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Math for Data Science: self-study materials
23 Jan

Data is often referred to as "new oil" or "new gold" for a good reason. Data helps businesses to grow and prosper, predict trends, identify opportunities, and stay ahead of competitors by providing insights into consumer behavior or certain market conditions before they actually occur. That’s why specialists capable of transforming scattered data into valuable business insights are extremely appraised. If you are interested in stepping in the field of Data Science, start with the materials recommended by EPAM Data specialists.

Fundamentals of mathematical analysis

Derivatives

Integrals

Maxima and Minima

Differential equations 

Linear algebra

Handbook 

Eigenvectors and Eigenvalues

Quadratic Forms 

Mathematics for Machine Learning: Linear Algebra 

A comprehensive course on Linear Algebra covering such topics as vectors and matrices, eigenvalues and eigenvectors, and their implementation in working with datasets. The course aims to help students bridge the gap into linear algebra problems, and understand how to apply these concepts to machine learning.

Probability theory fundamentals

Probability theory

Bayes inference

Statistics

Basic Concepts

Hypothesis Testing

Maximum Likelihood Estimation



Optimization theory

Optimization for Data Scientists

Optimization is one of the three pillars that Data Science professionals must understand thoroughly. Familiarize yourself with its fundamentals.

Algorithms and data structures

Data structures

Sorting algorithms

Algorithm Complexity

  • A part of the Data structures and Algorithms course, dedicated to Big O notation

Basics of Python/SQL

Python Environment

Introduction to Python and Data Science stack

  • A quick crash course on both the Python programming language and its use for scientific computing

SQL basics

This is the “starter pack” to begin your Data science journey. If you find this specialization exciting and would like to dive deep into the world of data, check out our educational programs in Data Science and join us to broaden your knowledge and enrich it with hands-on experience.