About this course: Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material. Topics include: ~Set theory, including Venn diagrams ~Properties of the real number line ~Interval notation and algebra with inequalities ~Uses for summation and Sigma notation ~Math on the Cartesian (x,y) plane, slope and distance formulas ~Graphing and describing functions and their inverses on the x-y plane, ~The concept of instantaneous rate of change and tangent lines to a curve ~Exponents, logarithms, and the natural log function. ~Probability theory, including Bayes’ theorem. While this course is intended as a general introduction to the math skills needed for data science, it can be considered a prerequisite for learners interested in the course, "Mastering Data Analysis in Excel," which is part of the Excel to MySQL Data Science Specialization. Learners who master Data Science Math Skills will be fully prepared for success with the more advanced math concepts introduced in "Mastering Data Analysis in Excel." Good luck and we hope you enjoy the course!
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.
Taught by: Daniel Egger, Executive in Residence and Director, Center for Quantitative Modeling
Pratt School of Engineering, Duke University
Taught by: Paul Bendich, Assistant research professor of Mathematics; Associate Director for Curricular Engagement at the Information Initiative at Duke
Mathematics
| Level | Beginner |
| Commitment | Four weeks, 3-5 hours per week. |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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Very good course, can't wait for the more advance classes.
While most of material is well known, it is presented in a great way, so it is a clean and smart refresher for Sets, basic Algebra and notations, Cartesian geometry and functions, and derivatives. I knew the material about logarithms, exponentials and probabilities, but I felt that I knew it better in the end of this courses. Material is great, and teachers are very clear. I wish they came with more material about calculus (matrices), vector spaces, Lagrangian, Hessian and so on, which are also really interesting in Data Sciences.
Thanks a lot for a wonderful course. Gave a quick overview of the basic Math skills required for Data Science.
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Quality instruction with clear explanation of concepts and relevant practice/test modules to check your understanding.