@ -104,7 +104,7 @@ The first step is to collect the data. While in many cases it can be a straight
</dd>
<dt>2) Data Storage</dt>
<dd>
Storing data can be challenging, especially if we are talking about big data. When deciding how to store data, it makes sense to anticipate the way you would to query the data in the future. There are several ways data can be stored:
Storing data can be challenging, especially if we are talking about big data. When deciding how to store data, it makes sense to anticipate the way you would like to query the data in the future. There are several ways data can be stored:
<ul>
<li>A relational database stores a collection of tables, and uses a special language called SQL to query them. Typically, tables are organized into different groups called schemas. In many cases we need to convert the data from original form to fit the schema.</li>
<li><ahref="https://en.wikipedia.org/wiki/NoSQL">A NoSQL</a> database, such as <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, does not enforce schemas on data, and allows storing more complex data, for example, hierarchical JSON documents or graphs. However, NoSQL databases do not have the rich querying capabilities of SQL, and cannot enforce referential integrity, i.e. rules on how the data is structured in tables and governing the relationships between tables.</li>
@ -32,7 +32,7 @@ The word "ethics" comes from the [Greek word "ethikos"](https://en.wikipedia.org
**Ethics** is about the shared values and moral principles that govern our behavior in society. Ethics is based not on laws but on
widely accepted norms of what is "right vs. wrong". However, ethical considerations can influence corporate governance initiatives and government regulations that create more incentives for compliance.
**Data Ethics** is 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_". Here, **"data"** focuses on actions related to generation, recording, curation, processing dissemination, sharing,and usage, **"algorithms"** focuses on AI, agents, machine learning,and robots, and **"practices"** focuses on topics like responsible innovation, programming, hacking and ethics codes.
**Data Ethics** is 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_". Here, **"data"** focuses on actions related to generation, recording, curation, processing, dissemination, sharing,and usage, **"algorithms"** focuses on AI, agents, machine learning,and robots, and **"practices"** focuses on topics like responsible innovation, programming, hacking, and ethics codes.
**Applied Ethics** is the [practical application of moral considerations](https://en.wikipedia.org/wiki/Applied_ethics). It's the process of actively investigating ethical issues in the context of _real-world actions, products and processes_, and taking corrective measures to make that these remain aligned with our defined ethical values.
**Raw data** is data that has come from its source in its initial state and has not been analyzed or organized. In order to make sense of what is happening with a dataset, it needs to be organized into a format that can be understood by humans as well as the technology they may use to analyze it further. The structure of a dataset describes how it's organized and can be classified at structured, unstructured and semi-structured. These types of structure will vary, depending on the source but will ultimately fit in these three categories.
### Raw Data
Raw data is data that has come from its source in its initial state and has not been analyzed or organized. In order to make sense of what is happening with a dataset, it needs to be organized into a format that can be understood by humans as well as the technology they may use to analyze it further. The structure of a dataset describes how it's organized and can be classified at structured, unstructured and semi-structured. These types of structure will vary, depending on the source but will ultimately fit in these three categories.
### Quantitative Data
Quantitative data is numerical observations within a dataset and can typically be analyzed, measured and used mathematically. Some examples of quantitative data are: a country's population, a person's height or a company's quarterly earnings. With some additional analysis, quantitative data could be used to discover seasonal trends of the Air Quality Index (AQI) or estimate the probability of rush hour traffic on a typical work day.
Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
@ -23,12 +25,26 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson cur
|:---:|
| Data Science For Beginners - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
## Announcement - New Curriculum on Generative AI was just released!
We just released a 12 lesson curriculum on generative AI. Come learn things like:
- prompting and prompt engineering
- text and image app generation
- search apps
As usual, there's a lesson, assignments to complete, knowledge checks and challenges.
Check it out:
> https://aka.ms/genai-beginners
# Are you a student?
Get started with the following resources:
- [Student Hub page](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) In this page, you will find beginner resources, Student packs and even ways to get a free cert voucher. This is one page you want to bookmark and check from time to time as we switch out content at least monthly.
- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Join a global community of student ambassadors, this could be your way into Microsoft
- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Join a global community of student ambassadors, this could be your way into Microsoft.
# Getting Started
@ -65,7 +81,7 @@ In addition, a low-stakes quiz before a class sets the intention of the student
- Assignment
- Post-lesson quiz
> **A note about quizzes**: All quizzes are contained [in this app](https://purple-hill-04aebfb03.1.azurestaticapps.net/), for 40 total quizzes of three questions each. They are linked from within the lessons, but the quiz app can be run locally; follow the instruction in the `quiz-app` folder. They are gradually being localized.
> **A note about quizzes**: All quizzes are contained in the Quiz-App folder, for 40 total quizzes of three questions each. They are linked from within the lessons, but the quiz app can be run locally or deployed to Azure; follow the instruction in the `quiz-app` folder. They are gradually being localized.
## Lessons
@ -128,13 +144,18 @@ You can run this documentation offline by using [Docsify](https://docsify.js.org
## Help Wanted!
If you would like to translate all or part of the curriculum, please follow our [Translations](TRANSLATIONS.md) guide
If you would like to translate all or part of the curriculum, please follow our [Translations](TRANSLATIONS.md) guide.
## Other Curricula
Our team produces other curricula! Check out:
- [Machine Learning for Beginners](https://aka.ms/ml-beginners)
- [IoT for Beginners](https://aka.ms/iot-beginners)
- [Web Dev for Beginners](https://aka.ms/webdev-beginners)
- [AI for Beginners](https://aka.ms/ai-beginners)
- [Data Science for Beginners](https://aka.ms/datascience-beginners)
- [Generative AI for Beginners](https://aka.ms/genai-beginners)
- [**NEW** Cybersecurity for Beginners](https://github.com/microsoft/Security-101??WT.mc_id=academic-96948-sayoung)
- [Web Dev for Beginners](https://aka.ms/webdev-beginners?WT.mc_id=academic-113596-abartolo)
- [IoT for Beginners](https://aka.ms/iot-beginners)
- [Machine Learning for Beginners](https://aka.ms/ml-beginners)
- [XR Development for Beginners](https://aka.ms/xr-dev-for-beginners)
- [Mastering GitHub Copilot for AI Paired Programming](https://aka.ms/GitHubCopilotAI)
See [Configuration Reference](https://cli.vuejs.org/config/).
Credits: Thanks to the original version of this quiz app: https://github.com/arpan45/simple-quiz-vue
## Deploying to Azure
Here’s a step-by-step guide to help you get started:
1. Fork the a GitHub Repository
Ensure your static web app code is in your GitHub repository. Fork this repository.
2. Create an Azure Static Web App
- Create and [Azure account](http://azure.microsoft.com)
- Go to the [Azure portal](https://portal.azure.com)
- Click on “Create a resource” and search for “Static Web App”.
- Click “Create”.
3. Configure the Static Web App
- Basics: Subscription: Select your Azure subscription.
- Resource Group: Create a new resource group or use an existing one.
- Name: Provide a name for your static web app.
- Region: Choose the region closest to your users.
- #### Deployment Details:
- Source: Select “GitHub”.
- GitHub Account: Authorize Azure to access your GitHub account.
- Organization: Select your GitHub organization.
- Repository: Choose the repository containing your static web app.
- Branch: Select the branch you want to deploy from.
- #### Build Details:
- Build Presets: Choose the framework your app is built with (e.g., React, Angular, Vue, etc.).
- App Location: Specify the folder containing your app code (e.g., / if it’s in the root).
- API Location: If you have an API, specify its location (optional).
- Output Location: Specify the folder where the build output is generated (e.g., build or dist).
4. Review and Create
Review your settings and click “Create”. Azure will set up the necessary resources and create a GitHub Actions workflow in your repository.
5. GitHub Actions Workflow
Azure will automatically create a GitHub Actions workflow file in your repository (.github/workflows/azure-static-web-apps-<name>.yml). This workflow will handle the build and deployment process.
6. Monitor the Deployment
Go to the “Actions” tab in your GitHub repository.
You should see a workflow running. This workflow will build and deploy your static web app to Azure.
Once the workflow completes, your app will be live on the provided Azure URL.
### Example Workflow File
Here’s an example of what the GitHub Actions workflow file might look like:
jj
10 months ago
committed by
GitHub