Shahriar Talebi | Computer Programming with Python (under construction)

An introduction to computer programming and computational thinking with a focus on the Python 3 language. The course starts from first principles—variables, branching, loops—and advances to object-oriented design, algorithmic efficiency, and practical software engineering techniques such as testing and debugging. Key programming concepts include basic data types, control structures, input/output, functions, recursion, and object-oriented programming with an emphasis on Python-based data structures, writing efficient, modular code and testing/debugging techniques.

Examples and exercises are drawn from engineering, mathematics, and the physical sciences to ground abstract concepts in real-world problem solving. Instruction blends mini-lectures, live-coding sessions, and hands-on notebook exercises to ensure students leave each class with runnable code and a clear mental model of how it works.

Course Logistics

Structure & Workload:
Lecture · 2 h / week | Discussion · 2 h / week | Laboratory · 2 h / week | Outside study · ≈ 6 h / week
Prerequisite: Mathematics 33A
Grading: Letter
Class Time: TBA
Location: TBA (in person) | Zoom
Office Hours: TBA | Zoom
Q&A hosted on Piazza

Course Websites

Main site: shahriarta.github.io/teachings/computer_programming
Canvas (homework submissions)

Syllabus PDF

Download the full syllabus (PDF)

Main Texts & References

Other Texts & Broader references (Mostly Free Online)

Course Schedule (Tentative)
Date	Topic	Lab	Exercises (Think Python 3e)	Prerequisite	Lecture notes/slides*	Resources / Extra Reading
Lecture 1	Course logistics • Programming mindset Syntax, variables, types, operators, Basic I/O, code style	Variables & control-flow drill + mini-debug (pdb watch-list)	Ch 01 & 02	Math 33A		Guttag Ch 1; Python tutorial (official), PEP 8 style guide, Turing Machine
Lecture 2	Branching and conditionals, loops, ranges; Comparison/Boolean Operators; Data structures:lists, tuples;list comprehensions	Array exercises Loop-based problem solving (max, sum, reverse) off-by-one error debuging	Ch 06 (Iteration) + Ch 08 (Lists) §03.12 list-comprehension peek	Basic program structure		Guttag Ch 2-3; LearnPython .org interactive Real Python • Loops Python docs – lists & tuples
Lecture 3	Functions, scopes, args/*kwargs, iterables, map, lambda function	Modular design &	Ch 03 (Functions) & Ch 05 §5.1-5.4 (Fruitful funcs) Skip recursion parts until L6	Branching & loops		Guttag Ch 4 & 8; Real Python • Defining functions args & *kwargs explained Python docs – `__main__` pytest intro
Lecture 4	Recursion & problem decomposition • Sorting (built-in & custom) • Searching (linear / binary) Root-finding (bisection) • Big-O basics	Sort/search challenges + timing decorator (simple profiler) + Matplotlib multi-plot demo	Ch 05 §5.5-5.9 (Recursion) & Ch 13 §13.3-13.6 (Search & Selection)	Dictionaries & files		Guttag Ch 11, 14-15; Big-O Cheat Sheet Visual Algo • Search animations GfG sorting/search practice
Lecture 5	Dictionaries, sets, mutability vs hashability Hash-based problem solving (freq maps, memoisation)	Strings review; Anagrams & hashmap puzzles (with logging)	Ch 07 (Strings review) & Ch 09 (Dictionaries) Ch 10 §10.2-10.4 (Tuples vs lists)	Functions & testing		Guttag Ch 5 & 7; Python Tutor visualizer Real Python • Dictionaries Real Python • Sets
Lecture 6	File I/O (text, CSV, JSON); Script execution (`if __name__ == "__main__"`); Modules & packages Defensive programming: try/except, assertions, basic debugging	File parsing + input validation + Unit testing basics + TDD (pytest markers + assert patterns) Mini-lab: NumPy & Pandas warm-up (CSV → DataFrame) + Quick intro to Matplotlib: plot CSV columns	Ch 11 (Files & Exceptions) & Ch 12 (Modules)	Sequences overview		Guttag Ch 6, 8-9; Automate the Boring Stuff – Ch 10-11 Real Python • File I/O Matplotlib getting started NumPy quickstart Pandas I/O guide
Lecture 7	OOP I: Classes, constructors, encapsulation, `__str__`/`__repr__` Data Structures I: Stacks, queues, priority queues (`heapq`) Interview patterns: balanced parentheses, sliding-window max	Stack/queue/heap drills + class-based simulation (with doctest)	Ch 14 & 15 (Classes & Objects)	Recursion & complexity		Guttag Ch 10 & 13; heapq docs Real Python • Stacks & Queues LeetCode stack practice
Lecture 8	OOP II: Inheritance, polymorphism, abstract base classes Data Structures & Graph Algorithms: adjacency lists, DFS, BFS, shortest path, memoisation Dynamic Programming intro	Graph problems (maze reachability, shortest path) + inheritance refactor	Ch 17 (Inheritance) & Ch 18 §18.1-18.3 (Simple DP)	OOP concepts		Guttag Ch 12 & 15; NetworkX docs Khan Academy • Graph intro CP-Algorithms • Graph guide
Lecture 9	Testing & Debugging Deep-Dive: pytest fixtures, coverage, pdb, logging config, profiling Exception hierarchies, input validation patterns	End-to-end debugging workflow on prior homework	Appendix B (Debugging) & Ch 11 §11.4 (try/except)	Algorithm analysis		Guttag Ch 8-9; pdb tutorial (Real Python) Logging How-To pytest usage guide
Lecture 10	Capstone: Data analysis pipeline, advanced visualization, intro ML (scikit-learn glimpse) Project wrap-up & interview-style review	Pandas + NumPy project support, mock tech-interview round-robin	Ch 19 (Case study: Data analysis)	All prior material		Guttag Ch 23-26; Pandas intro tutorials scikit-learn basic tutorial Real Python • Data science pipeline

Part of the lecture materials are adapted from the cited references and will be available on the course website as we progress through the course.

Grading Policy

Weekly Problem Sets (7) — 30 %
Released each Friday, due the following Thursday at 11:59 pm ET--Canvas submission.
Exercises — 20 %
The completed and compiled Jupyter notebook (including the exercises)--Canvas submission. Due the midnight before each class (Wednesdays 11:59pm)
Check-in Quizes&Participation — 20 %
Quize is based on last lecture. Attendance, discussion, Piazza/Ed posts, peer-citation, and peer-code reviews.
Final (Individual) Capstone Project — 30 %
(1) Analysis, (2) simulation, and (3) code documentation will be due on date:TBA. Collaborations are highly encouraged based on "Collaboration Policy"--Canvas submission.

Letter-grade bands: A ≥ 93, A- ≥ 90, B+ ≥ 87, B ≥ 83, B- ≥ 80, C+ ≥ 77, C ≥ 73, C- ≥ 70, D ≥ 60, F < 60.

Late Policy: Late submissions are not accepted. If you are unable to submit on time due to a valid reason, please contact me at least 48 hours before the deadline to discuss possible accommodations.

Collaboration Policy: Discuss concepts freely, but all submitted code and write-ups must be your own. You must also cite precisely and in detail any external resources or generative-AI assistance used.