About the Course
INF 659 Probability and Data Analysis provides a comprehensive introduction to probability theory and statistical methods with practical Python implementation. Students will master fundamental concepts including probability distributions, hypothesis testing, confidence intervals, regression analysis, and ANOVA. The course emphasizes both theoretical understanding and hands-on application using Python libraries such as NumPy, SciPy, pandas, and Matplotlib to analyze real-world datasets and draw meaningful conclusions.
Course Schedule
Course Materials
Available Lectures
| Lecture | Topic | Materials |
|---|---|---|
| -- | Course Introduction and Overview | Syllabus review, course expectations |
| 1 | Probability Theory I - Sample Spaces, Events, and Probability Axioms | Notebook Google Colab |
| 2 | Probability Theory II - Conditional Probability and Independence | Notebook Google Colab |
| 3 | Probability Theory III - Bayes' Theorem and Law of Total Probability | Notebook Google Colab |
| 4 | Random Variables I - Discrete Random Variables and PMF | Notebook Google Colab |
| 5 | Random Variables II - Continuous Random Variables and PDF | Notebook Google Colab |
| 6 | Discrete Distributions I - Bernoulli and Binomial Distributions | Notebook Google Colab |
| 7 | Discrete Distributions II - Poisson, Geometric, and Hypergeometric | Notebook Google Colab |
| 8 | Continuous Distributions I - Uniform and Exponential Distributions | Notebook Google Colab |
| 9 | Continuous Distributions II - Gamma, Beta, and Lognormal Distributions | Notebook Google Colab |
| 10 | Normal Distribution I - Properties and Standardization | Notebook Google Colab |
| 11 | Normal Distribution II - Related Distributions, CLT, and Normal Approximation | Notebook Google Colab |
| 12 | Midterm Review | Notebook Google Colab |
| -- | Midterm Exam | In-class exam (Covers L01-L11) |
| 13 | Descriptive Statistics I - Central Tendency | Notebook Google Colab |
| 14 | Descriptive Statistics II - Dispersion and Visualization | Notebook Google Colab |
| 15 | Sampling Distributions and Central Limit Theorem | Notebook Google Colab |
| 16 | Statistical Estimation and Confidence Intervals | Notebook Google Colab |
| -- | Guest Lecture - Industry/Research Perspective on Data Analysis | Special session |
| 17 | Hypothesis Testing I - Z-tests and t-tests | Notebook Google Colab |
| 18 | Hypothesis Testing II - p-values, Type I/II Errors, Power | Notebook Google Colab |
| 19 | Comparing Two Population Means - Two-Sample t-tests | Notebook Google Colab |
| 20 | Inference for Proportions and Chi-Square Tests | Notebook Google Colab |
| 21 | Analysis of Variance (ANOVA) | Notebook Google Colab |
| 22 | Correlation and Simple Linear Regression | Notebook Google Colab |
| 23 | Regression Inference and Prediction | Notebook Google Colab |
| 24 | Final Review | Notebook Google Colab |
| -- | Final Project Presentations | Presentation schedule TBA |
| -- | Final Exam | Comprehensive exam |
Assignments
Programming Assignments
| Assignment | Topic | Due Date | Materials |
|---|---|---|---|
| 1 | Probability and Bayes' Theorem - Medical Diagnosis Challenge | Sun, Feb 15 | Assignment Notebook Google Colab Submit |
| 2 | Random Variables and Discrete Distributions - Quality Control Analysis | Sun, Mar 1 | Assignment Notebook Google Colab Submit |
| 3 | Continuous and Normal Distributions - Financial Risk Analysis | Sun, Mar 22 | Assignment Notebook Google Colab Submit |
| 4 | Descriptive Statistics and Estimation - Sports Analytics | Sun, Apr 12 | Assignment Notebook Google Colab Submit |
| 5 | Statistical Inference and ANOVA - A/B Testing for Marketing | Sun, May 3 | Assignment Notebook Google Colab Submit |
| 6 | Regression and Advanced Analysis - Real Estate Price Prediction | Sun, May 3 | Assignment Notebook Google Colab Submit |
Textbook and References
Primary Textbook
Probability and Statistics for Engineers and Scientists
by A.J. Hayter, 4th Edition
Python Resources
SciPy and NumPy Documentation
Official documentation for statistical computing in Python