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

- Comprehensive beginners guide to Linear Algebra for Data Scientists

## Eigenvectors and Eigenvalues

- An easy-to-digest visual explanation of Eigenvalues and Eigenvectors
- Advanced level Eigenvalues and Eigenvectors explanation by MIT educators

## Quadratic Forms

- An explanation of how to express a quadratic form with a matrix

## 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

- A visualized intro to the basic concepts of probability theory
- Basics of probability in seven bite-sized videos
- Probability theory explanation by Crash Course with real-life examples
- Probability explained by Harvard educators

## Bayes inference

- An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python.
- An explanation of the Bayes theorem, which is believed to be the most important one in probability theory
- An introduction to Bayes Theorem for Machine Learning

# Statistics

## Basic Concepts

- Introduction to Statistics for Data Science
- Eight-hour course that covers the essentials of statistics, and introduces the various methods used to collect, organize, summarize, interpret and reach conclusions about data

## Hypothesis Testing

- An Introduction to Statistical Hypothesis Testing
- Step-by-step statistics tutorial that teaches students how to perform hypothesis testing in statistics by working examples and solved problems
- An explanation of how to write the null and alternate hypothesis as part of a hypothesis test in statistics
- A video tutorial that explains how to calculate the P-value in hypothesis testing

## Maximum Likelihood Estimation

- A brief introduction to Maximum Likelihood Estimation for Machine Learning

# 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

- Introduction to sorting techniques in Data Structure

## Algorithm Complexity

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

# Basics of Python/SQL

## Python Environment

- Instructions for setting up a virtual 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.