Course Syllabus
INF 605 | Introduction to Programming — Python
Fall 2026 | August 24 – December 12, 2026
INF60502: Tuesdays & Thursdays, 11:00 AM – 12:15 PM, Tator Hall 130
INF605DA: online / web
This syllabus is subject to change.
Instructor Information
Instructor: Prof. Ron (Rongyu) Lin
Email: rongyu.lin@quinnipiac.edu · rongyu.lin@qu.edu
Office: CCE 233, 275 Mt. Carmel Ave., Hamden, CT 06518
Office hours (shared across CSC 150, CSC 352/552, and INF 605):
- Tuesday 2:00–3:00 PM, CCE 233 and Zoom
- Wednesday 10:00–11:00 AM, CCE 233 and Zoom
- Thursday 1:00–2:00 PM, CCE 233 and Zoom
- Friday 10:00–11:00 AM, Zoom only
To meet during office hours, book an appointment, then join Zoom (Meeting ID: 960 3493 7817) or come to CCE 233.
Term: Monday, August 24, 2026 – Saturday, December 12, 2026
On-campus instruction ends: Saturday, December 5, 2026
Finals window: December 7–12, 2026
Census: Monday, September 14, 2026 | Add/Drop ends: Friday, August 28, 2026
Thanksgiving recess: November 23–28, 2026 (no Tuesday or Thursday class that week)
Course Description
This course develops computational thinking while using Python as a tool to answer real-world questions with data. Students will gain experience exploring messy datasets, cleaning and preparing data, writing programs to automate analysis, and crafting compelling visualizations that communicate insights to both technical and non-technical audiences. The class emphasizes iterative design, statistical thinking, and the ethical implications of computing.
Learning Objectives
By the end of this course, students will be able to:
- Write Python programs using variables, control structures, functions, and object-oriented concepts
- Manipulate and analyze data using NumPy arrays and pandas DataFrames
- Clean, transform, and prepare messy real-world datasets for analysis
- Perform exploratory data analysis using descriptive statistics and data aggregation
- Create effective data visualizations using Matplotlib and Seaborn
- Work with various file formats (CSV, JSON, Excel) and databases
- Apply best practices for reproducible data analysis and code documentation
Required Materials
Paul and Harvey Deitel, Pearson.
Wes McKinney.
Installed as instructed in class.
Section Delivery
INF60502 meets in person Tuesday and Thursday, 11:00–12:15, in Tator Hall 130.
INF605DA is the online/web section. DA students use recorded lectures, written instructions, and Canvas Inbox.
Grading and Assessment
Each item below is 10% of the course grade. Each assignment covers lectures that have already been taught. Due dates are posted in Canvas.
| Item | Weight |
|---|---|
| Assignment 1 | 10% |
| Assignment 2 | 10% |
| Assignment 3 | 10% |
| Assignment 4 | 10% |
| Assignment 5 | 10% |
| Assignment 6 | 10% |
| Final project | 10% |
| Midterm exam (take-home) | 10% |
| Final exam (take-home) | 10% |
| Attendance and participation | 10% |
| Course total | 100% |
Course Policies
- Late work: Assignments are due Sunday of the due week, 11:59 PM ET, unless Canvas says otherwise. Late work loses 10% per day for the first 5 days. After 5 days the maximum score is 50%.
- Attendance & participation: INF60502 meets in person in Tator Hall 130; consistent presence is essential. INF605DA participation is through recordings, written instructions, and Canvas Inbox. In-class or online activities count toward the grade. If you miss a class, email the instructor in advance to arrange make-up work.
- Academic integrity: Students are expected to maintain high standards of academic integrity. Cheating, plagiarism, and other forms of academic dishonesty, including unauthorized use of ChatGPT or other AI tools, are prohibited. Use of AI tools is permitted only when explicitly authorized for that assignment.
Course Modules
Six modules. Instruction follows the Tuesday/Thursday calendar for INF60502. INF605DA covers the same modules through recordings and written instructions. Module 3 has no assignment of its own; the midterm exam covers it. Thanksgiving recess (Week 14) falls during Module 6.
| Range | Module | Assessed in |
|---|---|---|
| Weeks 1–2 | Module 1: Python fundamentals. Course introduction, Python and Jupyter setup, variables, and basic types. | A1 |
| Weeks 3–4 | Module 2: Control flow and functions. Conditionals, loops, and functions. | A2 |
| Weeks 5–7 | Module 3: Data structures and OOP. Collections, classes, and related programming practice. | Midterm |
| Weeks 8–10 | Module 4: Files and NumPy. File processing, array computing, and the midterm examination window. | A3 |
| Weeks 11–12 | Module 5: pandas data analysis. Series and DataFrames, data cleaning, and aggregation. | A4, A5 |
| Weeks 13–15 | Module 6: Data visualization. Matplotlib and Seaborn, then the final project. Week 14 is Thanksgiving recess (no class). | A5, A6 |
| Final examinations | Final examination and project completion as posted in Canvas. | Final exam, project |