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MITx: 6.00.1x

"Introduction to Computer Science and Programming Using Python," is 3 credits course from MITx on the edX platform. In this course provide basic to hard excercise and problem sets. So, the user get gradually understanding in python.

Couse Syllabus:

Lecture 1 – Introduction to Python: Knowledge Machines Languages Types Variables Operators and Branching

Lecture 2 – Core elements of programs:

Bindings Strings Input/Output IDEs Control Flow Iteration Guess and Check

Week 2 Lecture 3 – Simple Programs: Approximate Solutions Bisection Search Floats and Fractions Newton-Raphson Lecture 4 – Functions: Decomposition and Abstraction Functions and Scope Keyword Arguments Specifications Iteration vs Recursion Inductive Reasoning Towers of Hanoi Fibonacci Recursion on non-numerics Files

Week 3 Lecture 5 – Tuples and Lists: Tuples Lists List Operations Mutation, Aliasing, Cloning Lecture 6 – Dictionaries: Functions as Objects Dictionaries Example with a Dictionary Fibonacci and Dictionaries Global Variables

MidTerm Exam ((8 hours time limits))

Week 4 Lecture 7 – Debugging: Programming Challenges Classes of Tests Bugs Debugging Debugging Examples Lecture 8 – Assertions and Exceptions Assertions Exceptions Exception Examples

Week 5 Lecture 9 – Classes and Inheritance: Object Oriented Programming Class Instances Methods Classes Examples Why OOP Hierarchies Your Own Types Lecture 10 – An Extended Example: Building a Class Viualizing the Hierarchy Adding another Class Using Inherited Methods Gradebook Example Generators

Week 6 Lecture 11 – Computational Complexity: Program Efficiency Big Oh Notation Complexity Classes Analyzing Complexity Lecture 12 – Searching and Sorting Algorithms: Indirection Linear Search Bisection Search Bogo and Bubble Sort Selection Sort Merge Sort

Week 7 Lecture 13 – Visualization of Data: Visualizing Results Overlapping Displays Adding More Documentation Changing Data Display An Example Lecture 14 – Summary

Final Exam (8 hours time limits)

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An introduction to computer science as a tool to solve real-world analytical problems using Python 3.5.

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