6.100A: Introduction to CS and Programming using Python

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6.100A is about:

• solving problems using computation
• the Python programming language
• organizing modular programs
• some simple but important algorithms
• algorithmic complexity

Spring 2023

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(previous semesters)

Lecture and reading notes

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• Lecture 1 (2023-02-06)
  • topics: what is computation, introduction to programming languages and Python, assignment, types, objects, branching, indentation
• Lecture 2 (2023-02-08)
  • topics: strings, booleans, branching and conditionals, iteration (
    while
    and
    for
    loops), guess and check algorithms
• Lecture 3 (2023-02-13)
  • topics: floats, approximate solutions, bisection methods
• Lecture 4 (2023-02-15)
  • topics: decomposition, abstraction, functions
• Lecture 5 (2023-02-21)
  • topics: lists, mutability, exceptions, assertions
• Lecture 6 (2023-02-22)
  • topics: dictionaries, HOPs (higher-order procedures), recursion
• Lecture 7 (2023-02-28)
  • topics: data abstraction, classes, subtyping
• Lecture 8 (2023-03-01)
  • topics: inheritance, class attributes
• Lecture 9 (2023-03-06)
  • topics: testing and debugging
• Lecture 10 (2023-03-08)
  • topics algorithm analysis, implementing lists using indirection
• Lecture 11 (2023-03-13)
  • topics: binary search, selection sort, merge sort, amortized analysis, hashing
• Lecture 12 (2023-03-15)
  • topics: plotting, wrap-up
• Lecture 13 (2023-10-17)
  • topics: review session

Recitation notes

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• Recitation 1 (2023-02-10)
  • lectures: 1, 2
  • slides, notebook
• Recitation 2 (2023-02-17)
  • lectures: 3, 4
  • slides, notebook, code
• Recitation 3 (2023-02-24)
  • lectures: 5, 6
  • slides, notebook, code
  • practice problem, solution
• Recitation 4 (2023-03-03)
  • lectures: 7, 8
  • slides, code
  • practice problem, solution
• Recitation 5 (2023-03-10)
  • lectures: 9, 10
  • slides, notebook, code
  • practice problem, solution
• Recitation 6 (2023-03-17)
  • lectures: 11, 12
  • slides, notebook, code

Final exam review

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• Practice problems
• Additional Python concept question practice (although the questions are a little too simple): Python Multiple Choice Questions

Supplementary materials

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• Some more advanced Python builtins ("batteries"). Python has a rich set of builtins that make programming much more enjoyable. This is an attempt to compile some of the best features.
• Scientific Visualization: Python + Matplotlib. Learn how to use
  matplotlib
  to build beautiful visualizations for your research.
• The missing semester of your CS education. An MIT IAP course taught by CSAIL PhD students on the practical parts of programming that is generally glossed over in other classes. Covers topics such as the shell ("terminal"), scripting, version control (
  git
  ), and more.
• Automate the Boring Stuff with Python. Learn how to use Python and its vast number of libraries to automate tedious aspects of your workflow.