Data Science: Basic Mathematics

You might be thinking how much math we need to know to get started with Data Science? There is no specific answer to that but I have the way for you to get started.

Why?

Unlike software engineering data-science is not mostly about programming, it's more about data and understanding relation between datapoints. In order to do that we need eye for that and most of us don't have that so we need math to make sense of the data. A significant portion of your ability to translate yourdata science skillsinto real-world scenarios depends on your success and understanding of mathematics. Mathematical knowledge is necessary for data science careers because machine learning algorithms, data analysis, and insight discovery all depend on it. Although there are other requirements for your degree and employment in data science, math is frequently one of the most crucial.

Data Science Maths

Let's talk about the most common types of math that you will use in your data science career.

Linear Algebra

Linear algebra is the branch of mathematics that deals with vector spaces. It contains concept of vector, matrix etc. Linear algebra is widely used by data scientists (frequently implicitly, and not infrequently by people who dont understand it). It wouldnt be a bad idea to read a textbook.

Resource

• https://www.khanacademy.org/math/linear-algebra
• https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
• https://web.stanford.edu/~boyd/vmls/
• http://mitran-lab.amath.unc.edu/courses/MATH347DS/textbook.pdf
• https://fong.cs.wmich.edu/modules/LinearAlgebraPrimerConcepts.pdf

Statistics & Probability

Statistics refers to the mathematics and techniques with which we understand data. This is essential in machine learning when working with classifications such as logistic regression, discrimination analysis and hypothesis testing and distributions.

Resource

• https://www.khanacademy.org/math/statistics-probability
• https://seeing-theory.brown.edu/#secondPage
• https://www.youtube.com/watch?v=XcLO4f1i4Yo
• https://www.udacity.com/course/intro-to-descriptive-statistics--ud827

Calculus

Calculus is used in machine learning to create loss/cost/objective functions, which are used to train algorithms to achieve their goals. It contains study of derivatives, curvature, divergence, and quadratic approximations.

Resource

• https://www.khanacademy.org/math/multivariable-calculus
• https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
• https://ocw.mit.edu/courses/18-01sc-single-variable-calculus-fall-2010/
• https://ocw.mit.edu/courses/18-02sc-multivariable-calculus-fall-2010/

In future article we will talk about each topic in detail and how to use them and when to use them so stay tuned and save the series. If you have anything to say comment down below I'm new to blog writing so any type of feedback is appreciated Thanks.

References

• https://towardsdatascience.com/mathematics-for-data-science-e53939ee8306
• https://www.analyticsvidhya.com/blog/2021/06/how-to-learn-mathematics-for-machine-learning-what-concepts-do-you-need-to-master-in-data-science/
• https://www.simplilearn.com/math-and-data-science-article