15 Mathematics MOOCs for Data Science
The essential mathematics necessary for Data Science can be acquired with these 15 MOOCs, with a strong emphasis on applied algebra & statistics.
By Matthew Mayo.
Most of the mathematics required for Data Science lie within the realms of statistics and algebra, which explains the disproportionate number of these courses listed below. A few other areas are included to round out the list, including calculus, finite mathematics, and a few more advanced offerings; however, the essence of the skills on parade here are statistical and algebraic in nature.
Statistics, in particular, is at the very foundation of Data Science, and is the collection of tools which helps us separate significance from randomness. Algebra is quite often at the heart of the analysis itself. The further quantitative skills facilitate intuition, which is essential in analytics.
Whether you lack formal quantitative education, or are simply looking to brush up on these skills, the following MOOCs can help you achieve your goals.
Getting in the Mood
These courses can help lay the foundation for quantitative thinking.
1. Introduction to Mathematical Thinking
School: Stanford
Platform: Coursera
Instructor: Keith Devlin
Dates: Sep. 21  Nov. 27
Description excerpt: [A] key feature of mathematical thinking is thinking outsidethebox  a valuable ability in today's world. This course helps to develop that crucial way of thinking.
2. Math is Everywhere: Applications of Finite Math
School: Davidson College
Platform: Udemy
Instructor: Tim Chartier
Dates: Selfpaced (any time)
Description excerpt: Computer fonts, Angry Birds, March Madness, and Google  sound like fun? Indeed, finite math is engaging and influences the world around us.
3. Model Thinking
School: University of Michigan
Platform: Coursera
Instructor: Scott E. Page
Dates: Oct. 5  Dec. 13
Description excerpt: Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures.
4. Introduction to Logic
School: Stanford
Platform: Coursera
Instructor: Michael Genesereth
Dates: Sep. 28  Nov. 21
Description excerpt: This course is a basic introduction to Logic. It shows how to formalize information in form of logical sentences. It shows how to reason systematically with this information to produce all logical conclusions and only logical conclusions. And it examines logic technology and its applications  in mathematics, science, engineering, business, law, and so forth.
Algebra
These courses progress from introductory to realworld applications of algebra.
5. Introduction to Algebra
School: School Yourself
Platform: edX
Instructor: Zach WissnerGross, et al.
Dates: Selfpaced (any time)
Description excerpt: Algebra is an essential tool for all of high school and collegelevel math, science, and engineering. So if you're starting out in one of these fields and you haven't yet mastered algebra, then this is the course for you!
6. Linear Algebra  Foundations to Frontiers
School: University of Texas at Austin
Platform: edX
Instructor: Maggie Myers & Robert A. van de Geijn
Dates: Archived material (any time)
Description excerpt: Through short videos, exercises, visualizations, and programming assignments, you will study Vector and Matrix Operations, Linear Transformations, Solving Systems of Equations, Vector Spaces, Linear LeastSquares, and Eigenvalues and Eigenvectors.
7. Applications of Linear Algebra Part 1
School: Davidson College
Platform: edX
Instructor: Tim Chartier, et al.
Dates: Archived material (any time)
Description excerpt: This course is part 1 of a 2part course. In this part, we'll learn basics of matrix algebra with an emphasis on application. This class has a focus on computer graphics while also containing examples in data mining. We'll learn to make an image transparent, fade from one image to another, and rotate a 3D wireframe model. We'll also mine data; for example, we will find similar movies that one might enjoy seeing.
Most of the mathematics required for Data Science lie within the realms of statistics and algebra, which explains the disproportionate number of these courses listed below. A few other areas are included to round out the list, including calculus, finite mathematics, and a few more advanced offerings; however, the essence of the skills on parade here are statistical and algebraic in nature.
Statistics, in particular, is at the very foundation of Data Science, and is the collection of tools which helps us separate significance from randomness. Algebra is quite often at the heart of the analysis itself. The further quantitative skills facilitate intuition, which is essential in analytics.
Whether you lack formal quantitative education, or are simply looking to brush up on these skills, the following MOOCs can help you achieve your goals.
Getting in the Mood
These courses can help lay the foundation for quantitative thinking.
1. Introduction to Mathematical Thinking
School: Stanford
Platform: Coursera
Instructor: Keith Devlin
Dates: Sep. 21  Nov. 27
Description excerpt: [A] key feature of mathematical thinking is thinking outsidethebox  a valuable ability in today's world. This course helps to develop that crucial way of thinking.
2. Math is Everywhere: Applications of Finite Math
School: Davidson College
Platform: Udemy
Instructor: Tim Chartier
Dates: Selfpaced (any time)
Description excerpt: Computer fonts, Angry Birds, March Madness, and Google  sound like fun? Indeed, finite math is engaging and influences the world around us.
3. Model Thinking
School: University of Michigan
Platform: Coursera
Instructor: Scott E. Page
Dates: Oct. 5  Dec. 13
Description excerpt: Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures.
4. Introduction to Logic
School: Stanford
Platform: Coursera
Instructor: Michael Genesereth
Dates: Sep. 28  Nov. 21
Description excerpt: This course is a basic introduction to Logic. It shows how to formalize information in form of logical sentences. It shows how to reason systematically with this information to produce all logical conclusions and only logical conclusions. And it examines logic technology and its applications  in mathematics, science, engineering, business, law, and so forth.
Algebra
These courses progress from introductory to realworld applications of algebra.
5. Introduction to Algebra
School: School Yourself
Platform: edX
Instructor: Zach WissnerGross, et al.
Dates: Selfpaced (any time)
Description excerpt: Algebra is an essential tool for all of high school and collegelevel math, science, and engineering. So if you're starting out in one of these fields and you haven't yet mastered algebra, then this is the course for you!
6. Linear Algebra  Foundations to Frontiers
School: University of Texas at Austin
Platform: edX
Instructor: Maggie Myers & Robert A. van de Geijn
Dates: Archived material (any time)
Description excerpt: Through short videos, exercises, visualizations, and programming assignments, you will study Vector and Matrix Operations, Linear Transformations, Solving Systems of Equations, Vector Spaces, Linear LeastSquares, and Eigenvalues and Eigenvectors.
7. Applications of Linear Algebra Part 1
School: Davidson College
Platform: edX
Instructor: Tim Chartier, et al.
Dates: Archived material (any time)
Description excerpt: This course is part 1 of a 2part course. In this part, we'll learn basics of matrix algebra with an emphasis on application. This class has a focus on computer graphics while also containing examples in data mining. We'll learn to make an image transparent, fade from one image to another, and rotate a 3D wireframe model. We'll also mine data; for example, we will find similar movies that one might enjoy seeing.
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