Lecture 1: Goals of the course; what is computation; introduction to data types, operators, and variables

 

1. Goals, Introduction to the Course
2. Operators, operands, statements, branching, iteration
3. Common code patterns: iterative programs
4. Decomposition, abstraction through functions and recursion
5. Floating point numbers, finding roots
6. Bisection methods, Newton/Raphson, introduction to lists
7. Lists, mutability, dictionaries, pseudocode and 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. Debugging, knapsack problem, dynamic programming
13. Dynamic programming: overlapping subproblems, optimal substructure
14. Knapsack problem and 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. Simulation results, curve fitting, linear regression
22. Normal, uniform, and exponential distributions
23. Stock market simulation
24. Course overview; what do computer scientists do?

This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python™ programming language.