# Statistics for Data Science

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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 Study*optional*

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