Proficiency in mathematics is very essential if you are planning to start your career in data science or transitioning to data science from another field. 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 talent pool from which the large organizations pick you up. Consequently, data science aspirants must ensure that they master algebra, calculus, probability, among others before diving deep into machine learning.
Well now that you know what skills do you need, you can easily go to google and search maths for data science where you would find plenty of ads of courses which claim that they have the best instructors, student reviews, syllabi, etcetera which could diverge you from your goal and only serve as a hurdle for selecting the right course.
To make things simpler and clearer for you we have picked up gems out of these pile of courses offered by the world’s top universities and to add icing on the cake: these courses are free to enroll.
1. Mathematics for Machine Learning: Linear Algebra
In this course, you will learn what linear algebra is and how it relates to vectors and matrices and what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally, you’ll look at how to use these to do fun things with datasets – like how to rotate images of faces and how to extract eigenvectors to look at how the PageRank algorithm works.
You will also be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you’ll write code blocks and encounter Jupyter notebooks in Python, but don’t worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before.
Course overview:
- Skills you will gain:
- Eigenvalues & Eigenvectors
- Basis
- Transformation Matrix
- Linear Algebra
- Beginner level
- Duration: Approx 19 hours to complete
- Offered by: Imperial College London
You can enroll in this course for free by auditing this course or earn a certificate by applying for financial aid. (Note: This aid is made available only to students who are financially deprived)
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.
Course Overview:
- Skills you will gain:
- Probability theory
- Random Variables
- Independence
- Monte Carlo Simulation
- Compute expected values and standard errors in R
- Central Limit theorem
- Beginner level
- Duration: Self-paced
- Offered by: HarvardX
3. Introduction to Calculus
Calculus is another important concept for data science that is used in back-propagation used in neural networks 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.
Course Overview:
- Skills you will gain:
- Familiarity with Precalculus
- Manipulation of equations and elementary functions
- Develop fluency with preliminary methodology of tangents and limits
- Definition of derivatives
- Differential calculus and applications
- Integral calculus
- Intermediate level
- Duration: 51 Hours
- Offered by: The University of Sydney
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.
Course Overview:
- Skills you will learn:
- Derivatives of Multivariable calculus
- Applications of Multivariable derivatives
- Integrating multivariable functions
- Divergence Theorems
- Intermediate level
- Duration: Self-paced
- Offered by: Khan Academy
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.
Course Overview:
- Skills you will gain:
- Forward Euler Method
- Comparing solvers
- Heun’s Method
- Symplectic Euler Method
- Implicit methods and Stiffness
- Stability, Sensitivity, and Optimization
- Friction, Equilibria and Control theory
- Partial differential equations and Heat conduction
- Chaos, Software, and Capability.
- Intermediate level
- Duration: 2 Months
- Offered by: Udacity
- Prerequisites:
- Basics of Computer Science
- Python Fundamentals
- Vector Algebra and Trigonometry
- Calculus
- Recalling basic physics won’t hurt.
6. Linear Algebra
Linear Algebra by MIT is yet another course recorded by MIT to provide advanced educational services. The course will help you understand linear algebra to its fullest. The course has 34 video lectures given by professor Gilbert Strang who is also the author of Introduction to Linear Algebra, 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.
Course Overview:
- Skills you will gain:
- Graphs and Networks
- Systems of Differential Equations
- Least Squares and Projections
- Fourier Series
- Fast Fourier Transform
- Intermediate level
- Duration: Self-paced
- Offered by: MIT
7. Data Science Maths Skills
This course is designed to teach learners the basic math they 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.
Course Overview:
- Skills you will gain:
- Bayes’ Theorem
- Bayesian Probability
- Probability Theory
- General math skills for data science
- Intermediate level
- Duration: 13 hours
- Offered by: Duke University
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 optimization techniques.
Course Overview:
- Skills you will gain:
- Topic Modelling with NMF and SVD
- Background Removal with Robust PCA
- Compressed Sensing with Robust Regression
- Predicting Health Outcomes with Linear Regression
- Implementing Linear Regression
- PageRank with Eigen Decompositions
- Implementing QR Factorization
- Advanced level
- Duration: Self-paced
- Offered by: fast.ai on GitHub
Whoever plans to enter into data science (DS) should look for completing the above courses to get started. We have classified courses for all the levels so you can try your brain at any of them no matter at which level you are entering into data science. If you think we dropped an important course please let us know in the comments.
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2 replies on “Here Are 8 Free Math Courses Aspiring Data Scientist Must Take.”
Useful for anyone looking to be productive in lockdown.
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Activity is not always productivity. Things that push you out of your comfort zone make you more valuable and productive.
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