Introduction to Computer Science and Programming (600)

Lecture Overview

• Instructors: Eric Grimson and John Guttag
• Course Focus: Introduction to programming and computational thinking
• Goals: Help students learn how to think like computer scientists

Course Goals

Strategic Goals

1. Entry Preparation: Support freshmen and sophomores interested in Course 6 without prior programming experience.
2. Confidence Building: Equip non-majors with the ability to read and write small pieces of code.
3. Understanding Computation: Teach students the role of computation in solving technical problems.
4. Competitiveness: Prepare all students for opportunities like UROPs and summer jobs.

Tactical Goals (Skills to Acquire)

1. Basic Tools: Use computational thinking to write small-scale programs.
2. Vocabulary: Understand programs written by others.
3. Capabilities and Limitations: Comprehend what can and cannot be computed.
4. Mapping Problems: Translate scientific problems into a computational framework.

Course Structure

• Format: Two hours of lecture per week, one hour of recitation, and nine hours of outside work.
• Programming Language: Python will be used for programming assignments.
• Assignments: Problem sets will increase in complexity over the term. The initial problem sets will be easier to help students ramp up.
• Quizzes: Two quizzes scheduled for October 2nd and November 4th, plus a final exam; open book/notes format.
• Grading: 55% from problem sets, 45% from quizzes and the final exam.

Administrative Notes

• First Problem Set: Problem set zero is posted to help install Python.
• Recitation Registration: Sign up for recitations available on Fridays.
• Textbook: No required textbook; a suggested one is available.
• Attendance: Not mandatory, but material outside of readings will be covered in lectures.
• Class Notes: No class notes will be provided; students are encouraged to take their own notes.
• Support: Instructors and TAs are available for assistance, and it's important to seek help when needed.

Conceptual Framework

What is Computation?

• Primary Knowledge: Understanding computational problem solving and the tools required.
• Types of Knowledge:
  • Declarative Knowledge: Statements of fact (e.g., definitions).
  • Imperative Knowledge: Descriptions of how to perform a task or process (e.g., recipes for algorithms).

Historical Context

• Early Computers: Fixed program computers (specific tasks like calculators).
• Stored Program Computers: The evolution to machines that can execute any sequence of instructions provided (e.g., interpreters).

Programming Languages

• Python: High-level general-purpose interpreted language used in this course.
• Language Characteristics:
  • High-Level vs. Low-Level: How close to machine language.
  • General vs. Targeted: General-purpose languages can handle a broad range of tasks.
  • Interpreted vs. Compiled: Interpreted languages run directly from source code, while compiled languages are converted to object code first.

Python Basics

Data Types and Operations

• Primitive Data Types: Numbers and strings.
• Creating Expressions: Using operators (addition, multiplication, etc.) to create expressions.
• Variable Assignment: Storing values in variables for later use.

Examples in Python

• Basic Operations: Arithmetic operations using numbers and string concatenation.
• Common Errors: Understanding static semantic errors and syntax errors.

Conclusion

• Next Steps: Further exploration of Python's syntax and capabilities in future lectures.
• Action Items: Sign up for recitation and familiarize with the Python shell.