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Statistics for Data Science

Statistics for Data Science

 
 5
 4

This course is a practical and beginner-friendly introduction to Statistics for Data Science. By the end of this course, you will learn probability, distributions, hypothesis testing etc. to solve real-world problems.

Introduction to Probability

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  • Coin tosses, dice rolls and playing cards
  • Intersection, union and independence
  • Conditional probability and Bayes theorem

Measures of Central Tendency

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  • Mean, standard deviation & variance
  • Median, percentiles, quartiles & range
  • Mode of a dataset & frequency tables

Statistics & Probability Practice

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  • Simple and compound probability
  • Mean and standard deviation
  • Median, quartiles, and mode

Counting Techniques & Random Variables

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  • Factorials, permutations & combinations
  • Discrete and continuous random variables
  • Probability distributions and expected values

Hypothesis Testing and Statistical Significance

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  • Stating null and alternate hypothesis
  • Computing Z scores and p values
  • Significance and confidence levels

Evaluating A/B Tests

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  • Introduction to A/B tests
  • Computing the p-value
  • Picking a winning variant

Introduction to Product Analytics

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  • User journeys and the Pirate funnel
  • Key metrics & tools to measure them
  • Improving products using machine learning

Assignment 3 - Business Case Studyoptional

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  • Understand the business problem
  • Analyze the business problem
  • Identify & propose a solution to the problem