Probability for Data Science

Name: Probability for Data Science
Rating: 4.4 (35 reviews)

Covers the probability concepts essential for data science

Created byAnand Seetharam

Last updated 1/2023

English

What you'll learn

Basic probability concepts such as mean and variance
Compute the mean and variance of random variables.
Conditional probability
Bayes rule and statistical independence.
Discrete distributions such as geometric, binomial, Poisson.

Course content

4 sections • 16 lectures • 1h 55m total length

Introduction to probability (part 1)9:48
In this video, I provide an introduction to probability by discussing experiments, events and sample spaces.
Introduction to probability (part 2)15:08
In this video, I discuss the basics of set theory and use it to describe the axioms of probability.

Discrete Random Variables: An introduction7:44
In this video, I discuss discrete random variables, probability mass functions and cumulative distribution functions.
Expectation of a Discrete Random Variable4:02
In this video, we discuss how to determine the expectation of a discrete random variable using a simple example.
Variance of a Discrete Random Variable6:50
In this video, we explain variance of a discrete random variable and show how to compute it.

Properties of Expectation and Variance9:14
In this video, we discuss some of the basic properties of expectation and variance of random variables. We demonstrate from first principles how to calculate the expectation of a sum of random variable. The video also teaches how to determine the variance of a random variable which is a function of another random variable.
Conditional Probability6:26
In this video, we describe the concept of conditional probability using a simple example. We also discuss the basic axioms of conditional probability.
Conditional Probability: Simple Example 13:33
In the video, I work out a simple example to illustrate the concept of conditional probability.
Bayes Rule7:49
In this video, we explain the concept of Bayes Rule using a simple example.
Statistical Independence and Independent Random Variables6:20
In this video, we discuss statistical independence and independent random variables and illustrate this concept using a simple example.
Find Expectation and Variance: Simple Example7:38
In this video, I explain how to find expectation and variance of a sum of two independent random variables from first principles using a step by step approach.

Bernoulli and Binomial Random Variables9:05
In this video, I discuss Bernoulli and Binomial random variables. We will study an example balls and bins problem that can be modeled using a binomial random variable.
Binomial Distribution: Simple Example3:22
In this video, we look at a simple example to understand the application of Binomial random variables.
Geometric Distribution8:53
In this video, we discuss geometric random variables and geometric distribution. The video also covers the important memoryless property of the geometric distribution.
Poisson Distribution8:04
In this video, I provide an overview of Poisson distribution. The video also show the relationship between binomial and Poisson distributions.
Poisson Distribution: A Simple Example2:03

Requirements

Learner should have finished high school math, preferably calculus I

Description

A strong understanding of probability is critical for becoming a successful data scientist. Probability is a key mathematical concept that is essential for modeling and understanding computer system performance and real-world data generated from day-to- day activities and interactions. In particular areas such as data science, machine learning, natural language processing and computer vision rely heavily on probabilistic models.

This short course in probability is designed to provide the necessary background for learning and understanding machine learning and data science concepts. Specifically, the course will introduce the concept of probability, provide an overview of discrete random variables and describe how to compute expectation and variance. The course will also discuss specific distributions such as geometric, binomial and Poisson distributions. The course includes multiple worked-out examples so that students can appreciate how to apply the concepts learnt in the lectures.

At the end of the course, students will

Be able to describe the basic probability concepts such as mean, variance, conditional probability, Bayes rule and statistical independence.
Be able to compute the mean and variance of random variables.
Be able to describe discrete and continuous distributions such as geometric, binomial and Poisson
Be able to understand how real-world phenomena can be modeled using probability distributions.

Who this course is for:

Individuals who are interested in pursuing a career in data science

Probability for Data Science

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 25min

Discrete Random Variables3 lectures • 19min

Expectation, Variance and Conditional Probabaility6 lectures • 41min

Example of Discrete Random Variables (Bernoulli, Binomial Geometric, Poisson)5 lectures • 31min

Requirements

Description

Who this course is for: