Linear algebra and statistics courses for data scientists and machine learning engineers.
Mathematics is crucial for really understanding how Data Science works. The basics of linear algebra and statistics are needed to interpret data and train machine learning models. In this text I will cover 3 most popular math courses on Coursera, which are accessible to any math beginner and can help you with your data science career.
3 Best Math Courses on Coursera:
- Data Science Math Skills by Duke University
- Mathematics for Machine Learning by Imperial College London
- Statistics with Python by University of Michigan
Math Courses on Coursera in 2020
Coursera is a great platform to learn any tech related skill with a vast range of courses and a certification system. Courses are available for free if you don’t need a certificate for completion.
Data Science Math Skills by Duke University
Data Science Math Skills offered by Duke University is the most popular course on mathematics fundamentals on Coursera right now. 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.
Mathematics for Machine Learning by Imperial College London
Mathematics for Machine Learning offered by Imperial College London is a specialization which consists of 3 courses: Linear Algebra, Calculus and PCA. It’s great for people who want to have strong math fundamentals.
In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.
The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.
The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.
Statistics with Python by University of Michigan
Statistics with Python offered by University of Michigan is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language.
Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.
Choosing a math course for you
When choosing a math course you should asses your math level by looking at curriculum of those courses. See how many concepts you don’t understand and pick a place when you want to start your education again.
If you’re looking for other educational materials, have a look here:
There are plenty of materials to learn online.
Last but not least, you can read my Data Science Job ebook on how to approach a career in Data Science and what to expect. From zero to hero in 100 pages.