Course Syllabus
INF 659 | Probability and Data Analysis
Spring 2026 | January - May 2026
Schedule: 2 lectures per week, 75 minutes each
Note: This syllabus is subject to change as needed to accommodate course requirements and student learning objectives. Any modifications will be announced in advance.
Instructor Information
Instructor: Ron (Rongyu) Lin
Email: rongyu.lin@quinnipiac.edu
Office Hours: (All times Eastern Time, held virtually via Zoom)
- Monday: 3:30 PM - 5:00 PM
- Tuesday: 3:00 PM - 5:00 PM
- Wednesday: 3:30 PM - 5:00 PM
How to Schedule:
- Book an appointment
- Join via Zoom (Meeting ID: 960 3493 7817)
Campus: Mount Carmel Campus
Classroom: CCE, Room 030
Schedule: Mondays & Wednesdays, 9:30 AM - 10:45 AM
Semester Dates: January 20, 2026 - May 8, 2026
Final Exams: May 4-8, 2026
Course Description
This course provides a comprehensive introduction to probability theory and statistical analysis 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.
Course Objectives
By the end of this course, students will be able to:
- Apply probability concepts including conditional probability, Bayes' theorem, and independence
- Work with discrete and continuous probability distributions
- Calculate and interpret descriptive statistics for data analysis
- Construct and interpret confidence intervals for population parameters
- Perform hypothesis tests and understand Type I/II errors and statistical power
- Apply Chi-square tests for categorical data analysis
- Conduct ANOVA for comparing multiple group means
- Perform correlation analysis and simple linear regression
- Implement statistical methods using Python (NumPy, SciPy, pandas, Matplotlib)
Textbooks/Materials
by A.J. Hayter, 4th Edition (Primary Textbook)
probability-statistics-notebook (GitHub repository)
Jupyter Notebook, NumPy, SciPy, pandas, Matplotlib, Seaborn
Course Policies
- Attendance and Participation: This course meets in regularly scheduled sessions each week. In-class activities and discussions count toward your grade. If you miss a class, email the instructor in advance to arrange make-up work.
- Late Work: Assignments are due before class starts on the specified due date. Late work will incur a 10% penalty for each day it is late without prior approval. Assignments submitted more than 5 days late will receive a maximum of 50% credit.
- Academic Integrity: Students are expected to maintain the highest standards of academic integrity. Cheating, plagiarism, and any form of academic dishonesty are strictly prohibited. Use of AI tools is permitted only when explicitly authorized.
- Accommodation: Students who require accommodation for a disability should contact the Office of Student Accessibility as soon as possible. Please provide your accommodation letter early in the semester.
Course Schedule Overview
Spring 2026 (January 20 - May 8, 2026)
| Week | Dates | Topics | Key Dates |
|---|---|---|---|
| 1 | Wed 1/21 | L1: Probability Theory I - Sample Spaces, Events, Probability Axioms | MLK Day 1/19 - No Mon class; A1 released (due 2/15) |
| 2 | Mon 1/26, Wed 1/28 | L2: Probability Theory II, L3: Probability Theory III - Conditional Probability, Bayes' Theorem | |
| 3 | Mon 2/2, Wed 2/4 | L4: Random Variables I, L5: Random Variables II - Discrete & Continuous RVs | A2 released (due 3/1) |
| 4 | Mon 2/9, Wed 2/11 | L6: Discrete Distributions I, L7: Discrete Distributions II - Binomial, Poisson | A1 due (2/15) |
| 5 | Mon 2/16, Wed 2/18 | L8: Continuous Distributions I, L9: Continuous Distributions II - Uniform, Exponential, Gamma | |
| 6 | Mon 2/23, Wed 2/25 | L10: Normal Distribution I, L11: Normal Distribution II - CLT, Normal Approximation | A2 due (3/1); A3 released (due 3/22) |
| 7 | Mon 3/2, Wed 3/4 | L12: Midterm Review, L13: Descriptive Statistics I - Central Tendency | |
| Spring Break (Mar 9-14) - No Classes | |||
| 8 | Mon 3/16, Wed 3/18 | Midterm Exam (Mon), L14: Descriptive Statistics II (Wed) | Midterm Exam; A4 released (due 4/12) |
| 9 | Mon 3/23, Wed 3/25 | L15: Sampling Distributions, L16: Statistical Estimation | A3 due (3/22) |
| 10 | Mon 3/30, Wed 4/1 | L17: Hypothesis Testing I, Guest Lecture (Wed) | |
| 11 | Mon 4/6, Wed 4/8 | L18: Hypothesis Testing II, L19: Two-Sample Tests | A4 due (4/12); A5 released (due 5/3) |
| 12 | Mon 4/13, Wed 4/15 | L20: Chi-Square Tests, L21: ANOVA | A6 released (due 5/3) |
| 13 | Mon 4/20, Wed 4/22 | L22: Regression I, L23: Regression II - Correlation, Linear Regression | |
| 14 | Mon 4/27, Wed 4/29 | L24: Final Review, Final Project Presentations | A5, A6 due (5/3) |
| 15 | May 4-8 | Finals Week | Final Exam |
Grading Breakdown
| Component | Weight | Description |
|---|---|---|
| Attendance & Participation | 10% | In-class activities, discussions, and engagement |
| Programming Assignments (6) | 40% | ~6.7% each, coding-based probability/statistics problems |
| Midterm Exam | 15% | Week 8 (after Spring Break), covers L01-L11 |
| Final Exam | 15% | Week 15, comprehensive with emphasis on L15-L24 |
| Final Project | 20% | Presentations in Week 14, complete data analysis project |
| Bonus Points (Optional) | Up to 3% | CS lectures/seminars attendance, course improvement suggestions, and other eligible activities (0.5% each, announced in advance) |