 # 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

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

### Measures of Central Tendency

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

### Statistics & Probability Practice

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

### Counting Techniques & Random Variables

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

### Hypothesis Testing and Statistical Significance

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

### Evaluating A/B Tests

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

### Introduction to Product Analytics

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