edX MIT 6.00.1x Syllabus 2020 Lectures, Finger Exercises, Problem Sets, Grades, Midterm, Final Exams
edX MIT 6.00.1x Syllabus 2020 - Introduction to Computer Science and Programming Using Python - Curriculum
Syllabus, List of Lecture Topics, Subtopics to be covered in the course.
Lectures, Finger Exercises, Problem Sets, Assignments, Exams, Grades, Grading Policy, Exercises, Midterm and Final Exams
Deadlines and Due Dates
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
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
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
Lecture 7 – Debugging:
• Programming Challenges
• Classes of Tests
• Bugs
• Debugging
• Debugging Examples
Lecture 8 – Assertions and Exceptions
• Assertions
• Exceptions
• Exception Examples
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
• Visualizing the Hierarchy
• Adding another Class
• Using Inherited Methods
• Gradebook Example
• Generators
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
Lecture 13 – Visualization of Data:
• Visualizing Results
• Overlapping Displays
• Adding More Documentation
• Changing Data Display
• An Example
Lecture 14 – Summary
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