MIT OpenCourseWare 6.00 Introduction to Computer Science and Programming

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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?

Lesson 7: Lists and mutability, dictionaries, pseudocode, introduction to efficiency

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