Introduction to Computer Science and Programming

Course Description

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.

Lectures

1. favorites

   Lecture 1 - Introduction and Goals of the Course

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

2. favorites

   Lecture 2 - Operators and operands

   Operators and operands; statements; branching, conditionals, and iteration

3. favorites

   Lecture 3 - Common code patterns

   Common code patterns: iterative programs

4. favorites

   Lecture 4 - Decomposition and abstraction through functions

   Decomposition and abstraction through functions; introduction to recursion

5. favorites

   Lecture 5 - Floating point numbers

   Floating point numbers, successive refinement, finding roots

6. favorites

   Lecture 6 - Bisection methods

   Bisection methods, Newton/Raphson, introduction to lists

7. favorites

   Lecture 7 - Lists and mutability

   Lists and mutability, dictionaries, pseudocode, introduction to efficiency

8. favorites

   Lecture 8 - Complexity

   Complexity; log, linear, quadratic, exponential algorithms

9. favorites

   Lecture 9 - Binary search

   Binary search, bubble and selection sorts

10. favorites

    Lecture 10 - Divide and conquer methods

    Divide and conquer methods, merge sort, exceptions

11. favorites

    Lecture 11 - Testing and debugging

    Testing and debugging

12. favorites

    Lecture 12 - Knapsack problem

    More about debugging, knapsack problem, introduction to dynamic programming

13. favorites

    Lecture 13 - Dynamic programming

    Dynamic programming: overlapping subproblems, optimal substructure

14. favorites

    Lecture 14 - Introduction to object-oriented programming

    Analysis of knapsack problem, introduction to object-oriented programming

15. favorites

    Lecture 15 - Abstract data types

    Abstract data types, classes and methods

16. favorites

    Lecture 16 - Encapsulation

    Encapsulation, inheritance, shadowing

17. favorites

    Lecture 17 - Computational models

    Computational models: random walk simulation

18. favorites

    Lecture 18 - Presenting simulation results

    Presenting simulation results, Pylab, plotting

19. favorites

    Lecture 19 - Biased random walks

    Biased random walks, distributions

20. favorites

    Lecture 20 - Monte Carlo simulations

    Monte Carlo simulations, estimating pi

21. favorites

    Lecture 21 - Validating simulation results

    Validating simulation results, curve fitting, linear regression

22. favorites

    Lecture 22 - Normal, uniform, and exponential distributions

    Normal, uniform, and exponential distributions; misuse of statistics

23. favorites

    Lecture 23 - Stock market simulation

    Stock market simulation

24. favorites

    Lecture 24 - Course overview: What do computer scientists do?

    Course overview; what do computer scientists do?