What is this?
A free 10-week, 20-lesson data science curriculum from Microsoft's Azure Cloud Advocates, built to take complete beginners from core concepts all the way to real-world projects.
A free 10-week, 20-lesson data science curriculum from Microsoft's Azure Cloud Advocates, built to take complete beginners from core concepts all the way to real-world projects.How is the course structured?Ten weeks, 20 lessons grouped into themed blocks: intro, working with data, visualization, lifecycle, cloud, and real-world use. Each lesson pairs written material with pre- and post-lesson quizzes, a project, a challenge and an assignment, and projects grow harder toward week 10.
Ten weeks, 20 lessons grouped into themed blocks: intro, working with data, visualization, lifecycle, cloud, and real-world use. Each lesson pairs written material with pre- and post-lesson quizzes, a project, a challenge and an assignment, and projects grow harder toward week 10.How do I take the course on my own?Fork the whole repo, start each lesson with its pre-lecture quiz, then read the lesson and complete the activities yourself instead of copying solutions. Solutions sit in /solutions folders, and forming a study group is encouraged.
Fork the whole repo, start each lesson with its pre-lecture quiz, then read the lesson and complete the activities yourself instead of copying solutions. Solutions sit in /solutions folders, and forming a study group is encouraged.Do I need to know Python before starting?No overall prerequisite is stated, but the Python lesson recommends a foundational understanding of Python programming since it teaches data exploration with Pandas. The rest of the course starts from zero.
No overall prerequisite is stated, but the Python lesson recommends a foundational understanding of Python programming since it teaches data exploration with Pandas. The rest of the course starts from zero.I only want the lessons — is the download really that big?Yes: the repo carries 50+ language translations which significantly increases its size. The README gives sparse-checkout commands for Bash/macOS/Linux and Windows CMD so you can clone just what you need for the course.
Yes: the repo carries 50+ language translations which significantly increases its size. The README gives sparse-checkout commands for Bash/macOS/Linux and Windows CMD so you can clone just what you need for the course.Is it really usable by absolute beginners?Yes — a dedicated examples directory takes you from a 'Hello World' data science program through loading data, simple analysis, basic visualization and a full real-world project before you start the lessons.
Yes — a dedicated examples directory takes you from a 'Hello World' data science program through loading data, simple analysis, basic visualization and a full real-world project before you start the lessons.Can I do the whole course in the browser without installing anything?Yes. Open the repo in GitHub Codespaces via the Code dropdown and you get a ready-to-use environment with no local setup; otherwise follow the Installation Guide to configure your machine.
Yes. Open the repo in GitHub Codespaces via the Code dropdown and you get a ready-to-use environment with no local setup; otherwise follow the Installation Guide to configure your machine.What does the end of the course look like?Lessons 17-20 move to applied work: data science in the cloud, training models with low-code tools, deploying models with Azure Machine Learning Studio, and a look at data science driven projects in the real world.
Lessons 17-20 move to applied work: data science in the cloud, training models with low-code tools, deploying models with Azure Machine Learning Studio, and a look at data science driven projects in the real world.Is there anything here for teachers?Yes — the repo includes teaching guidance and classroom resources, and instructors are invited to share feedback on how they use the curriculum in the discussion forum.
Yes — the repo includes teaching guidance and classroom resources, and instructors are invited to share feedback on how they use the curriculum in the discussion forum.Why the constant quizzes before and after each lesson?It is deliberate pedagogy: the low-stakes pre-lesson quiz sets your intention toward the topic, the post-lesson quiz reinforces retention, and project-based lessons let you learn while building so the new skills stick.
It is deliberate pedagogy: the low-stakes pre-lesson quiz sets your intention toward the topic, the post-lesson quiz reinforces retention, and project-based lessons let you learn while building so the new skills stick.What are similar projects?mlcourse.ai, ML-For-Beginners, IoT-For-Beginners, pandas_exercises, Python-100-Days, 100-Days-Of-ML-Code, 30-Days-Of-Python, pydata-book
mlcourse.ai, ML-For-Beginners, IoT-For-Beginners, pandas_exercises, Python-100-Days, 100-Days-Of-ML-Code, 30-Days-Of-Python, pydata-bookWhich topics is it in?microsoft-for-beginners, matplotlib, pandas, nosql, data-analysis, microsoft, curriculum, course, jupyter notebook, data-visualization, sql, data-science
microsoft-for-beginners, matplotlib, pandas, nosql, data-analysis, microsoft, curriculum, course, jupyter notebook, data-visualization, sql, data-science