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As a data professional, you probably know that mathematics is fundamental to data science. Mathematics underpins data science: from understanding how data points are represented as vectors in a vector space to optimization algorithms that find the best parameters for a model and more.
Therefore, mastering mathematical fundamentals can help you both in interviews and gain a deeper understanding of the algorithms you implement. Here, we have compiled a list of free Massachusetts Institute of technology (MIT) courses on the following math topics:
- Linear algebra
- Calculation
- Statistics
- Probability
You can take these courses at WITH OpenCourseWare platform. Make the most of these courses and improve your data science expertise!
1. Linear algebra
Aside from getting comfortable with high school math, linear algebra is by far the most important math topic for data science. the super popular Linear algebra Prof. Gilbert Strang's course is one of the best math courses you can take. For this and subsequent courses, solve problem sets and attempt quizzes to test your understanding.
The course is structured in the following three main modules:
- Systems of equations Ax = b and the four matrix subspaces
- Least squares, determinants and eigenvalues
- Positive definite matrices and applications.
Link: Linear algebra
2. Single variable and multivariable calculation
It is important to have a good understanding of calculus to master data science concepts. You should be comfortable with single and multivariable calculus, partial derivatives, applying the chain rule, and more. Here are two courses on single variable and multivariable calculus.
He Calculus I: Single variable calculus The course covers:
- Differentiation
- Integration
- Coordinate systems and infinite series.
Once you are comfortable with single variable calculus, you can continue with the Multivariable calculus course that covers:
- Vectors and matrices
- Partial derivatives
- Double integrals and line integrals in the plane.
- Triple integrals and surface integrals in 3D space.
Links to courses.:
3. Analysis of probabilistic systems and applied probability
Probability is another important mathematical topic for data science, and a good foundation in probability is essential for mastering mathematical modeling and statistical analysis and inference.
He Analysis of probabilistic systems and applied probability The course is a great resource that covers the following topics:
- Probability models and axioms.
- Conditioning and Bayes rule
- Independence
- counting
- Discrete and continuous random variables
- Continuous Bayes rule
Link: Analysis of probabilistic systems and applied probability
4. Request Statistics
To master data science, you must have a good foundation in statistics. He Statistics for applications The course covers many applied statistics concepts relevant to data science.
Here is a list of topics covered:
- Parametric inference
- Maximum likelihood estimation
- Moments
- Hypothesis evaluation
- Goodness of fit
- Regression
- Bayesian statistics
- Principal component analysis
- Generalized linear models
If you're interested in exploring statistics in depth, check out 5 Free Courses to Master Statistics for Data Science.
Link: Statistics for applications
5. Matrix Calculus for Machine Learning and More
You should already be familiar with the optimization of single-variable and multivariable calculus courses. But in machine learning, you may encounter large-scale optimization that requires matrix computation and computation in arbitrary vector spaces.
He Matrix calculus for machine learning and more It will help you build on what you have learned in calculus and linear algebra courses. This is perhaps the most advanced course on this list. But it can be very useful if you are planning to take a postgraduate course in data science or if you want to explore machine learning and research.
The following are some of the topics covered in this course:
- Derivatives as linear operators; linear approximations in arbitrary vector spaces
- Derivatives of functions with matrix as input or output
- Derivatives of matrix factorizations
- Multidimensional chain rule
- Manual forward and reverse mode and automatic differentiation.
There are many other optimization approaches and algorithms that you can explore as well.
Link: Matrix calculus for machine learning and more
Ending
If you ever want to master the math for data science, this list of courses should be enough to learn everything you need, whether it's getting into machine learning research or an advanced degree in data science.
If you are looking for some more courses to learn mathematics for data science, read 5 Free Courses to Master Mathematics for Data Science.
twitter.com/balawc27″ rel=”noopener”>Bala Priya C. is a developer and technical writer from India. He enjoys working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include DevOps, data science, and natural language processing. He likes to read, write, code and drink coffee! Currently, he is working to learn and share his knowledge with the developer community by creating tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource descriptions and coding tutorials.
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