Exploration of Data Science requires certain background in probability and statistics. This course introduces you to the necessary sections of probability theory and statistics, guiding you from the very basics all way up to the level required for jump starting your ascent in Data Science.

이 강좌는 Mathematics for Data Science 전문 분야의 일부입니다.

# Probability Theory, Statistics and Exploratory Data Analysis

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## About this Course

#### 100% 온라인

#### 다음 전문 분야의 4개 강좌 중 4번째 강좌:

#### 유동적 마감일

#### 완료하는 데 약 19시간 필요

#### 영어

### 제공자:

#### 국립 연구 고등 경제 대학

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more.

## 석사 학위 취득 시작

## 강의 계획 - 이 강좌에서 배울 내용

**완료하는 데 3시간 필요**

## Conditional probability and Independence

During this week we discuss conditional probability and independence of events. Sometimes we can use this definition to find probabilities. Sometimes we check that this definition fulfills to assure whether events are independent. We discuss important law of total probability, which allows us to find probability of some event when we know its conditional probabilities provided some hypotheses and probabilities of the hypotheses. We also discuss Bayes's rule which allows us to find probability of hypothesis provided that some event occurred. We demonstrate how Python can be used for calculating conditional probabilities and checking independence of events.

**완료하는 데 3시간 필요**

**13개의 동영상**

**7개 연습문제**

**완료하는 데 3시간 필요**

## Random variables

Random variable denotes a value that depends on the result of some random experiment. Some natural examples of random variables come from gambling and lotteries. There are two main classes of random variables that we will consider in this course. This week we'll learn discrete random variables that take finite or countable number of values. Discrete random variables can be described by their distribution. We'll consider various discrete distributions, introduce notions of expected value and variance and learn to generate and visualize discrete random variables with Python.

**완료하는 데 3시간 필요**

**15개의 동영상**

**3개 연습문제**

**완료하는 데 3시간 필요**

## Systems of random variables; properties of expectation and variance, covariance and correlation.

Several random variables associated with the same random experiment constitute a system of random variables. To describe system of discrete random variables one can use joint distribution, which takes into account all possible combinations of values that random variables may take. We'll find some joint distributions, research their properties and introduce independence of random variables. Then we'll discuss properties of expected value and variance with respect to arithmetic operations and introduce measures of independence between random variables.

**완료하는 데 3시간 필요**

**16개의 동영상**

**7개 연습문제**

**완료하는 데 3시간 필요**

## Continuous random variables

This week we'll study continuous random variables that constitute important data type in statistics and data analysis. For continuous random variables we'll define probability density function (PDF) and cumulative distribution function (CDF), see how they are linked and how sampling from random variable may be used to approximate its PDF. We'll introduce expected value, variance, covariance and correlation for continuous random variables and discuss their properties. Finally, we'll use Python to generate independent and correlated continuous random variables.

**완료하는 데 3시간 필요**

**16개의 동영상**

**8개 연습문제**

## Mathematics for Data Science 전문 분야 정보

## 자주 묻는 질문

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