## 이 강좌에 대하여

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### 제공자: ## 석사 학위 취득 시작

This 강좌 is part of the 100% online Master of Data Science from 국립 연구 고등 경제 대학. If you are admitted to the full program, your courses count towards your degree learning.

1

완료하는 데 3시간 필요

## Conditional probability and Independence

완료하는 데 3시간 필요
13개 동영상 (총 123분), 1 개의 읽기 자료, 7 개의 테스트
13개의 동영상
Conditional probability. Motivation and Example13m
Conditional probability. Definition8m
Independent events. Example7m
Independent events. Definition12m
Mosaic Plot. Visualization of conditional probabilities and Independence11m
Using independence to find probabilities. Examples10m
Pairwise and mutual independence12m
Bernoulli Scheme11m
Law of total probability14m
Bayes's rule4m
Python for conditional probabilities9m
Conditional probability. Highlights3m
1개의 읽기 자료
Rules on the academic integrity in the course10m
7개 연습문제
Coins, dices and conditional probability20m
Independence and intersection5m
Fair coin and independence6m
Mutual independence conditions5m
Call center total probability5m
Bayes's taxi companies5m
2

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## Random variables

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15개 동영상 (총 150분)
15개의 동영상
Examples of random variables11m
Mathematical definition of random variable5m
Probability distribution and probability mass function (PMF)15m
Binomial distribution10m
Expected value of random variable. Motivation and definition14m
Expected value example and calculation11m
Expected value as best prediction15m
Variance of random variable. Motivation and definition7m
Discrete random variables with infinite number of values11m
Saint Petersburg Paradox. Example of infinite expected value6m
Geometric and Poisson distributions6m
Generating discrete random variables with Python11m
Numpy, scipy and matplotlib for generation and visualization of common distributions12m
Random variables. Highlights3m
3개 연습문제
Expected value exercises20m
Variance skill test25m
Random variables and geometric series10m
3

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

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16개 동영상 (총 127분)
16개의 동영상
Linear transformations of random variables8m
Linearity of expected value6m
Symmetric distributions and their expected values6m
Functions of random variables5m
Properties of variance6m
Sum of random variables. Expected value and variance8m
Joint probability distribution12m
Marginal distribution8m
Independent random variables7m
Another example of non-independent random variables8m
Expected value of product of independent random variables8m
Variance of sum of random variables. Covariance11m
Properties of covariance10m
Correlation of two random variables7m
Systems of random variables. Highlights3m
7개 연습문제
PMF of linear transformations5m
Expectation properties5m
Joint distribution skill test15m
Joint PMF10m
Variance of Binomial random variable5m
Covariance for a dice roll5m
Correlation quiz5m
4

완료하는 데 3시간 필요

## Continuous random variables

완료하는 데 3시간 필요
16개 동영상 (총 156분)
16개의 동영상
Continuous random variables. Motivation and Example10m
Probability density function (PDF)9m
Cumulative distribution function (CDF)13m
Properties of CDF6m
Examples of probability density functions10m
Histogram as approximation to a graph of PDF11m
Expected value of continuous random variable9m
Variance of continuous random variable. Properties of expected value and variance7m
Transformations of continuous random variables and their PDFs11m
Joint CDF and PDF. Level charts. Marginal PDF10m
Independence, covariance and correlation of continuous random variables9m
Mixed random variables. Example11m
Generating and visualizing continuous random variables with Python10m
Generating correlated random variables with Python11m
8개 연습문제
CDF of discrete random variable7m
PDF and CDF skill test15m
Finding expectation with PDF5m
Finding variance with PDF5m
Expectation of a function of random variable2m
PDF skill test7m
Variance of sum of Gaussian random variables5m
Distinguishing random variables3m

## Mathematics for Data Science 특화 과정 정보 ## 자주 묻는 질문

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