Proficiency in mathematics is essential for aspirants to get started with their data science journey. A strong foundation in mathematics will help beginners to not only learn existing and new machine learning techniques easily but also differentiate themselves from others in the competitive market. Consequently, data science aspirants must ensure that they master algebra, calculus, probability, among others before diving deep into machine learning.

Here are top courses on mathematics that aspiring data scientists must take into account while devising their learning strategy.

**1| Mathematics for Machine Learning: Linear Algebra**

The five-week-long course on Coursera can be the starting point for learners as linear algebra has a wide range of applications in data science practices. Linear algebra is essential when you start learning machine learning techniques right from the basics to advanced approaches. The use case ranges from regression analysis to sentiment analysis in NLP and image processing in computer vision.

Created by Imperial College London, Mathematics for Machine Learning course also has numerous programming assignments to help you practice while applying the learning on real-world use cases.

**2| Data Science: Probability**

Hosted by HarvardX on edX, Data Science: Probability is an eight-week course on probability. You will learn various probability theories, including random variables and independence. After completion of this course, you will be able to understand the importance of the central limit theorem in probability, which is essential while implementing statistics to find insights. Besides, you will perform Monte Carlo simulations and implement the learning in the R programming language. The course also comes with a case study on the financial crisis of 2007-2008 to help you understand the importance of probability while implementing in the real world use case.

**3| Introduction to Calculus**

Calculus is another important concept for data science that is used in back-propagation and other machine learning techniques. This course consists of lessons on almost every approach of calculus, which will give you a complete understanding of the concept. Spread across five weeks, this course is a must for data science aspirants to learn the mathematics behind machine learning modules.

**4| Multivariable Calculus **

Multivariable Calculus is a comprehensive course hosted on Khan Academy, which keeps the course updated with new assignments and reading materials. Some of the key concepts that you will learn are derivatives of multivariable functions, applications of multivariable derivatives, integrating multivariable functions, divergence theorems, among others. Advanced calculus will allow you to better understand the machine learning algorithms, thereby enhancing your capabilities to develop robust models.

**5| Differential Equations in Action**

Differential Equations in Action course is focused on enhancing your critical thinking to solve real-world problems. You will be expected to apply your learning of Python, calculus, and algebra to write algorithms to solve problems with differential equations. The two-month course is an intermediate-level course for aspirants who wants to improve their intuitions for data science. Some of the assignments of the course are: fight forest fires, rescue the Apollo 12 astronauts, and stop the spread of epidemics.

**6| Linear Algebra**

Linear Algebra by MIT is yet another course recorded in the fall of 1999 by MIT to provide advance educational services. The course will help you understand linear algebra to its fullest. The course has 34 video lecture, which also includes lessons on solving several problems with linear algebra. Although it doesn’t teach you the implementation of the concepts with programming languages, it provides a comprehensive learning experience of linear algebra.

**7| Data Science Math Skills**

Data Science Math Skills course is focused on covering basics mathematics skills like Venn diagrams, algebra, mean, variance, point-slope formula for line, logarithms, and Bayes’ theorem, and permutation and combination. This is an ideal course for aspirants who wants to learn right from the very basics of mathematics.

**8| Computational Linear Algebra for Coders**

Computational Linear Algebra for Coders is hosted on GitHub by fast.ai to teach matrix computation with acceptable speed and accuracy. The course includes Python with Jupyter notebook along with the libraries such as PyTorch, NumPy, Scikit-Learn, and more. Although it is not for complete beginners but after completing the above courses, one can get to the next level of implementation of the algebra, along with optimisation techniques.

**Source - AIM**