Mathematics for Data Science

Data science combines statistics, mathematics, and computer science to extract insights from data. This chapter covers the essential mathematical foundations needed for modern data science, including statistical inference, hypothesis testing, and mathematical modeling.

Core Mathematical Areas

1. Statistics and Probability

  • Descriptive statistics and data summarization
  • Probability distributions and their properties
  • Statistical inference and hypothesis testing
  • Bayesian statistics and decision theory

2. Linear Algebra for Data Science

  • Matrix operations for data manipulation
  • Dimensionality reduction techniques
  • Principal Component Analysis (PCA)
  • Singular Value Decomposition (SVD)

3. Calculus and Optimization

  • Optimization for model fitting
  • Gradient-based methods
  • Constrained and unconstrained optimization
  • Maximum likelihood estimation

4. Information Theory

  • Entropy and information content
  • Mutual information for feature selection
  • Information-theoretic model selection
  • Compression and encoding

Chapter Contents

  1. Statistical Foundations
  2. Probability Distributions
  3. Hypothesis Testing
  4. Regression Analysis
  5. Dimensionality Reduction
  6. Time Series Analysis
  7. Experimental Design
  8. Bayesian Methods

Prerequisites

  • Basic calculus and linear algebra
  • Programming experience (Python/R recommended)
  • Understanding of basic statistics
  • Familiarity with data manipulation

Tools and Libraries

Python Ecosystem

  • NumPy: Numerical computing
  • Pandas: Data manipulation and analysis
  • SciPy: Scientific computing
  • Scikit-learn: Machine learning
  • Statsmodels: Statistical modeling
  • Matplotlib/Seaborn: Data visualization

R Ecosystem

  • Base R: Statistical computing
  • dplyr: Data manipulation
  • ggplot2: Data visualization
  • caret: Machine learning
  • tidyverse: Data science workflow

Key Concepts Overview

Descriptive vs Inferential Statistics

  • Descriptive: Summarize and describe data
  • Inferential: Make conclusions about populations from samples

Parametric vs Non-parametric Methods

  • Parametric: Assume specific probability distributions
  • Non-parametric: Make fewer distributional assumptions

Frequentist vs Bayesian Approaches

  • Frequentist: Probability as long-run frequency
  • Bayesian: Probability as degree of belief

Supervised vs Unsupervised Learning

  • Supervised: Learn from labeled examples
  • Unsupervised: Find patterns in unlabeled data