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Demystify ....ing Maths for Data Science

- 2 minutes read | By Harshit Singh& Nityanand Mathur

Machine learning is built on the fundamentals of mathematics. However,
you do not need to know or understand all the math to learn and code.

I strongly believe that mathematics for data science is very important but many people might find it fearsome to face the maths. It might feel that just because you don't have a solid background in maths, stats, or computer science, you can't pursue data science.

In this blog, I have attempted to list out basic mathematical elements you need to know before starting a data science career. Even not having expertise in them won’t affect your journey.

**- Algebra -** It is one of the most basic domains of mathematics
which is applied directly/indirectly at many places in machine
learning algorithms such as calculating loss functions,
regularisation, covariance matrices, Singular Value Decomposition
(SVDs), Matrix Operations, and support vector machine classification,
PCA to reduce the dimensionality of data. Linear Algebra is also
intensively used in a neural network for processing and representation
of networks.

**- Calculus -** In calculus, if you are familiar with calculating
the derivative of a multivariate function, then it is quite easy to
understand the concept of calculus in Data Science. One example is
performing Gradient descent for calculating the minima of a loss
function. The essential calculus required for data science is
Differential and Integral Calculus, Partial Derivatives, Vector-Values
Functions, and Directional Gradients.

**- Probability -** Uncertainty is important to machine learning.
There is 3 common uncertainty in data science a) noisy data b) limited
coverage of the problem area c) imperfect models. Using correct
probability techniques we can easily solve the problem of uncertainty.

**- Statistics -** this is the part, most people find difficult.
The critical part needed for machine learning include distributions,
discrimination analysis, and hypothesis testing.

Fundamental
statistics needed for ML are Combinatorics, Axioms, Bayes’ Theorem,
Variance, Expectation, Random Variables, Conditional, and Joint
Distributions.

Complete Maths Tutorial required to start Data Science and Machine Learning......Comming Soon

Knowledge is power. Knowledge shared is power multiplied.