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importance of mathematics in data science

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In this article, we will see the importance of mathematics in data science and also a few resources from where one can master mathematics fundamentals and apply them to data science projects. To keep the answer short, it is very important, particularly a few concepts that one will use day in and day out. Just one side note, for first-timers, data science mentors can be game-changer. One-to-one interactions with an expert can help in learning and comprehension. It also develops a high amount of passion and critical thinking needed to succeed. Aspiring students can use Olyye to connect to top data scientists at fraction of the cost. Also, if you are looking for data science courses in Bangalore or machine learning training in Bangalore, we have one of the best training programs in the entire country. Okay, enough of marketing, let’s jump right into the topic to understand a few mathematical concepts which are the backbone of data science.

Linear Algebra

The most important piece of mathematics that every data scientist should know. Machine learning and deep learning concepts use this topic extensively. The best resource to learn linear algebra is this channel. 3 blue 1 Brown is one of my favorite go-to places whenever I feel to hone my mathematical skills. In this series of videos, he has taken a visual approach to all crucial concepts and designed it to clear your thoughts on how linear algebra formulas are created in the first place. Concepts like matrices, vectors, linear transformation, Stretching and bending, eigenvalues, eigenvectors, determinants, and SVDs are a few concepts that you need to know as a minimum requirement. Neural networks are based on linear and non-linear transformations. Principal Component Analysis (PCA) is based on SVDs. In a way, every mathematical model that we are going to create from linear regression to classification is going to use linear algebra.


This topic was dominating engineering maths for a reason. Calculus is widely used and vast. After teaching more than 200 students, I believe this is the most misunderstood topic among all the other mathematical concepts. Derivatives and Integrations are more than by heart formulas! It is the heart of optimization techniques. If someone is already aware of optimization techniques in deep learning and machine learning they might have seen the use of Calculus in backpropagation and gradient descent. But that’s not the end of it. As the heart of statistics and machine learning, one will use derivatives and integration regularly. It is one of the most important tools to have in the toolkit. Again no one has done a better job than this channel of explaining Calculus concepts.

Fourier Series and Transform

When it comes to DS training, Corpnce has given priority to this topic. As part of one of our projects for the University of Queensland on time series, I got a whole different view of understanding and analyzing data using Fourier transform and wavelet transform. Frequency analysis, data compression, time invariance, time series, and pattern classification are a few of the most used applications of Fourier series and Fourier transform. And when it comes to resources, I feel professor Steve Brunton has done the best job of explaining them. You can find his course here.


The geometry needs no introduction. we will use geometry for a superior understanding of areas and dimensions. Distance is used for calculating errors and that needs a fair understanding of Geometry. There will be also complex concepts like saddle points, multidimensions, and distributions that again will test one’s depth in Geometry. We will use it frequently and extensively. A person can’t have a good understanding of linear algebra without understanding Geometry. If you think you need to revise geometry, follow this link.


There are concepts like Permutations and Combinations, Lagrange Theorem, and Laplace that we will use. SVM uses Lagrange whereas probability concepts like binomial and Poisson distribution use the concepts of permutations and combinations. So, in short, there is a good amount of mathematics that you need to learn as a data scientist. If you are interested in learning how important statistics is to become a data scientist you can learn it here. Thanks for reading the article and if you are looking for a data science course in Bangalore don’t forget to check our website here.