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Overview of Computer Science with Python

Sep 3, 2024

Lecture Notes: Introduction to Computer Science with Python (600L)

Introduction

  • Lecturer: Anna Bell (ECS Department, teaching for almost 10 years)
  • Course Overview: Full semester version of 6.100a
  • Focus: Course administration, thoughts about computers, Python basics

Course Structure

  • U-Try Breaks: Interactive coding opportunities during lectures.
  • Emphasis on practice and participation.
  • Aim: To develop programming skills, problem-solving abilities, and knowledge of concepts.

Learning Objectives

  1. Knowledge of Concepts: Learn computer science ideas.
  2. Programming Skill: Hands-on coding practice.
  3. Problem-Solving Skills: Apply computer science concepts to solve problems.

Topics Covered

  • Computational Thinking: Approach problems computationally.
  • Python Programming Language: Basics, structure, and organization of code.
  • Algorithms: Base algorithms for future studies.
  • Algorithmic Complexity: Efficiency of programs and memory usage.

Knowledge Types in Computer Science

  • Declarative Knowledge: Statement of facts.
  • Imperative Knowledge: Recipes or instructions.

Example: Finding Square Roots

  • Algorithm Steps: Sequence of steps to find the square root of a number, e.g., 16.
    1. Make an initial guess.
    2. Average guess and target number.
    3. Repeat until close enough.
  • Key takeaway: Algorithms are structured instructions for computers.

Understanding Computers

  • Computers execute algorithms: they are not inherently smart and only follow instructions.
  • Brief History: Shift from fixed program computers (1940s) to stored program computers.

Python Basics

  • Objects: Created and manipulated in programming. Each object has a type.
  • Types: Integer, Float, Bool, NoneType.
    • Example of scalar objects (basic types) and non-scalar (structured types).
  • Casting Types: Change type without altering the original object.

Expressions and Operators

  • Expressions evaluate to one value. Operator precedence is crucial.
    • Basic operators: +, -, *, /, // (floor division), % (modulus), ** (exponentiation).***

Variables and Assignments

  • Variables: Names for objects that store values.
  • Assignment: Binding a value to a variable; not the same as equality in mathematics.
  • Variable Naming Rules: Should be descriptive and meaningful.

Code Example

pi = 3.14159 radius = 2.2 area = pi * radius ** 2

Important Concepts

  • Line-by-line Execution: Code runs in sequence; reassigned values do not automatically update previous calculations.
  • Python Tutor: Useful tool for stepping through code to visualize variable states.

Summary

  1. Objects: Created and manipulated in Python with specific types.
  2. Expressions: Combine objects to evaluate to a single value.
  3. Variables: Named bindings to values allow for better readability.
  4. Assignment vs. Equality: Understanding the difference is crucial.
  5. Next lecture will introduce decision-making in programs.

Questions and Engagement

  • Encourage questions and participation throughout the course.
  • Participate in coding exercises and use discussion opportunities to clarify doubts.

Full transcript