2
6.00 Introduction to Computer Science and Programming (Fall 2008, MIT OCW). Instructors: Professor Eric Grimson and Professor John Guttag.
FREE
This course includes
Hours of videos
666 years, 7 months
Units & Quizzes
24
Unlimited Lifetime access
Access on mobile app
Certificate of Completion
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.
(from ocw.mit.edu)
Course Currilcum
- Lecture 01 – Introduction and Goals; Data Types, Operators, and Variables Unlimited
- Lecture 02 – Branching, Conditionals, and Iteration Unlimited
- Lecture 03 – Common Code Patterns: Iterative Programs Unlimited
- Lecture 04 – Abstraction through Functions; Introduction to Recursion Unlimited
- Lecture 05 – Floating Point Numbers, Successive Refinement, Finding Roots Unlimited
- Lecture 06 – Bisection Methods, Newton/Raphson, Introduction to Lists Unlimited
- Lecture 07 – Lists and Mutability, Dictionaries, Introduction to Efficiency Unlimited
- Lecture 08 – Complexity: Log, Linear, Quadratic, Exponential Algorithms Unlimited
- Lecture 09 – Binary Search, Bubble and Selection Sorts Unlimited
- Lecture 10 – Divide and Conquer Methods, Merge Sort, Exceptions Unlimited
- Lecture 11 – Testing and Debugging Unlimited
- Lecture 12 – Debugging, Knapsack Problem, Introduction to Dynamic Programming Unlimited
- Lecture 13 – Dynamic Programming: Overlapping Subproblems, Optimal Substructure Unlimited
- Lecture 14 – Introduction to Object-oriented Programming Unlimited
- Lecture 15 – Abstract Data Types, Classes and Methods Unlimited
- Lecture 16 – Encapsulation, Inheritance, Shadowing Unlimited
- Lecture 17 – Computational Models: Random Walk Simulation Unlimited
- Lecture 18 – Presenting Simulation Results, Pylab, Plotting Unlimited
- Lecture 19 – Biased Random Walks, Distributions Unlimited
- Lecture 20 – Monte Carlo Simulations, Estimating pi Unlimited
- Lecture 21 – Validating Simulation Results, Curve Fitting, Linear Regression Unlimited
- Lecture 22 – Normal, Uniform, and Exponential Distributions Unlimited
- Lecture 23 – Stock Market Simulation Unlimited
- Lecture 24 – Course Overview; What Do Computer Scientists Do? Unlimited
