Probability for Data Science

Probability for Data Science, available at Free, has an average rating of 4.65, with 16 lectures, based on 21 reviews, and has 2954 subscribers.

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You will learn about 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. This course is ideal for individuals who are Individuals who are interested in pursuing a career in data science It is particularly useful for Individuals who are interested in pursuing a career in data science.

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Summary

Title: Probability for Data Science

Price: Free

Average Rating: 4.65

Number of Lectures: 16

Number of Published Lectures: 16

Number of Curriculum Items: 16

Number of Published Curriculum Objects: 16

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

Individuals who are interested in pursuing a career in data science

Target Audiences

Individuals who are interested in pursuing a career in data science

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

1. Be able to describe the basic probability concepts such as mean, variance, conditional probability, Bayes rule and statistical independence.

2. Be able to compute the mean and variance of random variables.

3. Be able to describe discrete and continuous distributions such as geometric, binomial and Poisson

4. Be able to understand how real-world phenomena can be modeled using probability distributions. 

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to probability (part 1)

Lecture 2: Introduction to probability (part 2)

Chapter 2: Discrete Random Variables

Lecture 1: Discrete Random Variables: An introduction

Lecture 2: Expectation of a Discrete Random Variable

Lecture 3: Variance of a Discrete Random Variable

Chapter 3: Expectation, Variance and Conditional Probabaility

Lecture 1: Properties of Expectation and Variance

Lecture 2: Conditional Probability

Lecture 3: Conditional Probability: Simple Example 1

Lecture 4: Bayes Rule

Lecture 5: Statistical Independence and Independent Random Variables

Lecture 6: Find Expectation and Variance: Simple Example

Chapter 4: Example of Discrete Random Variables (Bernoulli, Binomial Geometric, Poisson)

Lecture 1: Bernoulli and Binomial Random Variables

Lecture 2: Binomial Distribution: Simple Example

Lecture 3: Geometric Distribution

Lecture 4: Poisson Distribution

Lecture 5: Poisson Distribution: A Simple Example

Instructors

Anand Seetharam
Data Scientist, Researcher, Ex-professor, AI for good

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2 stars: 1 votes

3 stars: 3 votes

4 stars: 6 votes

5 stars: 11 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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