.2f}..{m1+h1[0]:.2f}, 2nd basemen height: {m2-h2[0]:.2f}..{m2+h2[0]:.2f}')" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n", + "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n", + "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "We can see that intervals do not overlap.\r\n", + "\r\n", + "More statistically correct way to prove the hypothesis is to use **Student t-test**:" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 200, + "source": [ + "from scipy.stats import ttest_ind\r\n", + "\r\n", + "tval, pval = ttest_ind(df.loc[df['Role']=='First_Baseman',['Height']], df.loc[df['Role']=='Second_Baseman',['Height']],equal_var=False)\r\n", + "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "T-value = 7.65\n", + "P-value: 9.137321189738925e-12\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Two values returned by the `ttest_ind` functions are:\r\n", + "* p-value can be considered as the probability of two distributions having the same mean. In our case, it is very low, meaning that there is strong evidence supporting that first basemen are taller\r\n", + "* t-value is the intermediate value of normalized mean difference that is used in t-test, and it is compared against threshold value for a given confidence value " + ], + "metadata": {} + }, { "cell_type": "markdown", "source": [ diff --git a/3-Data-Visualization/10-visualization-quantities/README.md b/3-Data-Visualization/10-visualization-quantities/README.md index c136ea93..72f8e813 100644 --- a/3-Data-Visualization/10-visualization-quantities/README.md +++ b/3-Data-Visualization/10-visualization-quantities/README.md @@ -189,17 +189,16 @@ In this plot you can see the range, per category, of the Minimum Length and Maxi ![superimposed values](images/superimposed.png) - ## 🚀 Challenge - +This bird dataset offers a wealth of information about different types of birds within a particular ecosystem. Search around the internet and see if you can find other bird-oriented datasets. Practice building charts and graphs around these birds to discover facts you didn't realize. ## Post-Lecture Quiz [Post-lecture quiz]() ## Review & Self Study - +This first lesson has given you some information about how to use Matplotlib to visualize quantities. Do some research around other ways to work with datasets for visualization. [Plotly](https://github.com/plotly/plotly.py) is one that we won't cover in these lessons, so take a look at what it can offer. ## Assignment -[Assignment Title](assignment.md) +[Lines, Scatters, and Bars](assignment.md) diff --git a/3-Data-Visualization/10-visualization-quantities/assignment.md b/3-Data-Visualization/10-visualization-quantities/assignment.md index b7af6412..6caa81fd 100644 --- a/3-Data-Visualization/10-visualization-quantities/assignment.md +++ b/3-Data-Visualization/10-visualization-quantities/assignment.md @@ -1,8 +1,11 @@ -# Title +# Lines, Scatters and Bars ## Instructions +In this lesson, you worked with line charts, scatterplots, and bar charts to show interesting facts about this dataset. In this assignment, dig deeper into the dataset to discover a fact about a given type of bird. For example, create a notebook visualizing all the interesting data you can uncover about Snow Geese. Use the three plots mentioned above to tell a story in your notebook. + ## Rubric Exemplary | Adequate | Needs Improvement --- | --- | -- | +A notebook is presented with good annotations, solid storytelling, and attractive graphs | The notebook is missing one of these elements | The notebook is missing two of these elements \ No newline at end of file diff --git a/3-Data-Visualization/11-visualization-distributions/README.md b/3-Data-Visualization/11-visualization-distributions/README.md index 3231e497..2dacdb95 100644 --- a/3-Data-Visualization/11-visualization-distributions/README.md +++ b/3-Data-Visualization/11-visualization-distributions/README.md @@ -176,6 +176,7 @@ Perhaps it's worth researching whether the cluster of 'Vulnerable' birds accordi ## 🚀 Challenge +Histograms are a more sophisticated type of chart than basic scatterplots, bar charts, or line charts. Go on a search on the internet to find good examples of the use of histograms. How are they used, what do they demonstrate, and in what fields or areas of inquiry do they tend to be used? ## Post-Lecture Quiz @@ -183,7 +184,8 @@ Perhaps it's worth researching whether the cluster of 'Vulnerable' birds accordi ## Review & Self Study +In this lesson, you used Matplotlib and started working with Seaborn to show more sophisticated charts. Do some research on `kdeplot` in Seaborn, a "continuous probability density curve in one or more dimensions". Read through [the documentation](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) to understand how it works. ## Assignment -[Assignment Title](assignment.md) +[Apply your skills](assignment.md) diff --git a/3-Data-Visualization/11-visualization-distributions/assignment.md b/3-Data-Visualization/11-visualization-distributions/assignment.md index b7af6412..e3e9301b 100644 --- a/3-Data-Visualization/11-visualization-distributions/assignment.md +++ b/3-Data-Visualization/11-visualization-distributions/assignment.md @@ -1,8 +1,10 @@ -# Title +# Apply your skills ## Instructions +So far, you have worked with the Minnesota birds dataset to discover information about bird quantities and population density. Practice your application of these techniques by trying a different dataset, perhaps sourced from [Kaggle]. Build a notebook to tell a story about this dataset, and make sure to use histograms when discussing it. ## Rubric Exemplary | Adequate | Needs Improvement --- | --- | -- | +A notebook is presented with annotations about this dataset, including it source, and uses at least 5 histograms to discover facts about the data. | A notebook is presented with incomplete annotations or bugs | A notebook is presented without annotations and includes bugs \ No newline at end of file diff --git a/3-Data-Visualization/12-visualization-proportions/README.md b/3-Data-Visualization/12-visualization-proportions/README.md index 9489b44d..69b20be2 100644 --- a/3-Data-Visualization/12-visualization-proportions/README.md +++ b/3-Data-Visualization/12-visualization-proportions/README.md @@ -6,6 +6,8 @@ In this lesson, you will use a different nature-focused dataset to visualize pro - Donut charts 🍩 - Waffle charts 🧇 +> 💡 A very interesting project called [Charticulator](https://charticulator.com) by Microsoft Research offers a free drag and drop interface for data visualizations. In one of their tutorials they also use this mushroom dataset! So you can explore the data and learn the library at the same time: https://charticulator.com/tutorials/tutorial4.html + ## Pre-Lecture Quiz [Pre-lecture quiz]() @@ -157,17 +159,26 @@ Using a waffle chart, you can plainly see the proportions of cap color of this m In this lesson you learned three ways to visualize proportions. First, you need to group your data into categories and then decide which is the best way to display the data - pie, donut, or waffle. All are delicious and gratify the user with an instant snapshot of a dataset. - ## 🚀 Challenge - +Try recreating these tasty charts in [Charticulator](https://charticulator.com). ## Post-Lecture Quiz [Post-lecture quiz]() ## Review & Self Study +Sometimes it's not obvious when to use a pie, donut, or waffle chart. Here are some articles to read on this topic: + +https://www.beautiful.ai/blog/battle-of-the-charts-pie-chart-vs-donut-chart + +https://medium.com/@hypsypops/pie-chart-vs-donut-chart-showdown-in-the-ring-5d24fd86a9ce + +https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c6.htm + +https://medium.datadriveninvestor.com/data-visualization-done-the-right-way-with-tableau-waffle-chart-fdf2a19be402 +Do some research to find more information on this sticky decision. ## Assignment -[Assignment Title](assignment.md) +[Try it in Excel](assignment.md) diff --git a/3-Data-Visualization/12-visualization-proportions/assignment.md b/3-Data-Visualization/12-visualization-proportions/assignment.md index b7af6412..e3ab407c 100644 --- a/3-Data-Visualization/12-visualization-proportions/assignment.md +++ b/3-Data-Visualization/12-visualization-proportions/assignment.md @@ -1,8 +1,11 @@ -# Title +# Try it in Excel ## Instructions +Did you know you can create donut, pie and waffle charts in Excel? Using a dataset of your choice, create these three charts right in an Excel spreadsheet + ## Rubric -Exemplary | Adequate | Needs Improvement ---- | --- | -- | +| Exemplary | Adequate | Needs Improvement | +| ------------------------------------------------------- | ------------------------------------------------- | ------------------------------------------------------ | +| An Excel spreadsheet is presented with all three charts | An Excel spreadsheet is presented with two charts | An Excel spreadsheet is presented with only one charts | \ No newline at end of file diff --git a/3-Data-Visualization/13-visualization-relationships/README.md b/3-Data-Visualization/13-visualization-relationships/README.md index f9fadc95..b2494971 100644 --- a/3-Data-Visualization/13-visualization-relationships/README.md +++ b/3-Data-Visualization/13-visualization-relationships/README.md @@ -10,15 +10,165 @@ It will be interesting to visualize the relationship between a given state's pro [Pre-lecture quiz]() +In this lesson, you can use Seaborn, which you use before, as a good library to visualize relationships between variables. Particularly interesting is the use of Seaborn's `relplot` function that allows scatter plots and line plots to quickly visualize '[statistical relationships](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)', which allow the data scientist to better understand how variables relate to each other. + +## Scatterplots + +Use a scatterplot to show how the price of honey has evolved, year over year, per state. Seaborn, using `relplot`, conveniently groups the state data and displays data points for both categorical and numeric data. + +Let's start by importing the data and Seaborn: + +```python +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +honey = pd.read_csv('../../data/honey.csv') +honey.head() +``` +You notice that the honey data has several interesting columns, including year and price per pound. Let's explore this data, grouped by U.S. state: + +| state | numcol | yieldpercol | totalprod | stocks | priceperlb | prodvalue | year | +| ----- | ------ | ----------- | --------- | -------- | ---------- | --------- | ---- | +| AL | 16000 | 71 | 1136000 | 159000 | 0.72 | 818000 | 1998 | +| AZ | 55000 | 60 | 3300000 | 1485000 | 0.64 | 2112000 | 1998 | +| AR | 53000 | 65 | 3445000 | 1688000 | 0.59 | 2033000 | 1998 | +| CA | 450000 | 83 | 37350000 | 12326000 | 0.62 | 23157000 | 1998 | +| CO | 27000 | 72 | 1944000 | 1594000 | 0.7 | 1361000 | 1998 | + + +Create a basic scatterplot to show the relationship between the price per pound of honey and its U.S. state of origin. Make the `y` axis tall enough to display all the states: + +```python +sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); +``` +![scatterplot 1](images/scatter1.png) + +Now, show the same data with a honey color scheme to show how the price evolves over the years. You can do this by adding a 'hue' parameter to show the change, year over year: + +> ✅ Learn more about the [color palettes you can use in Seaborn](https://seaborn.pydata.org/tutorial/color_palettes.html) - try a beautiful rainbow color scheme! + +```python +sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); +``` +![scatterplot 2](images/scatter2.png) + +With this color scheme change, you can see that there's obviously a strong progression over the years in terms of honey price per pound. Indeed, if you look at a sample set in the data to verify (pick a given state, Arizona for example) you can see a pattern of price increases year over year, with few exceptions: + +| state | numcol | yieldpercol | totalprod | stocks | priceperlb | prodvalue | year | +| ----- | ------ | ----------- | --------- | ------- | ---------- | --------- | ---- | +| AZ | 55000 | 60 | 3300000 | 1485000 | 0.64 | 2112000 | 1998 | +| AZ | 52000 | 62 | 3224000 | 1548000 | 0.62 | 1999000 | 1999 | +| AZ | 40000 | 59 | 2360000 | 1322000 | 0.73 | 1723000 | 2000 | +| AZ | 43000 | 59 | 2537000 | 1142000 | 0.72 | 1827000 | 2001 | +| AZ | 38000 | 63 | 2394000 | 1197000 | 1.08 | 2586000 | 2002 | +| AZ | 35000 | 72 | 2520000 | 983000 | 1.34 | 3377000 | 2003 | +| AZ | 32000 | 55 | 1760000 | 774000 | 1.11 | 1954000 | 2004 | +| AZ | 36000 | 50 | 1800000 | 720000 | 1.04 | 1872000 | 2005 | +| AZ | 30000 | 65 | 1950000 | 839000 | 0.91 | 1775000 | 2006 | +| AZ | 30000 | 64 | 1920000 | 902000 | 1.26 | 2419000 | 2007 | +| AZ | 25000 | 64 | 1600000 | 336000 | 1.26 | 2016000 | 2008 | +| AZ | 20000 | 52 | 1040000 | 562000 | 1.45 | 1508000 | 2009 | +| AZ | 24000 | 77 | 1848000 | 665000 | 1.52 | 2809000 | 2010 | +| AZ | 23000 | 53 | 1219000 | 427000 | 1.55 | 1889000 | 2011 | +| AZ | 22000 | 46 | 1012000 | 253000 | 1.79 | 1811000 | 2012 | + + +Another way to visualize this progression is to use size, rather than color. For colorblind users, this might be a better option. Edit your visualization to show an increase of price by an increase in dot circumference: + +```python +sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspect=.5); +``` +You can see the size of the dots gradually increasing. + +![scatterplot 3](images/scatter3.png) + +Is this a simple case of supply and demand? Due to factors such as climate change and colony collapse, is there less honey available for purchase year over year, and thus the price increases? + +To discover a correlation between some of the variables in this dataset, let's explore some line charts. + +## Line charts + +Question: Is there a clear rise in price of honey per pound year over year? You can most easily discover that by creating a single line chart: + +```python +sns.relplot(x="year", y="priceperlb", kind="line", data=honey); +``` +Answer: Yes, with some exceptions around the year 2003: + +![line chart 1](images/line1.png) + +✅ Because Seaborn is aggregating data around one line, it displays "the multiple measurements at each x value by plotting the mean and the 95% confidence interval around the mean". [source](https://seaborn.pydata.org/tutorial/relational.html). This time-consuming behavior can be disabled by adding `ci=None`. + +Question: Well, in 2003 can we also see a spike in the honey supply? What if you look at total production year over year? + +```python +sns.relplot(x="year", y="totalprod", kind="line", data=honey); +``` + +![line chart 2](images/line2.png) + +Answer: Not really. If you look at total production, it actually seems to have increased in that particular year, even though generally speaking the amount of honey being produced is in decline during these years. + +Question: In that case, what could have caused that spike in the price of honey around 2003? + +To discover this, you can explore a facet grid. + +## Facet grids + +Facet grids take one facet of your dataset (in our case, you can choose 'year' to avoid having too many facets produced). Seaborn can then make a plot for each of those facets of your chosen x and y coordinates for more easy visual comparison. Does 2003 stand out in this type of comparison? + +Create a facet grid by continuing to use `relplot` as recommended by [Seaborn's documentation](https://seaborn.pydata.org/generated/seaborn.FacetGrid.html?highlight=facetgrid#seaborn.FacetGrid). + +```python +sns.relplot( + data=honey, + x="yieldpercol", y="numcol", + col="year", + col_wrap=3, + kind="line" +``` +In this visualization, you can compare the yield per colony and number of colonies year over year, side by side with a wrap set at 3 for the columns: + +![facet grid](images/facet.png) + +For this dataset, nothing particularly stands out with regards to the number of colonies and their yield, year over year and state over state. Is there a different way to look at finding a correlation between these two variables? + +## Dual-line Plots + +Try a multiline plot by superimposing two lineplots on top of each other, using Seaborn's 'despine' to remove their top and right spines, and using `ax.twinx` [derived from Matplotlib](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html). Twinx allows a chart to share the x axis and display two y axes. So, display the yield per colony and number of colonies, superimposed: + +```python +fig, ax = plt.subplots(figsize=(12,6)) +lineplot = sns.lineplot(x=honey['year'], y=honey['numcol'], data=honey, + label = 'Number of bee colonies', legend=False) +sns.despine() +plt.ylabel('# colonies') +plt.title('Honey Production Year over Year'); + +ax2 = ax.twinx() +lineplot2 = sns.lineplot(x=honey['year'], y=honey['yieldpercol'], ax=ax2, color="r", + label ='Yield per colony', legend=False) +sns.despine(right=False) +plt.ylabel('colony yield') +ax.figure.legend(); +``` +![superimposed plots](images/dual-line.png) + +While nothing jumps out to the eye around the year 2003, it does allow us to end this lesson on a little happier note: while there are overall a declining number of colonies, their numbers might seem to be stabilizing and their yield per colony is actually increasing, even with fewer bees. + +Go, bees, go! + +🐝❤️ ## 🚀 Challenge +In this lesson, you learned a bit more about other uses of scatterplots and line grids, including facet grids. Challenge yourself to create a facet grid using a different dataset, maybe one you used prior to these lessons. Note how long they take to create and how you need to be careful about how many grids you need to draw using these techniques. ## Post-Lecture Quiz [Post-lecture quiz]() ## Review & Self Study - +Line plots can be simple or quite complex. Do a bit of reading in the [Seaborn documentation](https://seaborn.pydata.org/generated/seaborn.lineplot.html) on the various ways you can build them. Try to enhance the line charts you built in this lesson with other methods listed in the docs. ## Assignment -[Assignment Title](assignment.md) +[Dive into the beehive](assignment.md) diff --git a/3-Data-Visualization/13-visualization-relationships/assignment.md b/3-Data-Visualization/13-visualization-relationships/assignment.md index b7af6412..5dca670a 100644 --- a/3-Data-Visualization/13-visualization-relationships/assignment.md +++ b/3-Data-Visualization/13-visualization-relationships/assignment.md @@ -1,8 +1,11 @@ -# Title +# Dive into the beehive ## Instructions +In this lesson you started looking at a dataset around bees and their honey production over a period of time that saw losses in the bee colony population overall. Dig deeper into this dataset and build a notebook that can tell the story of the health of the bee population, state by state and year by year. Do you discover anything interesting about this dataset? + ## Rubric -Exemplary | Adequate | Needs Improvement ---- | --- | -- | +| Exemplary | Adequate | Needs Improvement | +| ------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------- | ---------------------------------------- | +| A notebook is presented with a story annotated with at least three different charts showing aspects of the dataset, state over state and year over year | The notebook lacks one of these elements | The notebook lacks two of these elements | \ No newline at end of file diff --git a/3-Data-Visualization/13-visualization-relationships/images/dual-line.png b/3-Data-Visualization/13-visualization-relationships/images/dual-line.png new file mode 100644 index 00000000..9f56938e Binary files /dev/null and b/3-Data-Visualization/13-visualization-relationships/images/dual-line.png differ diff --git a/3-Data-Visualization/13-visualization-relationships/images/facet.png b/3-Data-Visualization/13-visualization-relationships/images/facet.png new file mode 100644 index 00000000..ac6b606b Binary files /dev/null and b/3-Data-Visualization/13-visualization-relationships/images/facet.png differ diff --git a/3-Data-Visualization/13-visualization-relationships/images/line1.png b/3-Data-Visualization/13-visualization-relationships/images/line1.png new file mode 100644 index 00000000..5945dba2 Binary files /dev/null and b/3-Data-Visualization/13-visualization-relationships/images/line1.png differ diff --git a/3-Data-Visualization/13-visualization-relationships/images/line2.png b/3-Data-Visualization/13-visualization-relationships/images/line2.png new file mode 100644 index 00000000..7380832c Binary files /dev/null and b/3-Data-Visualization/13-visualization-relationships/images/line2.png differ diff --git a/3-Data-Visualization/13-visualization-relationships/images/scatter1.png b/3-Data-Visualization/13-visualization-relationships/images/scatter1.png new file mode 100644 index 00000000..b5581842 Binary files /dev/null and b/3-Data-Visualization/13-visualization-relationships/images/scatter1.png differ diff --git a/3-Data-Visualization/13-visualization-relationships/images/scatter2.png b/3-Data-Visualization/13-visualization-relationships/images/scatter2.png new file mode 100644 index 00000000..8a45eac6 Binary files /dev/null and b/3-Data-Visualization/13-visualization-relationships/images/scatter2.png differ diff --git a/3-Data-Visualization/13-visualization-relationships/images/scatter3.png b/3-Data-Visualization/13-visualization-relationships/images/scatter3.png new file mode 100644 index 00000000..1b186ceb Binary files /dev/null and b/3-Data-Visualization/13-visualization-relationships/images/scatter3.png differ diff --git a/3-Data-Visualization/13-visualization-relationships/solution/notebook.ipynb b/3-Data-Visualization/13-visualization-relationships/solution/notebook.ipynb index ca53da06..5e61879d 100644 --- a/3-Data-Visualization/13-visualization-relationships/solution/notebook.ipynb +++ b/3-Data-Visualization/13-visualization-relationships/solution/notebook.ipynb @@ -6,12 +6,371 @@ "# Visualizing Honey Production 🍯 🐝" ], "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 177, + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "honey = pd.read_csv('../../../data/honey.csv')\n", + "honey.head()" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " state numcol yieldpercol totalprod stocks priceperlb \\\n", + "0 AL 16000.0 71 1136000.0 159000.0 0.72 \n", + "1 AZ 55000.0 60 3300000.0 1485000.0 0.64 \n", + "2 AR 53000.0 65 3445000.0 1688000.0 0.59 \n", + "3 CA 450000.0 83 37350000.0 12326000.0 0.62 \n", + "4 CO 27000.0 72 1944000.0 1594000.0 0.70 \n", + "\n", + " prodvalue year \n", + "0 818000.0 1998 \n", + "1 2112000.0 1998 \n", + "2 2033000.0 1998 \n", + "3 23157000.0 1998 \n", + "4 1361000.0 1998 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "execution_count": 177 + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Use a scatterplot to show the relationship between a state and its price per pound for local honey" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 178, + "source": [ + "sns.relplot(x=\"priceperlb\", y=\"state\", data=honey, height=15, aspect=.5);\n" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" + }, + "metadata": {} + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Show this relationship plus a new yellow-colored scatterplot with hue determined by year." + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 179, + "source": [ + "sns.relplot(x=\"priceperlb\", y=\"state\", hue=\"year\", palette=\"YlOrBr\", data=honey, height=15, aspect=.5);\n" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" + }, + "metadata": {} + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Try this plot one more time, this time showing the price change by size of the dot in the scatterplot" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 180, + "source": [ + "sns.relplot(x=\"priceperlb\", y=\"state\", size=\"year\", data=honey, height=15, aspect=.5);\n" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" 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" 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" + }, + "metadata": {} + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Create a dual line plot (this solution is suggested by Kedar Ghule: https://kedar.hashnode.dev/how-to-combine-two-line-charts-in-seaborn-and-python)" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 200, + "source": [ + "fig, ax = plt.subplots(figsize=(12,6))\n", + "lineplot = sns.lineplot(x=honey['year'], y=honey['numcol'], data=honey, \n", + " label = 'Number of bee colonies', legend=False)\n", + "sns.despine()\n", + "plt.ylabel('# colonies')\n", + "plt.title('Honey Production Year over Year');\n", + "\n", + "ax2 = ax.twinx()\n", + "lineplot2 = sns.lineplot(x=honey['year'], y=honey['yieldpercol'], ax=ax2, color=\"r\", \n", + " label ='Yield per colony', legend=False) \n", + "sns.despine(right=False)\n", + "plt.ylabel('colony yield')\n", + "ax.figure.legend();" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" 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anything" -- [Ronald Coase](https://en.wikiquote.org/wiki/Ronald_Coase) + +One of the basic skills of a data scientist is the ability to create a meaningful data visualization that helps answer questions you might have. Prior to visualizing your data, you need to ensure that it has been cleaned and prepared, as you did in prior lessons. After that, you can start deciding how best to present the data. + +In this lesson, you will review: + +1. How to choose the right chart type +2. How to avoid deceptive charting +3. How to work with color +4. How to style your charts for readability +5. How to build animated or 3D charting solutions +6. How to build a creative visualization [Pre-lecture quiz]() +## Pre-Lecture Quiz -## 🚀 Challenge +## Choose the right chart type + +In previous lessons, you experimented with building all kinds of interesting data visualizations using Matplotlib and Seaborn for charting. In general, you can select the [right kind of chart](https://chartio.com/learn/charts/how-to-select-a-data-vizualization/) for the question you are asking using this table: + + +| You need to: | You should use: | +| -------------------------- | ------------------------------- | +| Show data trends over time | Line | +| Compare categories | Bar, Pie | +| Compare totals | Pie, Stacked Bar | +| Show relationships | Scatter, Line, Facet, Dual Line | +| Show distributions | Scatter, Histogram, Box | +| Show proportions | Pie, Donut, Waffle | + +> ✅ Depending on the makeup of your data, you might need to convert it from text to numeric to get a given chart to support it. +## Avoid deception + +Even if a data scientist is careful to choose the right chart for the right data, there are plenty of ways that data can be displayed in a way to prove a point, often at the cost of undermining the data itself. There are many examples of deceptive charts and infographics! + +[![Deceptive Charts by Alberto Cairo](./images/tornado.png)](https://www.youtube.com/Low28hx4wyk "Deceptive charts") + +> 🎥 Click the image above for a conference talk about deceptive charts + +This chart reverses the X axis to show the opposite of the truth, based on date: + +![bad chart 1](images/bad-chart-1.png) + +[This chart](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) is even more deceptive, as the eye is drawn to the right to conclude that, over time, COVID cases have declined in the various counties. In fact, if you look closely at the dates, you find that they have been rearranged to give that deceptive downward trend. + +![bad chart 2](images/bad-chart-2.jpg) + +This notorious example uses color AND a flipped Y axis to deceive: instead of concluding that gun deaths spiked after the passage of gun-friendly legislation, in fact the eye is fooled to think that the opposite is true: + +![bad chart 3](images/bad-chart-3.jpg) + +This strange chart shows how proportion can be manipulated, to hilarious effect: + +![bad chart 4](images/bad-chart-4.jpg) + +Comparing the incomparable is yet another shady trick. There is a [wonderful web site](https://tylervigen.com/spurious-correlations) all about 'spurious correlations' displaying 'facts' correlating things like the divorce rate in Maine and the consumption of margarine. A Reddit group also collects the [ugly uses](https://www.reddit.com/r/dataisugly/top/?t=all) of data. + +It's important to understand how easily the eye can be fooled by deceptive charts. Even if the data scientist's intention is good, the choice of a bad type of chart, such as a pie chart showing too many categories, can be deceptive. + +## Color + +You saw in the 'Florida gun violence' chart above how color can provide an additional layer of meaning to charts, especially ones not designed using libraries such as Matplotlib and Seaborn which come with various vetted color libraries and palettes. If you are making a chart by hand, do a little study of [color theory](https://colormatters.com/color-and-design/basic-color-theory) +> ✅ Be aware, when designing charts, that accessibility is an important aspect of visualization. Some of your users might be color blind - does your chart display well for users with visual impairments? +Be careful when choosing colors for your chart, as color can convey meaning you might not intend. The 'pink ladies' in the 'height' chart above convey a distinctly 'feminine' ascribed meaning that adds to the bizarreness of the chart itself. + +While [color meaning](https://colormatters.com/color-symbolism/the-meanings-of-colors) might be different in different parts of the world, and tend to change in meaning according to their shade. Generally speaking, color meanings include: + +| Color | Meaning | +| ------ | ------------------- | +| red | power | +| blue | trust, loyalty | +| yellow | happiness, caution | +| green | ecology, luck, envy | +| purple | happiness | +| orange | vibrance | + +If you are tasked with building a chart with custom colors, ensure that your charts are both accessible and the color you choose coincides with the meaning you are trying to convey. +## Styling your charts for readability + +Charts are not meaningful if they are not readable! Take a moment to consider styling the width and height of your chart to scale well with your data. If one variable (such as all 50 states) need to be displayed, show them vertically on the Y axis if possible so as to avoid a horizontally-scrolling chart. + +Label your axes, provide a legend if necessary, and offer tooltips for better comprehension of data. + +If your data is textual and verbose on the X-axis, you can angle the text for better readability. [Matplotlib](https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html) offers 3d plotting, if you data supports it. Sophisticated data visualizations can be produced using `mpl_toolkits.mplot3d`. + +![3d plots](images/3d.png) +## Animation and 3D chart display + +Some of the best data visualizations today are animated. Shirley Wu has amazing ones done with D3, such as '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', where each flower is a visualization of a movie. Another example for the Guardian is 'bussed out', an interactive experience combining visualizations with Greensock and D3 plus a scrollytelling article format to show how NYC handles its homeless problem by busing people out of the city. + +![busing](images/busing.png) + +While this lesson is insufficient to go into depth to teach these powerful visualization libraries, try your hand at D3 in a Vue.js app using a library to display a visualization of the book "Dangerous Liaisons" as an animated social network. + +> "Les Liaisons Dangereuses" is an epistolary novel, or a novel presented as a series of letters. Written in 1782 by Choderlos de Laclos, it tells the story of the vicious, morally-bankrupt social maneuvers of two dueling protagonists of the French aristocracy in the late 18th century, the Vicomte de Valmont and the Marquise de Merteuil. Both meet their demise in the end but not without inflicting a great deal of social damage. The novel unfolds as a series of letters written to various people in their circles, plotting for revenge or simply to make trouble. Create a visualization of these letters to discover the major kingpins of the narrative, visually. + +You will complete a web app that will display an animated view of this social network. It uses a library that was built to create a [visual of a network](https://github.com/emiliorizzo/vue-d3-network) using Vue.js and D3. When the app is running, you can pull the nodes around on the screen to shuffle the data around. + +![liaisons](images/liaisons.png) +## Project: Build a chart to show a network using D3.js + +> This lesson folder includes a `solution` folder where you can find the completed project, for your reference. + +1. Follow the instructions in the README.md file in the starter folder's root. Make sure you have NPM and Node.js running on your machine before installing your project's dependencies. + +2. Open the `starter/src` folder. You'll discover an `assets` folder where you can find a .json file with all the letters from the novel, numbered, with a 'to' and 'from' annotation. + +3. Complete the code in `components/Nodes.vue` to enable the visualization. Look for the method called `createLinks()` and add the following nested loop. + +Loop through the .json object to capture the 'to' and 'from' data for the letters and build up the `links` object so that the visualization library can consume it: + +```javascript +//loop through letters + let f = 0; + let t = 0; + for (var i = 0; i < letters.length; i++) { + for (var j = 0; j < characters.length; j++) { + + if (characters[j] == letters[i].from) { + f = j; + } + if (characters[j] == letters[i].to) { + t = j; + } + } + this.links.push({ sid: f, tid: t }); + } +``` +Run your app from the terminal (npm run serve) and enjoy the visualization! +## 🚀 Challenge + +Take a tour of the internet to discover deceptive visualizations. How does the author fool the user, and is it intentional? Try correcting the visualizations to show how they should look. ## Post-Lecture Quiz [Post-lecture quiz]() ## Review & Self Study +Here are some articles to read about deceptive data visualization: + +https://gizmodo.com/how-to-lie-with-data-visualization-1563576606 + +http://ixd.prattsi.org/2017/12/visual-lies-usability-in-deceptive-data-visualizations/ + +Take a look at these interest visualizations for historical assets and artifacts: + +https://handbook.pubpub.org/ + +Look through this article on how animation can enhance your visualizations +https://medium.com/@EvanSinar/use-animation-to-supercharge-data-visualization-cd905a882ad4 ## Assignment -[Assignment Title](assignment.md) +[Build your own custom vis](assignment.md) diff --git a/3-Data-Visualization/14-meaningful-visualizations/assignment.md b/3-Data-Visualization/14-meaningful-visualizations/assignment.md index b7af6412..e53b52af 100644 --- a/3-Data-Visualization/14-meaningful-visualizations/assignment.md +++ b/3-Data-Visualization/14-meaningful-visualizations/assignment.md @@ -1,8 +1,10 @@ -# Title +# Build your own custom vis ## Instructions +Using the code sample in this project to create a social network, mock up data of your own social interactions. You could map your usage of social media or make a diagram of your family members. Create an interesting web app that shows a unique visualization of a social network. ## Rubric Exemplary | Adequate | Needs Improvement --- | --- | -- | +A GitHub repo is presented with code that runs properly (try deploying it as a static web app) and has an annotated README explaining the project | The repo does not run properly or is not documented well | The repo does not run properly and is not documented well \ No newline at end of file diff --git a/3-Data-Visualization/14-meaningful-visualizations/images/3d.png b/3-Data-Visualization/14-meaningful-visualizations/images/3d.png new file mode 100644 index 00000000..9fe7a82a Binary files /dev/null and b/3-Data-Visualization/14-meaningful-visualizations/images/3d.png differ diff --git a/3-Data-Visualization/14-meaningful-visualizations/images/bad-chart-1.png b/3-Data-Visualization/14-meaningful-visualizations/images/bad-chart-1.png new file mode 100644 index 00000000..ed4ce397 Binary files /dev/null and b/3-Data-Visualization/14-meaningful-visualizations/images/bad-chart-1.png differ diff --git a/3-Data-Visualization/14-meaningful-visualizations/images/bad-chart-2.jpg b/3-Data-Visualization/14-meaningful-visualizations/images/bad-chart-2.jpg new file mode 100644 index 00000000..0ceb17ad Binary files /dev/null and b/3-Data-Visualization/14-meaningful-visualizations/images/bad-chart-2.jpg differ diff --git a/3-Data-Visualization/14-meaningful-visualizations/images/bad-chart-3.jpg b/3-Data-Visualization/14-meaningful-visualizations/images/bad-chart-3.jpg new file mode 100644 index 00000000..59e3dc7e Binary files /dev/null and b/3-Data-Visualization/14-meaningful-visualizations/images/bad-chart-3.jpg differ diff --git a/3-Data-Visualization/14-meaningful-visualizations/images/bad-chart-4.jpg b/3-Data-Visualization/14-meaningful-visualizations/images/bad-chart-4.jpg new file mode 100644 index 00000000..d52b1be5 Binary files /dev/null and b/3-Data-Visualization/14-meaningful-visualizations/images/bad-chart-4.jpg differ diff --git a/3-Data-Visualization/14-meaningful-visualizations/images/busing.png b/3-Data-Visualization/14-meaningful-visualizations/images/busing.png new file mode 100644 index 00000000..d54db0b1 Binary files /dev/null and b/3-Data-Visualization/14-meaningful-visualizations/images/busing.png differ diff --git a/3-Data-Visualization/14-meaningful-visualizations/images/liaisons.png b/3-Data-Visualization/14-meaningful-visualizations/images/liaisons.png new file mode 100644 index 00000000..f5a36e2c Binary files /dev/null and b/3-Data-Visualization/14-meaningful-visualizations/images/liaisons.png differ diff --git a/3-Data-Visualization/14-meaningful-visualizations/images/tornado.png b/3-Data-Visualization/14-meaningful-visualizations/images/tornado.png new file mode 100644 index 00000000..2ee9eafa Binary files /dev/null and b/3-Data-Visualization/14-meaningful-visualizations/images/tornado.png differ diff --git a/3-Data-Visualization/14-meaningful-visualizations/notebook.ipynb b/3-Data-Visualization/14-meaningful-visualizations/notebook.ipynb deleted file mode 100644 index e69de29b..00000000 diff --git a/3-Data-Visualization/14-meaningful-visualizations/solution/.gitignore b/3-Data-Visualization/14-meaningful-visualizations/solution/.gitignore new file mode 100644 index 00000000..403adbc1 --- /dev/null +++ b/3-Data-Visualization/14-meaningful-visualizations/solution/.gitignore @@ -0,0 +1,23 @@ +.DS_Store +node_modules +/dist + + +# local env files +.env.local +.env.*.local + +# Log files +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* + +# Editor directories and files +.idea +.vscode +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/3-Data-Visualization/14-meaningful-visualizations/solution/README.md b/3-Data-Visualization/14-meaningful-visualizations/solution/README.md new file mode 100644 index 00000000..0e610071 --- /dev/null +++ b/3-Data-Visualization/14-meaningful-visualizations/solution/README.md @@ -0,0 +1,26 @@ +# Dangerous Liaisons data visualization project + +To get started, you need to ensure that you have NPM and Node running on your machine. Install the dependencies (npm install) and then run the project locally (npm run serve): + +## Project setup +``` +npm install +``` + +### Compiles and hot-reloads for development +``` +npm run serve +``` + +### Compiles and minifies for production +``` +npm run build +``` + +### Lints and fixes files +``` +npm run lint +``` + +### Customize configuration +See [Configuration Reference](https://cli.vuejs.org/config/). diff --git a/3-Data-Visualization/14-meaningful-visualizations/solution/babel.config.js b/3-Data-Visualization/14-meaningful-visualizations/solution/babel.config.js new file mode 100644 index 00000000..e9558405 --- /dev/null +++ b/3-Data-Visualization/14-meaningful-visualizations/solution/babel.config.js @@ -0,0 +1,5 @@ +module.exports = { + presets: [ + '@vue/cli-plugin-babel/preset' + ] +} diff --git a/3-Data-Visualization/14-meaningful-visualizations/solution/notebook.ipynb b/3-Data-Visualization/14-meaningful-visualizations/solution/notebook.ipynb deleted file mode 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}, + { + "letter_num": 83, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "9/23/17**", + "location": "Chateau de…" + }, + { + "letter_num": 84, + "from": "Vicomte de Valmont", + "to": "Cécile Volanges", + "date": "9/24/17**", + "location": "…" + }, + { + "letter_num": 85, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "9/25/17**", + "location": "Chateau de…", + "notes": "au soir" + }, + { + "letter_num": 86, + "from": "Maréchale de ***", + "to": "Marquise de Merteuil", + "date": "9/25/17**", + "location": "Paris", + "notes": "letter included in previous" + }, + { + "letter_num": 87, + "from": "Marquise de Merteuil", + "to": "Madame de Volanges", + "date": "9/26/17**", + "location": "Paris" + }, + { + "letter_num": 88, + "from": "Cécile Volanges", + "to": "Vicomte de Valmont", + "date": "9/26/17**", + "location": "…" + }, + { + "letter_num": 89, + "from": "Vicomte de Valmont", + "to": "Chevalier Danceny", + "date": "9/26/17**", + "location": "Chateau de…" + }, + { + "letter_num": 90, + "from": "Présidente de Tourvel", + "to": "Vicomte de Valmont", + "date": "9/27/17**", + "location": "…" + }, + { + "letter_num": 91, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "9/27/17**", + "location": "…" + }, + { + "letter_num": 92, + "from": "Chevalier Danceny", + "to": "Vicomte de Valmont", + "date": "9/27/17**", + "location": "Paris" + }, + { + "letter_num": 93, + "from": "Chevalier Danceny", + "to": "Cécile Volanges", + "date": "9/27/17**", + "location": "Paris", + "notes": "attached to preceding letter" + }, + { + "letter_num": 94, + "from": "Cécile Volanges", + "to": "Chevalier Danceny", + "date": "9/28/17**", + "location": "…" + }, + { + "letter_num": 95, + "from": "Cécile Volanges", + "to": "Vicomte de Valmont", + "date": "9/28/17**", + "location": "…" + }, + { + "letter_num": 96, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "10/1/17**", + "location": "Chateau de…" + }, + { + "letter_num": 97, + "from": "Cécile Volanges", + "to": "Marquise de Merteuil", + "date": "10/1/17**", + "location": "Chateau de…", + "notes": "seduction of cecile" + }, + { + "letter_num": 98, + "from": "Madame de Volanges", + "to": "Marquise de Merteuil", + "date": "10/2/17**", + "location": "Chateau de…" + }, + { + "letter_num": 99, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "10/2/17**", + "location": "Chateau de…", + "notes": "au soir" + }, + { + "letter_num": 100, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "10/3/17**", + "location": "Chateau de…" + }, + { + "letter_num": 101, + "from": "Vicomte de Valmont", + "to": "Azolan, chasseur", + "date": "10/3/17**", + "location": "Chateau de…", + "notes": "attached to preceding letter" + }, + { + "letter_num": 102, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "10/3/17**", + "location": "…", + "notes": "1h in the morning - departure of Tourvel" + }, + { + "letter_num": 103, + "from": "Madame de Rosemonde", + "to": "Présidente de Tourvel", + "date": "10/3/17**", + "location": "Chateau de…" + }, + { + "letter_num": 104, + "from": "Marquise de Merteuil", + "to": "Madame de Volanges", + "date": "10/4/17**", + "location": "Paris" + }, + { + "letter_num": 105, + "from": "Marquise de Merteuil", + "to": "Cécile Volanges", + "date": "10/4/17**", + "location": "Paris" + }, + { + "letter_num": 106, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "10/4/17**", + "location": "Paris" + }, + { + "letter_num": 107, + "from": "Azolan, chasseur", + "to": "Vicomte de Valmont", + "date": "10/5/17**", + "location": "Paris", + "notes": "11h pm" + }, + { + "letter_num": 108, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "10/5/17**", + "location": "Paris" + }, + { + "letter_num": 109, + "from": "Cécile Volanges", + "to": "Marquise de Merteuil", + "date": "10/10/17**", + "location": "Chateau de…" + }, + { + "letter_num": 110, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "10/11/17**", + "location": "Chateau de…" + }, + { + "letter_num": 111, + "from": "Le Comte de Gercourt", + "to": "Madame de Volanges", + "date": "10/10/17**", + "location": "Bastia" + }, + { + "letter_num": 112, + "from": "Madame de Rosemonde", + "to": "Présidente de Tourvel", + "date": "10/14/17**", + "location": "Chateau de…", + "notes": "dictated, signed Adelaide" + }, + { + "letter_num": 113, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "10/15/17**", + "location": "Paris" + }, + { + "letter_num": 114, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "10/16/17**", + "location": "Paris" + }, + { + "letter_num": 115, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "10/19/17**", + "location": "Chateau de…" + }, + { + "letter_num": 116, + "from": "Chevalier Danceny", + "to": "Cécile Volanges", + "date": "10/17/17**", + "location": "Paris" + }, + { + "letter_num": 117, + "from": "Cécile Volanges", + "to": "Chevalier Danceny", + "date": "10/18/17**", + "location": "Chateau de…", + "notes": "dictated by Valmont" + }, + { + "letter_num": 118, + "from": "Chevalier Danceny", + "to": "Marquise de Merteuil", + "date": "10/19/17**", + "location": "Paris" + }, + { + "letter_num": 119, + "from": "Madame de Rosemonde", + "to": "Présidente de Tourvel", + "date": "10/20/17**", + "location": "Chateau de…" + }, + { + "letter_num": 120, + "from": "Vicomte de Valmont", + "to": "Père Anselme", + "date": "10/22/17**", + "location": "Chateau de…" + }, + { + "letter_num": 121, + "from": "Marquise de Merteuil", + "to": "Chevalier Danceny", + "date": "10/22/17**", + "location": "Chateau de…" + }, + { + "letter_num": 122, + "from": "Madame de Rosemonde", + "to": "Présidente de Tourvel", + "date": "10/25/17**", + "location": "Chateau de…" + }, + { + "letter_num": 123, + "from": "Père Anselme", + "to": "Vicomte de Valmont", + "date": "10/25/17**", + "location": "Paris" + }, + { + "letter_num": 124, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "10/25/17**", + "location": "Paris" + }, + { + "letter_num": 125, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "10/29/17**", + "location": "Paris", + "notes": "success of Valmont w/ Tourvel" + }, + { + "letter_num": 126, + "from": "Madame de Rosemonde", + "to": "Présidente de Tourvel", + "date": "10/30/17**", + "location": "Chateau de…" + }, + { + "letter_num": 127, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "10/31/17**", + "location": "Chateau de…" + }, + { + "letter_num": 128, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "11/1/17**", + "location": "Paris" + }, + { + "letter_num": 129, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "11/3/17**", + "location": "Paris" + }, + { + "letter_num": 130, + "from": "Madame de Rosemonde", + "to": "Présidente de Tourvel", + "date": "11/4/17**", + "location": "Chateau de…" + }, + { + "letter_num": 131, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "11/6/17**", + "location": "Chateau de…" + }, + { + "letter_num": 132, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "11/7/17**", + "location": "Paris" + }, + { + "letter_num": 133, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "11/8/17**", + "location": "Paris" + }, + { + "letter_num": 134, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "11/11/17*", + "location": "Chateau de…" + }, + { + "letter_num": 135, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "11/15/17**", + "location": "Paris", + "notes": "valmont w/ prostitute" + }, + { + "letter_num": 136, + "from": "Présidente de Tourvel", + "to": "Vicomte de Valmont", + "date": "11/15/17**", + "location": "Paris" + }, + { + "letter_num": 137, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "11/15/17**", + "location": "Paris" + }, + { + "letter_num": 138, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "11/15/17**", + "location": "Paris" + }, + { + "letter_num": 139, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "11/16/17**", + "location": "Paris", + "notes": "au soir" + }, + { + "letter_num": 140, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "11/21/17**", + "location": "Paris" + }, + { + "letter_num": 141, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "11/24/17**", + "location": "Chateau de…" + }, + { + "letter_num": 142, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "11/27/17**", + "location": "Paris" + }, + { + "letter_num": 143, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "11/27/17**", + "location": "Paris" + }, + { + "letter_num": 144, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "11/28/17**", + "location": "Paris" + }, + { + "letter_num": 145, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "11/29/17**", + "location": "Chateau de…" + }, + { + "letter_num": 146, + "from": "Marquise de Merteuil", + "to": "Chevalier Danceny", + "date": "11/29/17**", + "location": "Chateau de…" + }, + { + "letter_num": 147, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "11/29/17**", + "location": "Paris" + }, + { + "letter_num": 148, + "from": "Chevalier Danceny", + "to": "Marquise de Merteuil", + "date": "12/1/17**", + "location": "Paris" + }, + { + "letter_num": 149, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/2/17**", + "location": "Paris" + }, + { + "letter_num": 150, + "from": "Chevalier Danceny", + "to": "Marquise de Merteuil", + "date": "12/3/17**", + "location": "Paris" + }, + { + "letter_num": 151, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "12/3/17**", + "location": "Paris" + }, + { + "letter_num": 152, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "12/4/17**", + "location": "Paris" + }, + { + "letter_num": 153, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "12/4/17**", + "location": "Paris", + "notes": "at the bottom, mme de M write 'eh bien, la guerre'" + }, + { + "letter_num": 154, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/5/17**", + "location": "Paris" + }, + { + "letter_num": 155, + "from": "Vicomte de Valmont", + "to": "Chevalier Danceny", + "date": "12/5/17**", + "location": "Paris" + }, + { + "letter_num": 156, + "from": "Cécile Volanges", + "to": "Chevalier Danceny", + "date": "12/4/17**", + "location": "Paris" + }, + { + "letter_num": 157, + "from": "Chevalier Danceny", + "to": "Vicomte de Valmont", + "date": "12/5/17**", + "location": "Paris" + }, + { + "letter_num": 158, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "12/6/17**", + "location": "Paris" + }, + { + "letter_num": 159, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "12/6/17**", + "location": "Paris" + }, + { + "letter_num": 160, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/6/17**", + "location": "Paris" + }, + { + "letter_num": 161, + "from": "Présidente de Tourvel", + "to": "...", + "date": "12/5/17**", + "location": "Paris", + "notes": "dictated to her maid" + }, + { + "letter_num": 162, + "from": "Chevalier Danceny", + "to": "Vicomte de Valmont", + "date": "12/6/17**", + "location": "Paris", + "notes": "duel challenge" + }, + { + "letter_num": 163, + "from": "M. Bertrand", + "to": "Madame de Rosemonde", + "date": "12/7/17**", + "location": "Paris", + "notes": "death of Valmont" + }, + { + "letter_num": 164, + "from": "Madame de Rosemonde", + "to": "M. Bertrand", + "date": "12/8/17**", + "location": "Chateau de…" + }, + { + "letter_num": 165, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/9/17**", + "location": "Paris", + "notes": "death of Presidente" + }, + { + "letter_num": 166, + "from": "M. Bertrand", + "to": "Madame de Rosemonde", + "date": "12/10/17**", + "location": "Paris" + }, + { + "letter_num": 167, + "from": "Anonyme", + "to": "Chevalier Danceny", + "date": "12/10/17**", + "location": "Paris" + }, + { + "letter_num": 168, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/11/17**", + "location": "Paris" + }, + { + "letter_num": 169, + "from": "Chevalier Danceny", + "to": "Madame de Rosemonde", + "date": "12/12/17**", + "location": "Paris", + "notes": "revelation of letters" + }, + { + "letter_num": 170, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/13/17**", + "location": "Paris", + "notes": "plan to send cecile back to convent" + }, + { + "letter_num": 171, + "from": "Madame de Rosemonde", + "to": "Chevalier Danceny", + "date": "12/13/17**", + "location": "Chateau de…", + "notes": "drop charges" + }, + { + "letter_num": 172, + "from": "Madame de Rosemonde", + "to": "Madame de Volanges", + "date": "12/15/17**", + "location": "Chateau de…" + }, + { + "letter_num": 173, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/18/17**", + "location": "Paris", + "notes": "booing, fever of Merteuil" + }, + { + "letter_num": 174, + "from": "Chevalier Danceny", + "to": "Madame de Rosemonde", + "date": "12/26/17**", + "location": "Paris" + }, + { + "letter_num": 175, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "1/14/17**", + "location": "Paris", + "notes": "downfall of Merteuil" + } +] diff --git a/3-Data-Visualization/14-meaningful-visualizations/solution/src/components/Nodes.vue b/3-Data-Visualization/14-meaningful-visualizations/solution/src/components/Nodes.vue new file mode 100644 index 00000000..d3319328 --- /dev/null +++ b/3-Data-Visualization/14-meaningful-visualizations/solution/src/components/Nodes.vue @@ -0,0 +1,193 @@ + + + + + diff --git a/3-Data-Visualization/14-meaningful-visualizations/solution/src/main.js b/3-Data-Visualization/14-meaningful-visualizations/solution/src/main.js new file mode 100644 index 00000000..a3fd06b3 --- /dev/null +++ b/3-Data-Visualization/14-meaningful-visualizations/solution/src/main.js @@ -0,0 +1,8 @@ +import Vue from "vue"; +import App from "./App.vue"; + +Vue.config.productionTip = false; + +new Vue({ + render: h => h(App) +}).$mount("#app"); diff --git a/3-Data-Visualization/14-meaningful-visualizations/starter/.gitignore b/3-Data-Visualization/14-meaningful-visualizations/starter/.gitignore new file mode 100644 index 00000000..403adbc1 --- /dev/null +++ b/3-Data-Visualization/14-meaningful-visualizations/starter/.gitignore @@ -0,0 +1,23 @@ +.DS_Store +node_modules +/dist + + +# local env files +.env.local +.env.*.local + +# Log files +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* + +# Editor directories and files +.idea +.vscode +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/3-Data-Visualization/14-meaningful-visualizations/starter/README.md b/3-Data-Visualization/14-meaningful-visualizations/starter/README.md new file mode 100644 index 00000000..0e610071 --- /dev/null +++ b/3-Data-Visualization/14-meaningful-visualizations/starter/README.md @@ -0,0 +1,26 @@ +# Dangerous Liaisons data visualization project + +To get started, you need to ensure that you have NPM and Node running on your machine. Install the dependencies (npm install) and then run the project locally (npm run serve): + +## Project setup +``` +npm install +``` + +### Compiles and hot-reloads for development +``` +npm run serve +``` + +### Compiles and minifies for production +``` +npm run build +``` + +### Lints and fixes files +``` +npm run lint +``` + +### Customize configuration +See [Configuration Reference](https://cli.vuejs.org/config/). diff --git a/3-Data-Visualization/14-meaningful-visualizations/starter/babel.config.js b/3-Data-Visualization/14-meaningful-visualizations/starter/babel.config.js new file mode 100644 index 00000000..e9558405 --- /dev/null +++ b/3-Data-Visualization/14-meaningful-visualizations/starter/babel.config.js @@ -0,0 +1,5 @@ +module.exports = { + presets: [ + '@vue/cli-plugin-babel/preset' + ] +} diff --git a/3-Data-Visualization/14-meaningful-visualizations/starter/package-lock.json b/3-Data-Visualization/14-meaningful-visualizations/starter/package-lock.json new file mode 100644 index 00000000..f777f9d0 --- /dev/null +++ b/3-Data-Visualization/14-meaningful-visualizations/starter/package-lock.json @@ -0,0 +1,11969 @@ +{ + "name": "liaisons", + "version": "0.1.0", + "lockfileVersion": 1, + "requires": true, + "dependencies": { + "@babel/code-frame": { + "version": "7.12.11", + "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.12.11.tgz", + "integrity": "sha512-Zt1yodBx1UcyiePMSkWnU4hPqhwq7hGi2nFL1LeA3EUl+q2LQx16MISgJ0+z7dnmgvP9QtIleuETGOiOH1RcIw==", + "dev": true, + "requires": { + "@babel/highlight": "^7.10.4" + } + }, + "@babel/compat-data": { + "version": "7.12.7", + "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.12.7.tgz", + "integrity": "sha512-YaxPMGs/XIWtYqrdEOZOCPsVWfEoriXopnsz3/i7apYPXQ3698UFhS6dVT1KN5qOsWmVgw/FOrmQgpRaZayGsw==", + "dev": true + }, + "@babel/core": { + "version": "7.12.10", + "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.12.10.tgz", + "integrity": 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"Vicomte de Valmont", + "date": "8/24/17**", + "location": "…" + }, + { + "letter_num": 34, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "8/25/17**", + "location": "…" + }, + { + "letter_num": 35, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "8/21/17**", + "location": "…" + }, + { + "letter_num": 36, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "8/23/17**", + "location": "Stamped from Dijon" + }, + { + "letter_num": 37, + "from": "Présidente de Tourvel", + "to": "Madame de Volanges", + "date": "8/25/17**", + "location": "…" + }, + { + "letter_num": 38, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "8/27/17**", + "location": "…" + }, + { + "letter_num": 39, + "from": "Cécile Volanges", + "to": "Sophie Carnay", + "date": "8/27/17**", + "location": "…" + }, + { + "letter_num": 40, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "none" + }, + { + "letter_num": 41, + "from": "Présidente de Tourvel", + "to": "Vicomte de Valmont", + "date": "8/26/17**", + "location": "…" + }, + { + "letter_num": 42, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "8/26/17**", + "location": "…" + }, + { + "letter_num": "40-suite", + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "8/27/17**", + "location": "…" + }, + { + "letter_num": 43, + "from": "Présidente de Tourvel", + "to": "Vicomte de Valmont", + "date": "8/27/17**", + "location": "…" + }, + { + "letter_num": 44, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "8/28/17**", + "location": "…" + }, + { + "letter_num": 45, + "from": "Présidente de Tourvel", + "to": "Madame de Volanges", + "date": "8/29/17**", + "location": "…" + }, + { + "letter_num": 46, + "from": "Chevalier Danceny", + "to": "Cécile Volanges", + "date": "8/29/17**", + "location": "…" + }, + { + "letter_num": 47, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "8/30/17**", + "location": "P…" + }, + { + "letter_num": 48, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "8/30/17**", + "location": "Stamped from Paris" + }, + { + "letter_num": 49, + "from": "Cécile Volanges", + "to": "Chevalier Danceny", + "date": "8/31/17**", + "location": "…" + }, + { + "letter_num": 50, + "from": "Présidente de Tourvel", + "to": "Vicomte de Valmont", + "date": "9/1/17**", + "location": "…" + }, + { + "letter_num": 51, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "9/2/17**", + "location": "…" + }, + { + "letter_num": 52, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "9/3/17**", + "location": "…" + }, + { + "letter_num": 53, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "9/3/17**", + "location": "…" + }, + { + "letter_num": 54, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "9/4/17**", + "location": "…" + }, + { + "letter_num": 55, + "from": "Cécile Volanges", + "to": "Sophie Carnay", + "date": "9/4/17**", + "location": "…" + }, + { + "letter_num": 56, + "from": "Présidente de Tourvel", + "to": "Vicomte de Valmont", + "date": "9/5/17**", + "location": "…" + }, + { + "letter_num": 57, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "9/5/17**", + "location": "…" + }, + { + "letter_num": 58, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "9/7/17**", + "location": "…" + }, + { + "letter_num": 59, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "9/8/17**", + "location": "…" + }, + { + "letter_num": 60, + "from": "Chevalier Danceny", + "to": "Vicomte de Valmont", + "date": "9/8/17**", + "location": "…" + }, + { + "letter_num": 61, + "from": "Cécile Volanges", + "to": "Sophie Carnay", + "date": "9/7/17**", + "location": "…" + }, + { + "letter_num": 62, + "from": "Madame de Volanges", + "to": "Chevalier Danceny", + "date": "9/7/17**", + "location": "…" + }, + { + "letter_num": 63, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "9/9/17**", + "location": "…" + }, + { + "letter_num": 64, + "from": "Chevalier Danceny", + "to": "Madame de Volanges", + "date": "9/9/17**", + "location": "…" + }, + { + "letter_num": 65, + "from": "Chevalier Danceny", + "to": "Cécile Volanges", + "date": "9/9/17**", + "location": "…", + "notes": "letter sent, opened, to Merteuil via Valmont in next letter" + }, + { + "letter_num": 66, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "9/9/17**", + "location": "…" + }, + { + "letter_num": 67, + "from": "Présidente de Tourvel", + "to": "Vicomte de Valmont", + "date": "9/9/17**", + "location": "…" + }, + { + "letter_num": 68, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "9/10/17**", + "location": "…" + }, + { + "letter_num": 69, + "from": "Cécile Volanges", + "to": "Chevalier Danceny", + "date": "9/10/17**", + "location": "…", + "notes": "letter oopied by Danceny" + }, + { + "letter_num": 70, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "9/11/17**", + "location": "…" + }, + { + "letter_num": 71, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "9/13/17**", + "location": "Chateau de…" + }, + { + "letter_num": 72, + "from": "Chevalier Danceny", + "to": "Cécile Volanges", + "date": "9/11/17**", + "location": "Paris", + "notes": "sent on the 14th" + }, + { + "letter_num": 73, + "from": "Vicomte de Valmont", + "to": "Cécile Volanges", + "date": "9/14/17**", + "location": "Chateau de…", + "notes": "sent on the 14th in same letter as above" + }, + { + "letter_num": 74, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "9/15/17**", + "location": "Paris" + }, + { + "letter_num": 75, + "from": "Cécile Volanges", + "to": "Sophie Carnay", + "date": "9/14/17**", + "location": "Chateau de…" + }, + { + "letter_num": 76, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "9/17/17**", + "location": "Chateau de…" + }, + { + "letter_num": 77, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "9/15/17**", + "location": "…" + }, + { + "letter_num": 78, + "from": "Présidente de Tourvel", + "to": "Vicomte de Valmont", + "date": "9/16/17**", + "location": "…" + }, + { + "letter_num": 79, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "9/18/17**", + "location": "…" + }, + { + "letter_num": 80, + "from": "Chevalier Danceny", + "to": "Cécile Volanges", + "date": "9/18/17**", + "location": "Paris" + }, + { + "letter_num": 81, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "9/20/17**", + "location": "…", + "notes": "Vaincre ou perir" + }, + { + "letter_num": 82, + "from": "Cécile Volanges", + "to": "Chevalier Danceny", + "date": "9/21/17**", + "location": "Chateau de…" + }, + { + "letter_num": 83, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "9/23/17**", + "location": "Chateau de…" + }, + { + "letter_num": 84, + "from": "Vicomte de Valmont", + "to": "Cécile Volanges", + "date": "9/24/17**", + "location": "…" + }, + { + "letter_num": 85, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "9/25/17**", + "location": "Chateau de…", + "notes": "au soir" + }, + { + "letter_num": 86, + "from": "Maréchale de ***", + "to": "Marquise de Merteuil", + "date": "9/25/17**", + "location": "Paris", + "notes": "letter included in previous" + }, + { + "letter_num": 87, + "from": "Marquise de Merteuil", + "to": "Madame de Volanges", + "date": "9/26/17**", + "location": "Paris" + }, + { + "letter_num": 88, + "from": "Cécile Volanges", + "to": "Vicomte de Valmont", + "date": "9/26/17**", + "location": "…" + }, + { + "letter_num": 89, + "from": "Vicomte de Valmont", + "to": "Chevalier Danceny", + "date": "9/26/17**", + "location": "Chateau de…" + }, + { + "letter_num": 90, + "from": "Présidente de Tourvel", + "to": "Vicomte de Valmont", + "date": "9/27/17**", + "location": "…" + }, + { + "letter_num": 91, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "9/27/17**", + "location": "…" + }, + { + "letter_num": 92, + "from": "Chevalier Danceny", + "to": "Vicomte de Valmont", + "date": "9/27/17**", + "location": "Paris" + }, + { + "letter_num": 93, + "from": "Chevalier Danceny", + "to": "Cécile Volanges", + "date": "9/27/17**", + "location": "Paris", + "notes": "attached to preceding letter" + }, + { + "letter_num": 94, + "from": "Cécile Volanges", + "to": "Chevalier Danceny", + "date": "9/28/17**", + "location": "…" + }, + { + "letter_num": 95, + "from": "Cécile Volanges", + "to": "Vicomte de Valmont", + "date": "9/28/17**", + "location": "…" + }, + { + "letter_num": 96, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "10/1/17**", + "location": "Chateau de…" + }, + { + "letter_num": 97, + "from": "Cécile Volanges", + "to": "Marquise de Merteuil", + "date": "10/1/17**", + "location": "Chateau de…", + "notes": "seduction of cecile" + }, + { + "letter_num": 98, + "from": "Madame de Volanges", + "to": "Marquise de Merteuil", + "date": "10/2/17**", + "location": "Chateau de…" + }, + { + "letter_num": 99, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "10/2/17**", + "location": "Chateau de…", + "notes": "au soir" + }, + { + "letter_num": 100, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "10/3/17**", + "location": "Chateau de…" + }, + { + "letter_num": 101, + "from": "Vicomte de Valmont", + "to": "Azolan, chasseur", + "date": "10/3/17**", + "location": "Chateau de…", + "notes": "attached to preceding letter" + }, + { + "letter_num": 102, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "10/3/17**", + "location": "…", + "notes": "1h in the morning - departure of Tourvel" + }, + { + "letter_num": 103, + "from": "Madame de Rosemonde", + "to": "Présidente de Tourvel", + "date": "10/3/17**", + "location": "Chateau de…" + }, + { + "letter_num": 104, + "from": "Marquise de Merteuil", + "to": "Madame de Volanges", + "date": "10/4/17**", + "location": "Paris" + }, + { + "letter_num": 105, + "from": "Marquise de Merteuil", + "to": "Cécile Volanges", + "date": "10/4/17**", + "location": "Paris" + }, + { + "letter_num": 106, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "10/4/17**", + "location": "Paris" + }, + { + "letter_num": 107, + "from": "Azolan, chasseur", + "to": "Vicomte de Valmont", + "date": "10/5/17**", + "location": "Paris", + "notes": "11h pm" + }, + { + "letter_num": 108, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "10/5/17**", + "location": "Paris" + }, + { + "letter_num": 109, + "from": "Cécile Volanges", + "to": "Marquise de Merteuil", + "date": "10/10/17**", + "location": "Chateau de…" + }, + { + "letter_num": 110, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "10/11/17**", + "location": "Chateau de…" + }, + { + "letter_num": 111, + "from": "Le Comte de Gercourt", + "to": "Madame de Volanges", + "date": "10/10/17**", + "location": "Bastia" + }, + { + "letter_num": 112, + "from": "Madame de Rosemonde", + "to": "Présidente de Tourvel", + "date": "10/14/17**", + "location": "Chateau de…", + "notes": "dictated, signed Adelaide" + }, + { + "letter_num": 113, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "10/15/17**", + "location": "Paris" + }, + { + "letter_num": 114, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "10/16/17**", + "location": "Paris" + }, + { + "letter_num": 115, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "10/19/17**", + "location": "Chateau de…" + }, + { + "letter_num": 116, + "from": "Chevalier Danceny", + "to": "Cécile Volanges", + "date": "10/17/17**", + "location": "Paris" + }, + { + "letter_num": 117, + "from": "Cécile Volanges", + "to": "Chevalier Danceny", + "date": "10/18/17**", + "location": "Chateau de…", + "notes": "dictated by Valmont" + }, + { + "letter_num": 118, + "from": "Chevalier Danceny", + "to": "Marquise de Merteuil", + "date": "10/19/17**", + "location": "Paris" + }, + { + "letter_num": 119, + "from": "Madame de Rosemonde", + "to": "Présidente de Tourvel", + "date": "10/20/17**", + "location": "Chateau de…" + }, + { + "letter_num": 120, + "from": "Vicomte de Valmont", + "to": "Père Anselme", + "date": "10/22/17**", + "location": "Chateau de…" + }, + { + "letter_num": 121, + "from": "Marquise de Merteuil", + "to": "Chevalier Danceny", + "date": "10/22/17**", + "location": "Chateau de…" + }, + { + "letter_num": 122, + "from": "Madame de Rosemonde", + "to": "Présidente de Tourvel", + "date": "10/25/17**", + "location": "Chateau de…" + }, + { + "letter_num": 123, + "from": "Père Anselme", + "to": "Vicomte de Valmont", + "date": "10/25/17**", + "location": "Paris" + }, + { + "letter_num": 124, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "10/25/17**", + "location": "Paris" + }, + { + "letter_num": 125, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "10/29/17**", + "location": "Paris", + "notes": "success of Valmont w/ Tourvel" + }, + { + "letter_num": 126, + "from": "Madame de Rosemonde", + "to": "Présidente de Tourvel", + "date": "10/30/17**", + "location": "Chateau de…" + }, + { + "letter_num": 127, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "10/31/17**", + "location": "Chateau de…" + }, + { + "letter_num": 128, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "11/1/17**", + "location": "Paris" + }, + { + "letter_num": 129, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "11/3/17**", + "location": "Paris" + }, + { + "letter_num": 130, + "from": "Madame de Rosemonde", + "to": "Présidente de Tourvel", + "date": "11/4/17**", + "location": "Chateau de…" + }, + { + "letter_num": 131, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "11/6/17**", + "location": "Chateau de…" + }, + { + "letter_num": 132, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "11/7/17**", + "location": "Paris" + }, + { + "letter_num": 133, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "11/8/17**", + "location": "Paris" + }, + { + "letter_num": 134, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "11/11/17*", + "location": "Chateau de…" + }, + { + "letter_num": 135, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "11/15/17**", + "location": "Paris", + "notes": "valmont w/ prostitute" + }, + { + "letter_num": 136, + "from": "Présidente de Tourvel", + "to": "Vicomte de Valmont", + "date": "11/15/17**", + "location": "Paris" + }, + { + "letter_num": 137, + "from": "Vicomte de Valmont", + "to": "Présidente de Tourvel", + "date": "11/15/17**", + "location": "Paris" + }, + { + "letter_num": 138, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "11/15/17**", + "location": "Paris" + }, + { + "letter_num": 139, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "11/16/17**", + "location": "Paris", + "notes": "au soir" + }, + { + "letter_num": 140, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "11/21/17**", + "location": "Paris" + }, + { + "letter_num": 141, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "11/24/17**", + "location": "Chateau de…" + }, + { + "letter_num": 142, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "11/27/17**", + "location": "Paris" + }, + { + "letter_num": 143, + "from": "Présidente de Tourvel", + "to": "Madame de Rosemonde", + "date": "11/27/17**", + "location": "Paris" + }, + { + "letter_num": 144, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "11/28/17**", + "location": "Paris" + }, + { + "letter_num": 145, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "11/29/17**", + "location": "Chateau de…" + }, + { + "letter_num": 146, + "from": "Marquise de Merteuil", + "to": "Chevalier Danceny", + "date": "11/29/17**", + "location": "Chateau de…" + }, + { + "letter_num": 147, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "11/29/17**", + "location": "Paris" + }, + { + "letter_num": 148, + "from": "Chevalier Danceny", + "to": "Marquise de Merteuil", + "date": "12/1/17**", + "location": "Paris" + }, + { + "letter_num": 149, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/2/17**", + "location": "Paris" + }, + { + "letter_num": 150, + "from": "Chevalier Danceny", + "to": "Marquise de Merteuil", + "date": "12/3/17**", + "location": "Paris" + }, + { + "letter_num": 151, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "12/3/17**", + "location": "Paris" + }, + { + "letter_num": 152, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "12/4/17**", + "location": "Paris" + }, + { + "letter_num": 153, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "12/4/17**", + "location": "Paris", + "notes": "at the bottom, mme de M write 'eh bien, la guerre'" + }, + { + "letter_num": 154, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/5/17**", + "location": "Paris" + }, + { + "letter_num": 155, + "from": "Vicomte de Valmont", + "to": "Chevalier Danceny", + "date": "12/5/17**", + "location": "Paris" + }, + { + "letter_num": 156, + "from": "Cécile Volanges", + "to": "Chevalier Danceny", + "date": "12/4/17**", + "location": "Paris" + }, + { + "letter_num": 157, + "from": "Chevalier Danceny", + "to": "Vicomte de Valmont", + "date": "12/5/17**", + "location": "Paris" + }, + { + "letter_num": 158, + "from": "Vicomte de Valmont", + "to": "Marquise de Merteuil", + "date": "12/6/17**", + "location": "Paris" + }, + { + "letter_num": 159, + "from": "Marquise de Merteuil", + "to": "Vicomte de Valmont", + "date": "12/6/17**", + "location": "Paris" + }, + { + "letter_num": 160, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/6/17**", + "location": "Paris" + }, + { + "letter_num": 161, + "from": "Présidente de Tourvel", + "to": "...", + "date": "12/5/17**", + "location": "Paris", + "notes": "dictated to her maid" + }, + { + "letter_num": 162, + "from": "Chevalier Danceny", + "to": "Vicomte de Valmont", + "date": "12/6/17**", + "location": "Paris", + "notes": "duel challenge" + }, + { + "letter_num": 163, + "from": "M. Bertrand", + "to": "Madame de Rosemonde", + "date": "12/7/17**", + "location": "Paris", + "notes": "death of Valmont" + }, + { + "letter_num": 164, + "from": "Madame de Rosemonde", + "to": "M. Bertrand", + "date": "12/8/17**", + "location": "Chateau de…" + }, + { + "letter_num": 165, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/9/17**", + "location": "Paris", + "notes": "death of Presidente" + }, + { + "letter_num": 166, + "from": "M. Bertrand", + "to": "Madame de Rosemonde", + "date": "12/10/17**", + "location": "Paris" + }, + { + "letter_num": 167, + "from": "Anonyme", + "to": "Chevalier Danceny", + "date": "12/10/17**", + "location": "Paris" + }, + { + "letter_num": 168, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/11/17**", + "location": "Paris" + }, + { + "letter_num": 169, + "from": "Chevalier Danceny", + "to": "Madame de Rosemonde", + "date": "12/12/17**", + "location": "Paris", + "notes": "revelation of letters" + }, + { + "letter_num": 170, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/13/17**", + "location": "Paris", + "notes": "plan to send cecile back to convent" + }, + { + "letter_num": 171, + "from": "Madame de Rosemonde", + "to": "Chevalier Danceny", + "date": "12/13/17**", + "location": "Chateau de…", + "notes": "drop charges" + }, + { + "letter_num": 172, + "from": "Madame de Rosemonde", + "to": "Madame de Volanges", + "date": "12/15/17**", + "location": "Chateau de…" + }, + { + "letter_num": 173, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "12/18/17**", + "location": "Paris", + "notes": "booing, fever of Merteuil" + }, + { + "letter_num": 174, + "from": "Chevalier Danceny", + "to": "Madame de Rosemonde", + "date": "12/26/17**", + "location": "Paris" + }, + { + "letter_num": 175, + "from": "Madame de Volanges", + "to": "Madame de Rosemonde", + "date": "1/14/17**", + "location": "Paris", + "notes": "downfall of Merteuil" + } +] diff --git a/3-Data-Visualization/14-meaningful-visualizations/starter/src/components/Nodes.vue b/3-Data-Visualization/14-meaningful-visualizations/starter/src/components/Nodes.vue new file mode 100644 index 00000000..07fad1bf --- /dev/null +++ b/3-Data-Visualization/14-meaningful-visualizations/starter/src/components/Nodes.vue @@ -0,0 +1,178 @@ + + + + + diff --git a/3-Data-Visualization/14-meaningful-visualizations/starter/src/main.js b/3-Data-Visualization/14-meaningful-visualizations/starter/src/main.js new file mode 100644 index 00000000..a3fd06b3 --- /dev/null +++ b/3-Data-Visualization/14-meaningful-visualizations/starter/src/main.js @@ -0,0 +1,8 @@ +import Vue from "vue"; +import App from "./App.vue"; + +Vue.config.productionTip = false; + +new Vue({ + render: h => h(App) +}).$mount("#app"); diff --git a/5-Data-Science-In-Cloud/21-tbd/README.md b/5-Data-Science-In-Cloud/21-tbd/README.md index 1d45cbd4..e5ad6317 100644 --- a/5-Data-Science-In-Cloud/21-tbd/README.md +++ b/5-Data-Science-In-Cloud/21-tbd/README.md @@ -8,18 +8,133 @@ In this lesson, you will learn the fundamental principles of the Cloud, then you ## What is the Cloud? +The Cloud, or Cloud Computing, is the delivery of a wide range of pay-as-you-go computing services hosted on an infrastructure over the internet. Services include solutions such as storage, databases, networking, software, analytics, and intelligent services. + +We usually differentiate the Public, Private and Hybrid clouds: + +* Public cloud: a public cloud is owned and operated by a third-party cloud service provider which delivers their computing resources over the Internet to the public + +* Private cloud: refers to cloud computing resources used exclusively by a single business or organization, with services and an infrastructure maintained on a private network. + +* Hybrid cloud: the hybrid cloud is a system that combines public and private clouds. Users opt for an on-premises datacenter, while allowing data and applications to be run on one or more public clouds. + +Most cloud computing services fall into three categories: infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). + +* Infrastructure as a service (IaaS): users rent an IT infrastructure — servers and virtual machines (VMs), storage, networks, operating systems + +* Platform as a service (PaaS): users rent an environment for developing, testing, delivering, and managing software applications. Users don’t need to worry about setting up or managing the underlying infrastructure of servers, storage, network, and databases needed for development. + +* Software as a service (SaaS): users get access to software applications over the Internet, on demand and typically on a subscription basis. Users don’t need to worry about hosting, managing the software application, the underlying infrastructure or the maintenance, like software upgrades and security patching. + + +Some of the largest Cloud providers are Amazon Web Services, Google Cloud Platform and Microsoft Azure. + ## Why Choose the Cloud for Data Science? +Developers and IT professionals chose to work with the Cloud for many reasons, including the following: + +* Innovation: you can power your applications by integrating innovative services created by Cloud providers directly into your apps + +* Flexibility: you only pay for the services that you need and can choose from a wide range of services. You typically pay as you go and adapt your services according to your evolving needs. + +* Budget: you don’t need to make initial investments to purchase hardware and software, set up and run on-site datacenters and you can just pay for what you use + +* Scalability: your resources can scale according to the needs of your project, which means that your apps can use more or less computing power, storage and bandwidth, by adapting to external factors at any given time + +* Productivity: you can focus on your business rather than spend time on tasks that can be managed by someone else, such as managing datacenters + +* Reliability: cloud computing offers several ways to continuously back up your data and you can set up disaster recovery plans to keep your business going + +* Security: you can benefit from policies, technologies, and controls that strengthen the security of your project + + These are some of the most reasons why people choose to use Cloud services. Now that we have a better understanding of what the Cloud is and what its main benefits are, let's look more specifically into the jobs of Data scientists and developers working with data, and how the Cloud can help them with several of the specific challenges they face: + +* Storing large amounts of data: instead of buying, managing and protecting big servers, you can store your data directly in the cloud, with solutions such as Azure Cosmos DB, Azure SQL Database and Azure Data Lake Storage + +* Performing data integration: data integration is an essential part of data science, that lets you go from data collection to taking actions. With data integration services offered in the cloud, you can collect, transform and integrate data from various sources into a single data warehouse, with Data Factory + +* Processing data: processing vast amounts of data requires a lot of computing power, and not everyone has access to machines powerful enough for that, which is why many people choose to directly harness the cloud’s huge computing power to run and deploy their solutions + +* Using data analytics services: to turn your data into actionable insights, with Azure Synapse Analytics, Azure Stream Analytics, Azure Databricks + +* Using Machine Learning and data intelligence services: Instead of starting from scratch, you can use machine learning algorithms offered by the cloud provider, with services such as AzureML, and you can use cognitive services such as speech-to-text, text to speech, computer vision and more + + ## Examples of Data Science in the Cloud + -## Post-Lecture Quiz +Let’s make this more tangible by looking at a couple of scenarios. -[Post-lecture quiz]() + + +We’ll start with a scenario commonly studied by people who start with machine learning: social media sentiment analysis in real time. + + + +Let's say you run a news media website, and you want to leverage live data to understand what content your readers could be interested in. To know more about that, you can build a program that performs real-time sentiment analysis of data from Twitter publications, on topics that are relevant to your readers. + + -## Review & Self Study +The key indicators you will look at is the volume of tweets on specific topics (hashtags), and sentiment, which is established using analytics tools that perform sentiment analysis around the specified topics. + -## Assignment +The steps necessary to create this projects are the following: -[Assignment Title](assignment.md) +* Create an event hub for streaming input, which will collect data from Twitter + +* Configure and start a Twitter client application, which will call the Twitter Streaming APIs + +* Create a Stream Analytics job + +* Specify the job input and query + +* Create an output sink and specify the job output + +* Start the job + +To view the full process, check out the [documentation](https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends). + + + +Let’s take a more original example of a project created by Dmitry Soshnikov, one of the authors of this curriculum. + +Dmitry created a Generative Adversarial Network and taught it to create artificial paintings. We will see the different steps used to for this: + + +* Creating artificial paintings + +* Training the network on paintings from a dataset + +* Training a GAN, or Generative Adversarial Network + +* Creating a discriminator model and a generator model + +* Training the script in the Cloud with Azure ML + +* Generating new images + + + +As you can see, we can leverage Cloud services in many ways to perform Data Science. + + + +To see the full process, visit [Dmitry’s blog](https://soshnikov.com/scienceart/creating-generative-art-using-gan-on-azureml). + + +Here is a paragraph with an footnote [[1]](#f1). + +## Footnote + +Sources: +* https://azure.microsoft.com/overview/what-is-cloud-computing + +* https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends + +* https://soshnikov.com/scienceart/creating-generative-art-using-gan-on-azureml/ + +## Post-Lecture Quiz + +[Post-lecture quiz]()