Introduction-Operators and operands; statements; branching, conditionals, and iteration-Common code patterns: iterative programs – composition and abstraction through functions; introduction to recursion – Floating point numbers, successive refinement, finding roots, Bisection methods, Newton/Raphson, introduction to lists – Lists and mutability, dictionaries, pseudocode, introduction to efficiency – Complexity; log, linear, quadratic, exponential algorithms – Binary search, bubble and selection sorts – Divide and conquer methods, merge sort, exceptions – Testing and debugging -More about debugging, knapsack problem, introduction to dynamic programming – Dynamic programming: overlapping sub problems, optimal substructure – Analysis of knapsack problem, introduction to object – oriented programming-Abstract data types, classes and methods – Encapsulation, inheritance, shadowing

Computational models: random walk simulation – Presenting simulation results, Pylab, plotting – Biased random walks, distributions – Monte Carlo simulations, estimating pi – Validating simulation results, curve fitting, linear regression – normal, uniform, and exponential distributions; misuse of statistics – Stock market simulation – Course overview; what do computer scientists do?

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Course Curriculum

Introduction Details 53:30
Operators and operands; statements; branching, conditionals, and iteration Details 50:49
Common code patterns: iterative programs Details 0:51
Decomposition and abstraction through functions; introduction to recursion Details 51:27
Floating point numbers, successive refinement, finding roots Details 44:13
Bisection methods, Newton/Raphson, introduction to lists Details 50:11
Lists and mutability, dictionaries, pseudocode, introduction to efficiency Details 46:22
Complexity; log, linear, quadratic, exponential algorithms Details 50:3
Binary search, bubble and selection sorts Details 47:30
Divide and conquer methods, merge sort, exceptions Details 46:19
Testing and debugging Details 48:59
More about debugging, knapsack problem, introduction to dynamic programming Details 49:47
Dynamic programming: overlapping subproblems, optimal substructure Details 48:56
Analysis of knapsack problem, introduction to object-oriented programming Details 50:34
Abstract data types, classes and methods Details 50:25
Encapsulation, inheritance, shadowing Details 50:23
Computational models: random walk simulation Details 49:23
Presenting simulation results, Pylab, plotting Details 52:54
Biased random walks, distributions Details 49:53
Monte Carlo simulations, estimating pi Details 47:55
Validating simulation results, curve fitting, linear regression Details 53:48
Normal, uniform, and exponential distributions; misuse of statistics Details 50:49
Stock market simulation Details 51:10
Course overview; what do computer scientists do? Details 42:48

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