Who is this class for: This course is for anyone who has basic math skills, but is interested in learning or relearning algebra or pre-calculus so they can be successful in other data science math courses.

Created by:  Duke University

  • Daniel Egger

    Taught by:  Daniel Egger, Executive in Residence and Director, Center for Quantitative Modeling

    Pratt School of Engineering, Duke University

  • Paul Bendich

    Taught by:  Paul Bendich, Assistant research professor of Mathematics; Associate Director for Curricular Engagement at the Information Initiative at Duke

CommitmentFour weeks, 3-5 hours per week.
How To PassPass all graded assignments to complete the course.
User Ratings
4.4 stars
Average User Rating 4.4See what learners said

How It Works

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Duke University
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
Ratings and Reviews
Rated 4.4 out of 5 of 596 ratings

Course is simple yet difficult. It explains most of the concepts in simple way but the assignments are pretty hard to solve :) overall makes perfect foundation and provide necessary skills for data Science. More focus on derivatives will help.

Its nice course to brush up basic maths. It would be helpful if you can add more topics which are required to complete data science like matrices and other concepts

As it is now, the course is a much better resource for reviewing the material (which was fine for me as it was what I was trying to do) than for learning it first time. It would be much better if it had more of the same, which is why I am giving it 4 stars instead of 5. In my opinion, it is too brief; I hope to see a part 2 expanding on the material provided here. Many of the topics mentioned, and they really were mentioned more than really taught, should have been talked about in more detail. I've completed the whole course in about 4-6 hours over 2.5 days. It is a good attempt, but it is hardly a sufficient preparation for the field of Data Science; students looking to take the course should be aware of this.

TLDR: A nice and brief overview of many important concepts (sadly, missing linear algebra) which lay the mathematical foundation for getting into Data Science. Needs to be expanded upon.

Excelent course! I am studying at the Data Science Math Skills to fill some math gaps, and also doing the Data Scientist Toolbox . All of them are really great courses. Enjoy!