Introduction to Computer Science and Programming

This is the original M.I.T. course 6.00 Introduction to Computer Science and Programming, as taught in Fall 2008 by Prof. Eric Grimson and Prof. John Guttag. This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role c... (read more)

This is the original M.I.T. course 6.00 Introduction to Computer Science and Programming, as taught in Fall 2008 by Prof. Eric Grimson and Prof. John Guttag. 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. Many of the problem sets focus on specific topics, such as virus population dynamics, word games, protein sequences, or simulating the movement of a Roomba. The class will use the Python™ programming language.
Technical Requirements
Special software is required to use some of the files in this course: Use the Python interpreter to run the .py files found on a course site.
Exams and Solutions
Assignments (no solutions)
(read less)

This is the original M.I.T. course 6.00 Introduction to Computer Science and Programming, as taught in Fall 2008 by Prof. Eric Grimson and Prof. John Guttag. This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role c... (read more)

Video Lectures

#	Lecture	Play Video
1	Goals, Introduction to the Course	Play Video
2	Operators, operands, statements, branching, iteration	Play Video
3	Common code patterns: iterative programs	Play Video
4	Decomposition, abstraction through functions and recursion	Play Video
5	Floating point numbers, finding roots	Play Video
6	Bisection methods, Newton/Raphson, introduction to lists	Play Video
7	Lists, mutability, dictionaries, pseudocode and efficiency	Play Video
8	Complexity; log, linear, quadratic, exponential algorithms	Play Video
9	Binary search, bubble and selection sorts	Play Video
10	Divide and conquer methods, merge sort, exceptions	Play Video
11	Testing and debugging	Play Video
12	Debugging, knapsack problem, dynamic programming	Play Video
13	Dynamic programming: overlapping subproblems, optimal substructure	Play Video
14	Knapsack problem and object-oriented programming	Play Video
15	Abstract data types, classes and methods	Play Video
16	Encapsulation, inheritance, shadowing	Play Video
17	Computational models: random walk simulation	Play Video
18	Presenting simulation results, Pylab, plotting	Play Video
19	Biased random walks, distributions	Play Video
20	Monte Carlo simulations, estimating pi	Play Video
21	Simulation results, curve fitting, linear regression	Play Video
22	Normal, uniform, and exponential distributions	Play Video
23	Stock market simulation	Play Video
24	Course overview; what do computer scientists do?	Play Video