Data Science Math Skills
There's no getting around the fact that data science courses include math. This course was built for learners who have fundamental math skills but have not studied algebra or pre-calculus, and was meant to teach learners the basic math they will need to be successful in practically any data science math course. This is one of the best online calculus courses. Data Science Math Skills offers the essential math on which data science is built, with no added complexity, one at a time, presenting unexpected topics and math symbols.
Before going on to more advanced content, learners will grasp the vocabulary, notation, concepts, and algebra rules that all data scientists must know.
The following are some of the topics covered
- Venn diagrams are part of set theory.
- The actual number line's properties
- Interval notation and inequalities algebra
- Summation and Sigma notation are examples of applications.
- Slope and distance formulas on the Cartesian (x,y) plane
- On the x-y plane, graphing and describing functions and their inverses
- Tangent lines to a curve and the concept of instantaneous rate of change
- The natural log function, exponents, and logarithms
- Bayes' theorem is a part of probability theory.
While this course is meant as a broad introduction to the math abilities needed for data science, it can be used as a prerequisite for learners interested in the Excel to MySQL Data Science Specialization course "Mastering Data Analysis in Excel." Learners who master Data Science Math Skills will be well-prepared for the more complex math concepts covered in "Mastering Data Analysis in Excel."
Skills you will gain
- Bayes' Theorem
- Bayesian Probability
- Probability
- Probability Theory
Instructors: Daniel Egger, Paul Bendich
Coursera rate: 4.5/5.0, 10.246 ratings
Offered by: Duke University
Enroll here: https://www.coursera.org/learn/datasciencemathskills
