diff --git a/1-Introduction/03-defining-data/README.md b/1-Introduction/03-defining-data/README.md index fbdee72f..533d6b57 100644 --- a/1-Introduction/03-defining-data/README.md +++ b/1-Introduction/03-defining-data/README.md @@ -59,7 +59,7 @@ Kaggle is an excellent source of open datasets. Use the [dataset search tool](ht - Is the data quantitative or qualitative? - Is the data structured, unstructured, or semi-structured? -## [Post-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/5) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) diff --git a/1-Introduction/04-stats-and-probability/README.md b/1-Introduction/04-stats-and-probability/README.md index bc4fddb2..7d919692 100644 --- a/1-Introduction/04-stats-and-probability/README.md +++ b/1-Introduction/04-stats-and-probability/README.md @@ -244,7 +244,7 @@ Use the sample code in the notebook to test other hypothesis that: 2. First basemen are taller than third basemen 3. Shortstops are taller than second basemen -## [Post-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/7) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Review & Self Study diff --git a/2-Working-With-Data/05-relational-databases/README.md b/2-Working-With-Data/05-relational-databases/README.md index 0a11313c..4ed18528 100644 --- a/2-Working-With-Data/05-relational-databases/README.md +++ b/2-Working-With-Data/05-relational-databases/README.md @@ -166,7 +166,7 @@ There are numerous relational databases available on the internet. You can explo ## Post-Lecture Quiz -## [Post-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/9) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Review & Self Study diff --git a/2-Working-With-Data/06-non-relational/README.md b/2-Working-With-Data/06-non-relational/README.md index 691531d4..9e21e757 100644 --- a/2-Working-With-Data/06-non-relational/README.md +++ b/2-Working-With-Data/06-non-relational/README.md @@ -132,8 +132,7 @@ There is a `TwitterData.json` file that you can upload to the SampleDB database. Try to run a few select queries to find the documents that have Microsoft in the text field. Hint: try to use the [LIKE keyword](https://docs.microsoft.com/en-us/azure/cosmos-db/sql/sql-query-keywords#using-like-with-the--wildcard-character) - -## [Post-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/11) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) diff --git a/2-Working-With-Data/07-python/README.md b/2-Working-With-Data/07-python/README.md index 119b53cb..c496eb98 100644 --- a/2-Working-With-Data/07-python/README.md +++ b/2-Working-With-Data/07-python/README.md @@ -256,9 +256,7 @@ Here are some examples of exploring data from Image data sources: Whether you already have structured or unstructured data, using Python you can perform all steps related to data processing and understanding. It is probably the most flexible way of data processing, and that is the reason the majority of data scientists use Python as their primary tool. Learning Python in depth is probably a good idea if you are serious about your data science journey! - - -## [Post-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/13) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Review & Self Study diff --git a/2-Working-With-Data/08-data-preparation/README.md b/2-Working-With-Data/08-data-preparation/README.md index 3580cbd5..85c3f376 100644 --- a/2-Working-With-Data/08-data-preparation/README.md +++ b/2-Working-With-Data/08-data-preparation/README.md @@ -317,7 +317,7 @@ letters numbers All of the discussed materials are provided as a [Jupyter Notebook](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/2-Working-With-Data/08-data-preparation/notebook.ipynb). Additionally, there are exercises present after each section, give them a try! -## [Post-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/15) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) diff --git a/3-Data-Visualization/09-visualization-quantities/README.md b/3-Data-Visualization/09-visualization-quantities/README.md index 35fb09c3..c5077e9d 100644 --- a/3-Data-Visualization/09-visualization-quantities/README.md +++ b/3-Data-Visualization/09-visualization-quantities/README.md @@ -194,7 +194,8 @@ In this plot, you can see the range per bird category of the Minimum Length and ## πŸš€ 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](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/17) + +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Review & Self Study diff --git a/3-Data-Visualization/10-visualization-distributions/README.md b/3-Data-Visualization/10-visualization-distributions/README.md index de009b07..6fd6886f 100644 --- a/3-Data-Visualization/10-visualization-distributions/README.md +++ b/3-Data-Visualization/10-visualization-distributions/README.md @@ -192,7 +192,7 @@ Perhaps it's worth researching whether the cluster of 'Vulnerable' birds accordi 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](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/19) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Review & Self Study diff --git a/3-Data-Visualization/11-visualization-proportions/README.md b/3-Data-Visualization/11-visualization-proportions/README.md index 7c537ddd..b720a57a 100644 --- a/3-Data-Visualization/11-visualization-proportions/README.md +++ b/3-Data-Visualization/11-visualization-proportions/README.md @@ -12,7 +12,7 @@ In this lesson, you will use a different nature-focused dataset to visualize pro > πŸ’‘ 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: [Charticulator tutorial](https://charticulator.com/tutorials/tutorial4.html). -## [Pre-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/20) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Get to know your mushrooms πŸ„ diff --git a/3-Data-Visualization/12-visualization-relationships/README.md b/3-Data-Visualization/12-visualization-relationships/README.md index 87542e6f..38df1553 100644 --- a/3-Data-Visualization/12-visualization-relationships/README.md +++ b/3-Data-Visualization/12-visualization-relationships/README.md @@ -164,7 +164,8 @@ Go, bees, go! ## πŸš€ 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](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/23) + +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Review & Self Study diff --git a/3-Data-Visualization/13-meaningful-visualizations/README.md b/3-Data-Visualization/13-meaningful-visualizations/README.md index 212cdc8f..4c5a0712 100644 --- a/3-Data-Visualization/13-meaningful-visualizations/README.md +++ b/3-Data-Visualization/13-meaningful-visualizations/README.md @@ -145,7 +145,7 @@ Run your app from the terminal (npm run serve) and enjoy the visualization! 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](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/25) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Review & Self Study diff --git a/4-Data-Science-Lifecycle/14-Introduction/README.md b/4-Data-Science-Lifecycle/14-Introduction/README.md index d9e8a2da..eefd5184 100644 --- a/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -93,7 +93,7 @@ Explore the [Team Data Science Process lifecycle](https://docs.microsoft.com/en- |![Team Data Science Lifecycle](./images/tdsp-lifecycle2.png) | ![Data Science Process Alliance Image](./images/CRISP-DM.png) | | Image by [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Image by [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | -## [Post-Lecture Quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/27) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Review & Self Study diff --git a/4-Data-Science-Lifecycle/15-analyzing/README.md b/4-Data-Science-Lifecycle/15-analyzing/README.md index f388cf7f..bac3629a 100644 --- a/4-Data-Science-Lifecycle/15-analyzing/README.md +++ b/4-Data-Science-Lifecycle/15-analyzing/README.md @@ -39,8 +39,7 @@ You don’t have to wait until the data is thoroughly cleaned and analyzed to st ## Exploring to identify inconsistencies All the topics in this lesson can help identify missing or inconsistent values, but Pandas provides functions to check for some of these. [isna() or isnull()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.isna.html) can check for missing values. One important piece of exploring for these values within your data is to explore why they ended up that way in the first place. This can help you decide on what [actions to take to resolve them](/2-Working-With-Data/08-data-preparation/notebook.ipynb). - -## [Pre-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/27) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Assignment diff --git a/4-Data-Science-Lifecycle/16-communication/README.md b/4-Data-Science-Lifecycle/16-communication/README.md index 3eb8cc52..d227491b 100644 --- a/4-Data-Science-Lifecycle/16-communication/README.md +++ b/4-Data-Science-Lifecycle/16-communication/README.md @@ -211,9 +211,7 @@ If Emerson took approach #2, it is much more likely that the team leads will tak [1. Communicating Data - Communicating Data with Tableau [Book] (oreilly.com)](https://www.oreilly.com/library/view/communicating-data-with/9781449372019/ch01.html) - - -## [Post-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/31) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) Review what you've just learned with the Post-Lecture Quiz above! diff --git a/5-Data-Science-In-Cloud/17-Introduction/README.md b/5-Data-Science-In-Cloud/17-Introduction/README.md index 332538d9..4c991143 100644 --- a/5-Data-Science-In-Cloud/17-Introduction/README.md +++ b/5-Data-Science-In-Cloud/17-Introduction/README.md @@ -92,7 +92,7 @@ Sources: ## Post-Lecture Quiz -[Post-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/33) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Assignment diff --git a/5-Data-Science-In-Cloud/18-Low-Code/README.md b/5-Data-Science-In-Cloud/18-Low-Code/README.md index 960373c1..1c2d6507 100644 --- a/5-Data-Science-In-Cloud/18-Low-Code/README.md +++ b/5-Data-Science-In-Cloud/18-Low-Code/README.md @@ -27,7 +27,8 @@ Table of contents: - [Review & Self Study](#review--self-study) - [Assignment](#assignment) -## [Pre-Lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/34) +## [Pre-Lecture quiz](https://ff-quizzes.netlify.app/en/ds/) + ## 1. Introduction ### 1.1 What is Azure Machine Learning? @@ -325,7 +326,7 @@ Congratulations! You just consumed the model deployed and trained it on Azure ML Look closely at the model explanations and details that AutoML generated for the top models. Try to understand why the best model is better than the other ones. What algorithms were compared? What are the differences between them? Why is the best one performing better in this case? -## [Post-Lecture Quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/35) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Review & Self Study diff --git a/5-Data-Science-In-Cloud/19-Azure/README.md b/5-Data-Science-In-Cloud/19-Azure/README.md index 0a6059d7..d6964865 100644 --- a/5-Data-Science-In-Cloud/19-Azure/README.md +++ b/5-Data-Science-In-Cloud/19-Azure/README.md @@ -288,7 +288,7 @@ Congratulations! You just consumed the model deployed and trained on Azure ML wi **HINT:** Go to the [SDK documentation](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) and type keywords in the search bar like "Pipeline". You should have the `azureml.pipeline.core.Pipeline` class in the search results. -## [Post-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/37) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) ## Review & Self Study diff --git a/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index b50e294f..4848364c 100644 --- a/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -11,7 +11,8 @@ We started with definitions of data science and ethics, explored various tools & In this lesson, we'll explore real-world applications of data science across industry and dive into specific examples in the research, digital humanities, and sustainability, contexts. We'll look at student project opportunities and conclude with useful resources to help you continue your learning journey! ## Pre-Lecture Quiz -[Pre-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/38) +[Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) + ## Data Science + Industry Thanks to the democratization of AI, developers are now finding it easier to design and integrate AI-driven decision-making and data-driven insights into user experiences and development workflows. Here are a few examples of how data science is "applied" to real-world applications across the industry: @@ -129,7 +130,8 @@ Here are some examples of data science student projects to inspire you. Search for articles that recommend data science projects that are beginner friendly - like [these 50 topic areas](https://www.upgrad.com/blog/data-science-project-ideas-topics-beginners/) or [these 21 project ideas](https://www.intellspot.com/data-science-project-ideas) or [these 16 projects with source code](https://data-flair.training/blogs/data-science-project-ideas/) that you can deconstruct and remix. And don't forget to blog about your learning journeys and share your insights with all of us. ## Post-Lecture Quiz -[Post-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/39) +## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/) + ## Review & Self Study Want to explore more use cases? Here are a few relevant articles: