1: Introduction and Goals; Data Types, Operators, and Variables

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2: Branching, Conditionals, and Iteration

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3: Common Code Patterns: Iterative Programs

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4: Abstraction through Functions; Introduction to Recursion

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5: Floating Point Numbers, Successive Refinement, Finding Roots

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6: Bisection Methods, Newton/Raphson, Introduction to Lists

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7: Lists and Mutability, Dictionaries, Introduction to Efficiency

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8: Complexity: Log, Linear, Quadratic, Exponential Algorithms

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9: Binary Search, Bubble and Selection Sorts

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10: Divide and Conquer Methods, Merge Sort, Exceptions

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11: Testing and Debugging

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12: Debugging, Knapsack Problem, Introduction to Dynamic Programming

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13: Dynamic Programming: Overlapping Subproblems, Optimal Substructure

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14: Introduction to Object-oriented Programming

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15: Abstract Data Types, Classes and Methods

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16: Encapsulation, Inheritance, Shadowing

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17: Computational Models: Random Walk Simulation

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18: Presenting Simulation Results, Pylab, Plotting

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19: Biased Random Walks, Distributions

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20: Monte Carlo Simulations, Estimating pi

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21: Validating Simulation Results, Curve Fitting, Linear Regression

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22: Normal, Uniform, and Exponential Distributions

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23: Stock Market Simulation

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24: Course Overview; What Do Computer Scientists Do?

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