Introduction to Computer Science and Programming - ISBN:0000000000

Introduction to Computer Science and Programming

Date: 14 April 2011, 23:04

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

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