That said, they are not "computation appreciation" courses. Abstract data types, classes and methods Lecture Operators and operands; statements; branching, conditionals, and iteration Lecture 3: Decomposition and abstraction through functions; introduction to recursion Lecture 5: This course is the first of a two-course sequence: Analysis of knapsack problem, introduction to object-oriented programming Lecture Encapsulation, inheritance, shadowing Lecture Normal, uniform, and exponential distributions; misuse of statistics Lecture Even if you took the course with Python 2.
The goal is to provide students with a brief introduction to many topics so they will have an idea of what is possible when they need to think about how to use computation to accomplish some goal later in their career.
Course overview; what do computer scientists do?
Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. They are challenging and rigorous courses in which the students spend a lot of time and effort learning to bend the computer to their will.
This run features updated lecture videos, lecture exercises, and problem sets to use the new version of Python 3. See all courses Course Description This course is the first of a two-course sequence: Since these courses may be the only formal computer science courses many of the students take, we have chosen to focus on breadth Presenting simulation results, Pylab, plotting Lecture Validating simulation results, curve fitting, linear regression Lecture Floating point numbers, successive refinement, finding roots Lecture 6: Testing and debugging Lecture Thank you, MIT, for making your courses open like this.
Complexity; log, linear, quadratic, exponential algorithms Lecture 9: For more information, see the course website. Since these courses may be the only formal computer science courses many of the students take, we have chosen to focus on breadth rather than depth.
More about debugging, knapsack problem, introduction to dynamic programming Lecture Monte Carlo simulations, estimating pi Lecture To quote the overview given in the first video, the course revolves around: Binary search, bubble and selection sorts Lecture Biased random walks, distributions Lecture Stock market simulation Lecture Divide and conquer methods, merge sort, exceptions Lecture Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems.
Lists and mutability, dictionaries, pseudocode, introduction to efficiency Lecture 8: The course focuses on, quite simply, teaching you how to think like a programmer and solve problems computationally.
Goals of the course; what is computation; introduction to data types, operators, and variables Lecture 2:Computer Science (Computer Programs) OpenCourseWare: A Free Undergraduate Computer Programming Course by UC Berkeley Discover the fundamentals of computer programming in 'The Structure and Interpretation of Computer Programs,' a free OpenCourseWare project offered.
MIT OpenCourseWare: What course should I take after finishing Introduction to Computer Science and Programming?
Is MIT OCW good for self-teaching? Which course is better for a new programmer to learn programming: MIT's "Intro to Computer Science" or Stanford's "Programming Methodology"?
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An introduction to computer science as a tool to solve real-world analytical problems using Python Introduction to Computer Science and Programming Using Python Skip to main content.Download