# 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.

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**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!.

**Bio: Matthew Mayo**is a computer science graduate student currently working on his thesis parallelizing machine learning algorithms. He is also a student of data mining, a data enthusiast, and an aspiring machine learning scientist.

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