**PadhAI-Foundations of Data Science Course**

This article serves as the index page for the articles corresponding to different modules of the Foundations of Data Science course.

- Foundations of Data Science
- Statistical and Algorithmic Modeling
- Why is Data Science so popular today?
- Are AI and Data Science related?
- Engineering data science systems
- CRISP-DM
- Statistics
- Statistics continued
- Descriptive Statistics — Different types of data
- How to describe Qualitative Data?
- Measures of centrality and spread
- Characteristics of measures of centrality and sensitivity of measures of centrality to outliers
- Measures of Centrality for different types of distributions
- Computing measures of centrality from a histogram
- Measures of Spread — Percentiles
- Alternative ways of computing percentiles
- Frequently used percentiles and effect of transformations on percentiles
- Measures of spread
- Pandas
- Data Visualisation
- Styling Tabulation
- Distribution of Data — Histogram
- Box Plot
- Distribution of a categorical variable — bar plot
- Joint distribution of two variables
- Swarm Plot
- The need for counting and probability theory
- Multiplication principle
- Subtraction Principle
- Collections
- Collections with repetitions
- The element of chance
- Set Theory
- Experiment and Sample Space
- Axioms, Properties of the Probability function
- Designing probability function as a relative frequency
- Designing probability functions for experiments for which all outcomes are equally likely
- Conditional Probability
- Chain rule of probability
- Total probability theorem
- Baye’s theorem
- Independent Events
- Random variable
- Probability mass function
- Discrete distributions
- Bernoulli distribution
- Binomial distribution
- Geometric Distribution
- Uniform distribution
- Expectation
- The function of a random variable
- The variance of a random variable
- Continuous Random variable
- Experimental studies

References: PadhAI