Statistics for Data Science
Log in to enrollThis course is a practical and beginner-friendly introduction to Statistics for Data Science. You will learn to use probability, statistics and hypothesis testing to solve real-world problems.
Lesson
Introduction to Probability
- Coin tosses, dice rolls and playing cards
- Intersection, union and independence
- Conditional probability and Bayes theorem
Lesson
Measures of Central Tendency
- Mean, standard deviation & variance
- Median, percentiles, quartiles & range
- Mode of a dataset & frequency tables
Assignment
Statistics & Probability Practice
- Simple and compound probability
- Mean and standard deviation
- Median, quartiles, and mode
Lesson
Counting Techniques & Random Variables
- Factorials, permutations & combinations
- Discrete and continuous random variables
- Probability distributions and expected values
Lesson
Hypothesis Testing and Statistical Significance
- Stating null and alternate hypothesis
- Computing Z scores and p values
- Significance and confidence levels
Assignment
Evaluating A/B Tests
- Introduction to A/B tests
- Computing the p-value
- Picking a winning variant
Lesson
Introduction to Product Analytics
- User journeys and the Pirate funnel
- Key metrics & tools to measure them
- Improving products using machine learning
Assignment 3
Business Case Studyoptional
- Understand the business problem
- Analyze the business problem
- Identify & propose a solution to the problem