Course Schedule

Readings in italic are optional recommendations for further background on the material.

Week 1

M Sep. 12	Welcome to CSCI 0145!
	We discuss the structure of the class and briefly introduce computational thinking.
	Learning Objectives Computational Thinking	Reading Course syllabus Collaboration and academic honesty Course resources Asking for help Algorithms and bugs
W Sep. 14	Expressions and Variables
	We discuss expressions -- the simplest recipes for computations -- and variables, which allow us to store the results of computations for later use.
	Learning Objectives Computational Thinking Python Programming	Reading Expressions Variables
T Sep. 15	Guided Discovery: Introduction to Strings
	We learn some ways to manipulate text, save the results, and show them to the user. We'll come back to advanced text manipulation much later in the course.
	Learning Objectives Python Programming Applications	Activity GD1
F Sep. 16	Functions
	Functions are reusable computational recipes. We discuss the basic ingredients of a function and work through some simple examples.
	Learning Objectives Computational Thinking Python Programming	Reading Functions (1-3) ThinkPython 3.1-3.7

Week 2

M Sep. 19	More on Functions
	We discuss some important considerations for writing functions and using functions that other people have written. Our main topics are variable scope, documentation, and modules.
	Learning Objectives Computational Thinking Python Programming	Reading Functions (4-5) ThinkPython 3.8-3.14	Assigned HW1
W Sep. 21	Control Flow
	We discuss how to make decisions when executing a program.
	Learning Objectives Computational Thinking Python Programming	Reading Conditionals How Pair Programming Really Works by Stuart Wray Think Like a Computer Scientist, Chapter 7
T Sep. 22	Guided Discovery: Practice With Functions and Control Flow
	We practice writing functions that make simple decisions in response to user input.
	Learning Objectives Computational Thinking Python Programming Social Responsibility	Activity GD2
F Sep. 23	Lab: Developers as Decision-Makers
	We use our skills with gathering user input, writing functions, and making simple decisions that prioritize some users over others. We also practice the human-centered design process.
	Learning Objectives Python Programming Design and Collaboration Social Responsibility	Activity Lab 1	Assigned Lab 1

Week 3

M Sep. 26	Recursion
	We study recursion, a powerful tool for writing simple solutions to complex problems by breaking them down into smaller problems.
	Learning Objectives Computational Thinking	Reading Recursion ThinkPython 5.8-5.14	Assigned HW2	Due HW1
W Sep. 28	Recursion and Recursive Graphics
	We continue our study of recursion while creating simple recursive graphics.
	Learning Objectives Computational Thinking	Reading Turtle Graphics		Due Lab 1
T Sep. 29	Guided Discovery: Recursion and Performance
	We learn some ways to measure and analyze the performance of functions, and use these techniques to study the performance implications of recursive techniques.
	Learning Objectives Computational Thinking Python Programming	Activity GD3
F Sep. 30	Lab: Fractal Graphics with Recursion
	We practice writing functions to produce recursive graphics with the turtle module.
	Learning Objectives Computational Thinking Python Programming	Activity Lab 2	Assigned Lab 2

Week 4

M Oct. 03	Iteration
	We introduce definite and indefinite iteration as a method for performing repeated tasks.
	Learning Objectives Computational Thinking Python Programming	Reading Iteration Think Like a Computer Scientist 8.1-8.9, 9.10-9.14 Think Python Chapter 7 Foundations of Python Programming 7.1-7.4, 14	Assigned HW3	Due HW2
W Oct. 05	Lists
	We introduce lists, data structures that can hold ordered sequences of arbitrary objects. We also begin to discuss some of the synergies between lists and iteration.
	Learning Objectives Computational Thinking Python Programming	Reading Lists Think Like a Computer Scientist 10.1-14 Think Python Chapter 10 Foundations of Python Programming 6		Due Lab 2
T Oct. 06	Lab: Iteration and Lists
	We write functions that check the validity of International Standard Book Numbers (ISBNs). Along the way, we practice writing functions, iteration, and map-filter-reduce operations.
	Learning Objectives Computational Thinking Python Programming Applications	Activity Lab 3	Assigned Lab 3
F Oct. 07	Guided Discovery: Iteration and Lists
	We work on several common patterns for manipulating lists using iteration.
	Learning Objectives Computational Thinking Python Programming	Activity HW4, Problem 1

Week 5

M Oct. 10	List Operations: Map, Filter, and Reduce
	We discuss the map-filter-reduce paradigm for operating on lists of data, and work several examples.
	Learning Objectives Computational Thinking Python Programming	Reading Lists, Section 2 Lecture script	Assigned HW4
W Oct. 12	More Data Structures: Dictionaries and Tuples
	We discuss two other useful data structures, with a special emphasis on the role of dictionaries in representing relationships between pieces of data.
	Learning Objectives Computational Thinking Python Programming	Reading Data Structures Lecture script
T Oct. 13	Lab: Developers as Future-Makers
	We write functions that filter applicants to an opportunity according to a set of numerical criteria, and reflect on the ethical challenges associated with such filters. Along the way, we practice iteration and working with lists.
	Learning Objectives Computational Thinking Python Programming Social Responsibility	Activity Lab 4	Assigned Lab 4
F Oct. 14	No Class!
	Midterm Recess

Week 6

M Oct. 17	Review: Recursion
	We work together on several problems to reinforce core concepts and techniques for recursive problem-solving.
	Learning Objectives Computational Thinking Python Programming	Reading Recursion Lecture Script		Due HW4
W Oct. 19	Representing Data: Integers
	We begin a discussion of why computers represent data in binary, and how to convert between binary and decimal representations of integers.
	Learning Objectives Computational Thinking	Reading Representing Data Lecture Slides		Due Lab 4

Week 7

M Oct. 24	Representing Data: Floating Point Numbers
	We continue our discussion of data representation. We highlight some of the challenges for computers in representing floating point numbers.
	Learning Objectives Computational Thinking	Reading Representing Data Lecture Slides	Assigned HW5
W Oct. 26	Representing Data: and Images
	We discuss representations of digital images as arrays of pixels.
	Learning Objectives Computational Thinking Python Programming Applications	Reading Representing Data Image Processing Lecture Slides	Assigned HW5
T Oct. 27	Lab: Image Manipulation
	We write functions that allow us to manipulate digital images. We implement brightening, contrast-heightening, reflection, and several other transformations.
	Learning Objectives Computational Thinking Python Programming Applications	Activity Lab 5	Assigned Lab 4

Week 8

M Oct. 31	Introducing Object-Oriented Programming
	We begin a discussion of object-oriented programming, the standard approach in Python for writing complex programs by combining data (attributes) and behavior (methods).
	Learning Objectives Computational Thinking Python Programming	Reading Object-Oriented Programming Lecture Slides	Assigned HW5
W Nov. 02	More Object-Oriented Programming
	We continue using object-oriented programming to implement complex program behavior.
	Learning Objectives Computational Thinking Python Programming	Reading Object-Oriented Programming Lecture Slides	Assigned HW5
T Nov. 03	Lab: Object-Oriented Programming
	We use object-oriented programming to create a set of interacting classes that model a computer science course.
	Learning Objectives Computational Thinking Python Programming	Activity Lab 6	Assigned Lab 6

Week 9

M Nov. 07	Unit Testing
	We introduce unit testing, an automated framework for checking correct functionality of small pieces ("units") of code.
	Learning Objectives Python Programming Applications	Reading Blog Post on Medium	Assigned Lab 6
W Nov. 09	More Unit Testing
	We continue with an extended example of unit-testing.
	Learning Objectives Python Programming Applications	Reading Blog Post on Medium	Assigned Lab 6
T Nov. 10	Lab: Developers as Prioritizers
	We develop our skills using object-oriented interfaces while reflecting on some of the difficult choices posed by automated vehicles and rescue services.
	Learning Objectives Social Responsibility Applications	Activity Lab 7	Assigned Lab 7

Week 10

M Nov. 14	String Methods
	We survey several important string methods and use them for basic text analysis tasks.
	Learning Objectives Python Programming Applications	Reading String methods reference A Guide to Formatting with f-strings in Python	Assigned Lab 6
W Nov. 16	Case Study: Lines in Plays
	We use string methods in combination with our iteration and data structure skills to count the number of lines spoken by each character in Shakespeare's Hamlet.
	Learning Objectives Python Programming Applications	Reading String methods reference A Guide to Formatting with f-strings in Python	Assigned Lab 6
T Nov. 17	Lab: The Hedonometer
	We implement sentiment analysis in the style of The Hedonometer at the University of Vermont, and use it to analyze the emotional arc of several famous speeches.
	Learning Objectives Social Responsibility Applications	Activity Lab 8	Assigned Lab 8

Week 11

M Nov. 21	Thanksgiving Break!
	No class this week, enjoy the holiday!

Week 12

M Nov. 28	Introducing Analysis of Algorithms
	We begin the study of analyzing the worst-case runtime of algorithms using big-O analysis.
	Learning Objectives Analysis Computational Thinking	Reading Computational Complexity ThinkPython2e, Appendix B	Assigned Lab 6

© Philip Claude Caplan, Andrea Vaccari, and Phil Chodrow, 2022