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  1. Goals of the course; what is computation; introduction to data types, operators, and variables
  2. Operators and operands; statements; branching, conditionals, and iteration
  3. Common code patterns: iterative programs
  4. Decomposition and abstraction through functions; introduction to recursion
  5. Floating point numbers, successive refinement, finding roots
  6. Bisection methods, Newton/Raphson, introduction to lists
  7. Lists and mutability, dictionaries, pseudocode, introduction to efficiency
  8. Complexity; log, linear, quadratic, exponential algorithms
  9. Binary search, bubble and selection sorts
  10. Divide and conquer methods, merge sort, exceptions
  11. Testing and debugging
  12. More about debugging, knapsack problem, introduction to dynamic programming
  13. Dynamic programming: overlapping subproblems, optimal substructure
  14. Analysis of knapsack problem, introduction to object-oriented programming
  15. Abstract data types, classes and methods
  16. Encapsulation, inheritance, shadowing
  17. Computational models: random walk simulation
  18. Presenting simulation results, Pylab, plotting
  19. Biased random walks, distributions
  20. Monte Carlo simulations, estimating pi
  21. Validating simulation results, curve fitting, linear regression
  22. Normal, uniform, and exponential distributions; misuse of statistics
  23. Stock market simulation
  24. Course overview; what do computer scientists do?

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Description

This is the Intro to Programming course offered by MIT. The course uses python (2.x) to baptize you in the ways of computer programming and teaches students both the python language and programming topics in general. All course materials--lecture videos, suggested readings, assignments--are brought to you via the MIT OpenCourseWare page for this class and are made available via the Creative Commons by-nc-sa license.

Prerequisites and Difficulty

This class is designed for people who are interested in programming but have little or no programming experience.

Recommended Pace

Please proceed through the class at your own pace and disregard the dates posted below the course title. The recommended pace is one or two lessons a week.

Materials and Technical Requirements

Videos and readings are hosted online but can also be downloaded.

MIT Course Quicklinks

• Course Homepage
• Class calendar. NB: assignments will be due the crunchcourse lecture preceding the lecture indicated on the MIT calendar.
• Lectures. Sometimes it can be difficult to read the code from the video; you are encouraged to download the accompanying handouts to follow along.
• Assignments

Python resources

• Python's Getting Started Page
• Python Documentation
• Python Download Page

Associated Texts

• How To Think Like A Computer Progammer (Python) - Online - Download (pdf)
• Python Programming - Online
• The Python Tutorial - Online
• Code Like A Pythonista: Idiomatic Python - Online - Download (txt)
• Dive Into Python - Online

How Curious Reef Works

The purpose of the class is to learn collaboratively with others.
The course creator has organized the course information for everyone.

• Complete one lesson and its assignments per week
• Submit and self-grade your own work
• Help each other in the class forum

License