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 |
| 0 | Python Prerequisites | Notebook Google Colab |
| 1 | Probability Theory I - Foundations | 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 | Discrete Random Variables | Notebook Google Colab |
| 5 | Continuous Random Variables, Variance, and Joint Distributions | Notebook Google Colab |
| 6 | The Binomial Distribution | Notebook Google Colab |
| 7 | Poisson and Other Discrete Distributions | 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 | The Normal Distribution I | 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 - Measures of Central Tendency | Notebook Google Colab |
| 14 | Descriptive Statistics II - Measures of Dispersion | Notebook Google Colab |
| 15 | Sampling Distributions and the 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, Errors, and Power | Notebook Google Colab |
| 19 | Comparing Two Population Means | Notebook Google Colab |
| 20 | Inference for Proportions and Chi-Square Tests for Categorical Data | Notebook Google Colab |
| 21 | Analysis of Variance (ANOVA) | Notebook Google Colab |
| 22 | Correlation and Simple Linear Regression I | 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