Data science - one of the most popular career options in the current times. Nowadays, many freshers as well as experienced professionals are interested in this subject. It is a scientific field of interdisciplinary methods, processes, algorithms and systems that are utilised for the extraction of knowledge and data in structured as well as unstructured forms. Are you an aspiring data scientist, or an existing one? Does data science intrigue you? Do you often wonder if it is rocket science to learn, understand and pursue a career in data science? Or is there an easy way to fulfil your aspiration to be a data scientist? Let us figure out!

## What Is Data Science?

In simple terms, we could call data science as a science involving data, analysis of data, statistics and so on, which majorly employs the methods or theories in tandem with subjects such as mathematics, information science, statistics and computer science.

## What Is Machine Learning?

Being a part of data science, along with algorithms, machine learning is a practise of creating or fine-tuning algorithms using data. It involves making predictive analysis on the data, and is majorly used for data mining. This process comprises regression, Naive Bayes or supervised clustering techniques.

## Is It Good For You To Know Mathematics To Be A Data Scientist?

Yes, absolutely! Since data science involves the study or working around data, statistics, algorithms, etc., knowing mathematics would be most beneficial for you as a data scientist. The streams within mathematics that may be relevant would be algebra, 3-D geometry, statistics, calculus, etc. Through this article, let us focus and understand how linear algebra can effectively help you enhance your data science skills to eventually prepare you for a better career in the same field.

## What Is Linear Algebra?

Before we figure out the direct relation between learning linear algebra to improve skills in data science, let us first understand what linear algebra is. It is one of the major branches of mathematics, involving linear equations and linear functions along with representations in the form of vector spaces or matrices.

Basically, linear algebra is central to most of the areas within mathematics. For instance, in geometry, while defining lines, rotations, planes, etc., or in functional analysis, where linear algebra is used to define the space of functions. Additionally, linear algebra is useful in engineering areas too, in computing the models.

## How Does Linear Algebra Help You With Your Machine Learning Or Data Science Skills?

Since linear algebra is linear spaces, and the kind of spaces required in machine learning or data science may be scalar product spaces, linear algebra is a helpful option.

Machine learning-related techniques are mostly represented as matrices since they handle high dimensional data. In such a scenario, the linear algebra concepts such as matrix multiplication, regression, eigenvalue, etc. come handy.

## How Should You Learn Linear Algebra And Boost Your Data Science Skills For Better Career Options?

Brushing up your knowledge and expertise on the following would certainly help you hone your skills on data science:

• Matrices - Part 1 and Part 2 both

• Determinants

• Simultaneous equations in multiple variables

• Basic linear algebra and vector spaces

• Eigenvalues, eigenvectors, etc.

• Recurrence relations

Once you are clear about how the predictive modelling works in data science, or how algorithms are processed, you could additionally ensure the following to intensify your knowledge of linear algebra:

• **Linear Algebra Notation:** Since algorithms involve matrix and vector notations, learning linear algebra notations would help read, write and implement algorithms or new methods. Another way of implementing linear algebra notations is through Python or any other programming languages.

• **Linear Algebra For Statistics: **Since data science majorly involves data analysis or multivariate statistics, learning linear algebra for statistics is a good option.

•** Linear Algebra Arithmetic: **Since arithmetic involves addition, subtraction and multiplication, in data science, it will help understanding the adding, subtracting or multiplying of scalars, vectors or matrices.

• **Linear Algebra For Matrix Factorisation:** This involves the decomposition of a matrix into various matrices.

## Few popular books to help you learn linear algebra:

• Strang, Gilbert, Linear Algebra and Its Applications (4th ed.)

• B. Noble & J.W. Daniel: Applied Linear Algebra, Prentice-Hall, 1977

• Greub, Werner H., Linear Algebra, Graduate Texts in Mathematics (4th ed.), Springer

• Shilov, Georgi E., Linear algebra, Dover Publications

• Lipschutz, Seymour, 3,000 Solved Problems in Linear Algebra, McGraw-Hill

• Sheldon Axler's Linear Algebra Done Right

• Introduction to Linear Algebra

• Calculus

• Linear Algebra Done Right

•** Algebra: Volume I:** Fields and Galois Theory: 1 (Universitext)

• Numerical Linear Algebra - Trefethen & Bau III

• Numerical Linear Algebra - Sundarapandian

• Matrix Computations

• **Linear Algebra-** a modern introduction" by D. poole

• Linear Algebra by Hoffman/Kunze

You could even opt for online courses for linear algebra. Some of the best online resources are:

https://onlinecourses.nptel.ac.in/

https://www.courses.com/mathematics

https://www.edx.org/

https://www.class-central.com/

https://www.khanacademy.org

www.udemy.com

## Linear Algebra And Data Science Have A Mutual Relevance!

Linear algebra is an integral portion of the extensive maths within data science or the algorithm related to the machine learning aspects. Therefore, if you are accustomed to using the concepts of linear algebra, your data science-related skills will be automatically strengthened.

Be it the data mining or algorithm-writing, your thoughts would have certain clarity, if you are clear about the linear algebra involved in the process. Linear algebra will be useful from the basics to the probability factor, and further modelling of data using the high dimensional matrices or vector spaces, regression specific problems or least squares, and so on.

Therefore, it may be a wonderful idea to make use of the aforesaid links to understand and implement the linear algebra-specific methods in data science. It will not only lead to a logical approach in machine learning concepts for you, but also would help you grow as a data science professional with utmost confidence. Knowing linear algebra may be good, but knowing linear algebra extensively would be fantastic!