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Channel: Introduction to Computer Science and Programming Online Course, year, MIT
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Lecture 24: Course overview; what do computer scientists do?

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Lecture 23: Stock market simulation

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

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

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Lecture 20: Monte Carlo simulations, estimating pi

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Lecture 19: Biased random walks, distributions

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Lecture 18: Presenting simulation results, Pylab, plotting

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Lecture 17: Computational models: random walk simulation

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Lecture 16: Encapsulation, inheritance, shadowing

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Lecture 15: Abstract data types, classes and methods

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Lecture 14: Analysis of knapsack problem, introduction to object-oriented...

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Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure

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Lecture 12: More about debugging, knapsack problem, introduction to dynamic...

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Lecture 11: Testing and debugging

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

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Lecture 9: Binary search, bubble and selection sorts

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Lecture 8: Complexity; log, linear, quadratic, exponential algorithms

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Lecture 7: Lists and mutability, dictionaries, pseudocode, introduction to...

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Lecture 6: Bisection methods, Newton/Raphson, introduction to lists

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Lecture 5: Floating point numbers, successive refinement, finding roots

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Lecture 4: Decomposition and abstraction through functions; introduction to...

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Lecture 3: Common code patterns: iterative programs

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Lecture 2: Operators and operands; statements; branching, conditionals, and...

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Lecture 1: Introduction

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