15 Mathematics MOOCs for Data Science to be Data Scientist

By Kimberly Cook |Email | Sep 8, 2018 | 14073 Views

The essential mathematics necessary for Data Science can be acquired with these 15 MOOCs, with a strong emphasis on applied algebra & statistics.

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 outside-the-box - 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: Self-paced (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 real-world applications of algebra. 

5. Introduction to Algebra 
School: School Yourself 
Platform: edX 
Instructor: Zach Wissner-Gross, et al. 
Dates: Self-paced (any time) 

Description excerpt: Algebra is an essential tool for all of high school and college-level 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 Least-Squares, 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 2-part course. In this part, we'll learn the 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. 

8. Applications of Linear Algebra Part 2 
School: Davidson College
Platform: edX
Instructor: Tim Chartier, et al.
Dates: Archived material (any time) 

Description excerpt: This class has a focus on data mining with some applications of computer graphics. We'll discuss, in further depth than part 1, sports ranking and ways to rate teams from thousands of games. We'll apply the methods to March Madness. We'll also learn methods behind web search, utilized by such companies as Google. We'll also learn to cluster data to find similar groups and also how to compress images to lower the amount of storage used to store them. 

Calculus 
An understanding of calculus can help the practitioner think in terms of understanding change. 

9. Calculus One 
School: Ohio State University
Platform: Coursera
Instructor: Jim Fowler 
Dates: Self-paced (any time) 

Description excerpt: Calculus is about the very large, the very small, and how things change. The surprise is that something seemingly so abstract ends up explaining the real world. Calculus plays a starring role in the biological, physical, and social sciences. By focusing outside of the classroom, we will see examples of calculus appearing in daily life. 

Statistics
The mathematical meat of Data Science. These courses start with basic statistics topics and finish with some exploratory analysis in R. 

10. Introduction to Statistics: Descriptive Statistics 
School: UC Berkeley
Platform: edX
Instructor: Ani Adhikari & Philip B. Stark 
Dates: Archived material (any time) 

Description excerpt: The focus of Stat2.1x is on descriptive statistics. The goal of descriptive statistics is to summarize and present numerical information in a manner that is illuminating and useful. The course will cover graphical as well as numerical summaries of data, starting with a single variable and progressing to the relation between two variables. Methods will be illustrated with data from a variety of areas in the sciences and humanities. 

11. Introduction to Statistics: Probability 
School: UC Berkeley
Platform: edX
Instructor: Ani Adhikari & Philip B. Stark 
Dates: Archived material (any time) 

Description excerpt: The focus of Stat2.2x is on probability theory: exactly what is a random sample, and how does randomness work? If you buy 10 lottery tickets instead of 1, does your chance of winning go up by a factor of 10? What is the law of averages? How can polls make accurate predictions based on data from small fractions of the population? What should you expect to happen "just by chance"? These are some of the questions we will address in the course. 

12. Introduction to Statistics: Inference 
School: UC Berkeley
Platform: edX
Instructor: Ani Adhikari & Philip B. Stark 
Dates: Archived material (any time) 

Description excerpt: Stat 2.3x will discuss good ways to select the subset (yes, at random); how to estimate the numerical quantity of interest, based on what you see in your sample; and ways to test hypotheses about numerical or probabilistic aspects of the problem. 

13. Explore Statistics with R 
School: Karolinska Institutet
Platform: edX
Instructor: Andreas Montelius, et al. 
Dates: Self-paced (any time) 

Description excerpt: Do you want to learn how to harvest health science data from the Internet? Or learn to understand the world through data analysis? Start by learning R Statistics! Learn how to use R, a powerful open source statistical programming language, and see why it has become the tool of choice in many industries in this introductory R statistics course. 

Advanced 
A few slightly more advanced topics covering optimization and applied linear algebra. 

14. Discrete Optimization 
School: University of Melbourne 
Platform: Coursera 
Instructor: Pascal Van Hentenryck 
Dates: Archived material (any time) 

Description excerpt: This class is an introduction to discrete optimization and exposes students to some of the most fundamental concepts and algorithms in the field. It covers constraint programming, local search, and mixed-integer programming from their foundations to their applications for complex practical problems in areas such as scheduling, vehicle routing, supply-chain optimization, and resource allocation. 

15. Coding the Matrix: Linear Algebra through Computer Science Applications 
School: Brown University 
Platform: Coursera 
Instructor: Philip Klein 
Dates: Archived material (any time) 

Description excerpt: In this class, you will learn the concepts and methods of linear algebra, and how to use them to think about problems arising in computer science. You will write small programs in the programming language Python to implement basic matrix and vector functionality and algorithms, and use these to process real-world data. 

Bonus: Linear Algebra for Beginners: Open Doors to Great Careers 
School: Western Governors U. (and Trident U. International)
Platform: Udemy
Instructor: Richard Han
Dates: Self-paced (any time) 

Description excerpt: Learn the core topics of Linear Algebra to open doors to Data Science!. 


Source: HOB