MCS 275: Programming tools and file management

Emily Dumas

University of Illinois at Chicago
Spring 2021

About the course

This course is a second semester of study in introductory computer science, designed for students who have completed CS 107, CS 109, CS 111, or MCS 260.

In this course we focus on learning the Python programming language more deeply, including the use of popular add-on modules for API development, databases, and data science. We also cover some fundamental data structures and algorithms and their implementation in Python.

This is the public archival site for course materials from Spring 2021. The course site was originally located in UIC's Blackboard Learn LMS.

Policies

• Course Syllabus

Staff

• Instructor: Emily Dumas
• Teaching assistant: Jennifer Vaccaro

Texts

• Primary:
  • Learning Python, 5th Edition, by Mark Lutz
  • Python Cookbook, 3rd Edition, by David Beazley and Brian K. Jones
• Secondary:
  • Python Data Science Handbook by Jake VanderPlas
  • Problem Solving with Algorithms and Data Structures using Python Python, by Brad Miller and David Ranum
  • Think Python, 2ed, by Allen B. Downey

Example programs

Many MCS 275 lectures involved constructing programs or iPython notebooks from scratch to demonstrate course material.

These can be found in the course GitHub repository.

Slides

• Lecture 1 - Introduction (HTML)
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• Lecture 2 - Python tour part I (HTML)
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• Lecture 3 - Python tour part II (HTML)
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• Lecture 4 - Special Methods and Overloading (HTML)
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• Lecture 5 - Inheritance (HTML)
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• Lecture 6 - Inheritance II (HTML)
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• Lecture 7 - Notebooks; variadic functions (HTML)
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• Lecture 8 - Decorators (HTML)
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• Lecture 9 - Context Managers (HTML)
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• Lecture 10 - Errors and debugging (HTML)
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• Lecture 11 - pdb (HTML)
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• Lecture 12 - Recursion (HTML)
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• Lecture 13 - Recursion vs iteration (HTML)
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• Lecture 14 - Recursion vs iteration II (HTML)
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• Lecture 15 - Recursion with backtracking (HTML)
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• Lecture 16 - Mergesort (HTML)
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• Lecture 17 - Quicksort (HTML)
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• Lecture 18 - Comparison sorts (HTML)
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• Lecture 19 - Trees (HTML)
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• Lecture 20 - BST (HTML)
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• Lecture 21 - BST and tree traversals (HTML)
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• Lecture 22 - set and defaultdict (HTML)
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• Lecture 23 - CSV and JSON (HTML)
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• Lecture 24 - Pillow (HTML)
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• Lecture 25 - Numpy (HTML)
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• Lecture 26 - Numpy II (HTML)
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• Lecture 27 - Julia sets (HTML)
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• Lecture 28 - Matplotlib (HTML)
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• Lecture 29 - Matplotlib II (HTML)
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• Lecture 30 - Databases (HTML)
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• Lecture 31 - SQL and SQLite (HTML)
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• Lecture 32 - SQL and SQLite II (HTML)
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• Lecture 33 - HTML and CSS (HTML)
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• Lecture 34 - Planning our web apps (HTML)
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• Lecture 35 - HTTP and Flask (HTML)
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• Lecture 36 - Using Flask (HTML)
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• Lecture 37 - Forms (HTML)
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• Lecture 38 - Web app wrap-up (HTML)
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• Lecture 39 - HTTP Requests (HTML)
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• Lecture 40 - Parsing and scraping HTML (HTML)
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• Lecture 41 - Beautiful Soup (HTML)
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• Lecture 42 - argparse (HTML)
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• Lecture 43 - pandas (HTML)
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• Lecture 44 - Generators (HTML)
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Worksheets

• Week 1: Problems – Solutions
• Week 2: Problems – Solutions
• Week 3: Problems – Solutions
• Week 4: Problems – Solutions
• Week 5: Problems – Solutions
• Week 6: Problems – Solutions
• Week 7: Problems – Solutions
• Week 8: Problems – Solutions
• Week 9: Problems – Solutions
• Week 10: Problems – Solutions
• Week 11: Problems – Solutions
• Week 12: Problems – Solutions
• Week 13: Problems – Solutions
• Week 14: Problems – Solutions
• Week 15: Problems – Solutions

See also: worksheets in course GitHub repository.

Quizzes

• Quiz 1: Problems – Solutions
• Quiz 2: Problems – Solutions
• Quiz 3: Problems – Solutions
• Quiz 4: Problems – Solutions
• Quiz 5: Problems – Solutions
• Quiz 6: Problems – Solutions
• Quiz 7: Problems – Solutions
• Quiz 8: Problems – Solutions
• Quiz 9: Problems – Solutions
• Quiz 10: Problems – Solutions
• Quiz 11: Problems – Solutions
• Quiz 12: Problems – Solutions
• Quiz 13: Problems – Solutions
• Quiz 14: Problems – Solutions

See also: quizzes in course GitHub repository.

Projects

• Project 1: description – starter pack
• Project 1 Solution: tasks.py
• Project 2: description
• Project 2 Solution: eggs.py
• Project 3: description – starter pack
• Project 3 Solution: proj3solution.zip
• Project 4: description

See also: projects in course GitHub repository.

Up: Home page of Emily Dumas