Data Science for Beginners – A Curriculum

In Data Science for Beginners course We have chosen two pedagogical tenets while building this curriculum: ensuring that it is project-based and that it includes frequent quizzes. By the end of this series, students will have learned basic principles of data science, including ethical concepts, data preparation, different ways of working with data, data visualization, data analysis, real-world use cases of data science, and more.

In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 10 week cycle.

Find our Code of ConductContributingTranslation guidelines. We welcome your constructive feedback!

Each lesson includes:

  • Optional sketchnote
  • Optional supplemental video
  • Pre-lesson warmup quiz
  • Written lesson
  • For project-based lessons, step-by-step guides on how to build the project
  • Knowledge checks
  • A challenge
  • Supplemental reading
  • Assignment
  • Post-lesson quiz

A note about quizzes: All quizzes are contained in this app, 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.

00 Roadmap
Data Science for Beginners - A Curriculum 3
Lesson NumberTopicLesson GroupingLearning ObjectivesLinked LessonAuthor
01Defining Data ScienceIntroductionLearn the basic concepts behind data science and how it’s related to artificial intelligence, machine learning, and big data.lesson videoDmitry
02Data Science EthicsIntroductionData Ethics Concepts, Challenges & Frameworks.lessonNitya
03Defining DataIntroductionHow data is classified and its common sources.lessonJasmine
04Introduction to Statistics & ProbabilityIntroductionThe mathematical techniques of probability and statistics to understand data.lesson videoDmitry
05Working with Relational DataWorking With DataIntroduction to relational data and the basics of exploring and analyzing relational data with the Structured Query Language, also known as SQL (pronounced “see-quell”).lessonChristopher
06Working with NoSQL DataWorking With DataIntroduction to non-relational data, its various types and the basics of exploring and analyzing document databases.lessonJasmine
07Working with PythonWorking With DataBasics of using Python for data exploration with libraries such as Pandas. Foundational understanding of Python programming is recommended.lesson videoDmitry
08Data PreparationWorking With DataTopics on data techniques for cleaning and transforming the data to handle challenges of missing, inaccurate, or incomplete data.lessonJasmine
09Visualizing QuantitiesData VisualizationLearn how to use Matplotlib to visualize bird data 🦆lessonJen
10Visualizing Distributions of DataData VisualizationVisualizing observations and trends within an interval.lessonJen
11Visualizing ProportionsData VisualizationVisualizing discrete and grouped percentages.lessonJen
12Visualizing RelationshipsData VisualizationVisualizing connections and correlations between sets of data and their variables.lessonJen
13Meaningful VisualizationsData VisualizationTechniques and guidance for making your visualizations valuable for effective problem solving and insights.lessonJen
14Introduction to the Data Science lifecycleLifecycleIntroduction to the data science lifecycle and its first step of acquiring and extracting data.lessonJasmine
15AnalyzingLifecycleThis phase of the data science lifecycle focuses on techniques to analyze data.lessonJasmine
16CommunicationLifecycleThis phase of the data science lifecycle focuses on presenting the insights from the data in a way that makes it easier for decision makers to understand.lessonJalen
17Data Science in the CloudCloud DataThis series of lessons introduces data science in the cloud and its benefits.lessonTiffany and Maud
18Data Science in the CloudCloud DataTraining models using Low Code tools.lessonTiffany and Maud
19Data Science in the CloudCloud DataDeploying models with Azure Machine Learning Studio.lessonTiffany and Maud
20Data Science in the WildIn the WildData science driven projects in the real world.lessonNitya
Data Science for Beginners

Data Science for Beginners: GitHub Codespaces

Follow these steps to open this sample in a Codespace:

  1. Click the Code drop-down menu and select the Open with Codespaces option.
  2. Select + New codespace at the bottom on the pane. For more info, check out the GitHub documentation.

VSCode Remote - Containers

Follow these steps to open this repo in a container using your local machine and VSCode using the VS Code Remote - Containers extension:

  1. If this is your first time using a development container, please ensure your system meets the pre-reqs (i.e. have Docker installed) in the getting started documentation.

To use this repository, you can either open the repository in an isolated Docker volume:

Note: Under the hood, this will use the Remote-Containers: Clone Repository in Container Volume... command to clone the source code in a Docker volume instead of the local filesystem. Volumes are the preferred mechanism for persisting container data.

Or open a locally cloned or downloaded version of the repository:

  • Clone this repository to your local filesystem.
  • Press F1 and select the Remote-Containers: Open Folder in Container... command.
  • Select the cloned copy of this folder, wait for the container to start, and try things out.

Offline access

You can run this documentation offline by using Docsify. Fork this repo, install Docsify on your local machine, then in the root folder of this repo, type docsify serve. The website will be served on port 3000 on your localhost: localhost:3000.

Note, notebooks will not be rendered via Docsify, so when you need to run a notebook, do that separately in VS Code running a Python kernel.