Lecture 10: Divide and conquer methods, merge sort, exceptions

Lecture 03: Common code patterns: iterative programs

Lecture 23: Stock market simulation

Lecture 24: Course overview; what do computer scientists do?

Lecture 22: Normal, uniform, and exponential distributions; misuse of statistics

Lecture 21: Validating simulation results, curve fitting, linear regression

Lecture 19: Biased random walks, distributions

Lecture 20: Monte Carlo simulations, estimating pi

Lecture 18: Presenting simulation results, Pylab, plotting

Lecture 17: Computational models: random walk simulation

Introduction to Computer Science and Programming

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Lecture 16: Encapsulation, inheritance, shadowing

Lecture 15: Abstract data types, classes and methods

Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure

Lecture 09: Binary search, bubble and selection sorts

Lecture 08: Complexity; log, linear, quadratic, exponential algorithms

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