6.00SC - Introduction to Computer Science and Programming - MIT VIDEO LECTURES

6.00SC - Introduction to Computer Science and Programming - MIT VIDEO LECTURES
COURSE DESCRIPTION : 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.

TOTAL LECTURES - 26
Download Link (MIT Lecture Notes):
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Lecture Video Files	MPEG4	Ogg Video
Lecture 01: Goals of the course; what is computation; introduction to data types, operators, and variable	116.4 MB	212.6 MB
Lecture 02: Operators and operands; statements; branching, conditionals, and iteration	110.7 MB	195.1 MB
Lecture 03: Common code patterns: iterative programs	110.6 MB	200.5 MB
Lecture 04: Decomposition and abstraction through functions; introduction to recursion	112.5 MB	185.1 MB
Lecture 05: Floating point numbers, successive refinement, finding roots	96.3 MB	171.4 MB
Lecture 06: Bisection methods, Newton/Raphson, introduction to lists	109.5 MB	164.2 MB
Lecture 07: Lists and mutability, dictionaries, pseudocode, introduction to efficiency	101.0 MB	173.5 MB
Lecture 08: Complexity; log, linear, quadratic, exponential algorithms	109.0 MB	183.3 MB
Lecture 09: Binary search, bubble and selection sorts	103.3 MB	185.6 MB
Lecture 10: Divide and conquer methods, merge sort, exceptions	100.7 MB	175.8 MB
Lecture 11: Testing and debugging	106.7 MB	186.8 MB
Lecture 12: More about debugging, knapsack problem, introduction to dynamic programming	108.1 MB	201.8 MB
Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure	106.8 MB	173.9 MB
Lecture 14: Analysis of knapsack problem, introduction to object-oriented programming	109.9 MB	208.9 MB
Lecture 15: Abstract data types, classes and methods	110.3 MB	182.1 MB
Lecture 16: Encapsulation, inheritance, shadowing	110.1 MB	175.8 MB
Lecture 17: Computational models: random walk simulation	107.6 MB	185.5 MB
Lecture 18: Presenting simulation results, Pylab, plotting	115.6 MB	182.5 MB
Lecture 19: Biased random walks, distributions	108.8 MB	177.9 MB
Lecture 20: Monte Carlo simulations, estimating pi	104.8 MB	176.7 MB
Lecture 21: Validating simulation results, curve fitting, linear regression	116.9 MB	207.5 MB
Lecture 22: Normal, uniform, and exponential distributions; misuse of statistics	110.4 MB	195.3 MB
Lecture 23: Stock market simulation	111.8 MB	192.7 MB
Lecture 24: Course overview; what do computer scientists do?	94.6 MB	129.2 MB