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Python functions

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An introduction to functions in Python. Prior knowledge of variables, assignments, expressions, input-output, lists, conditionals, and loops is recommended. For CS0 students. Part of the CUNY CS04All project. Comments Lecture slides come in three formats and separate files, as well as three programs-examples. All the images used in the slides are CC0 licence, packed in the imgs.rar archive together with the information about them In-class work, HW assignment, assessment questions together with all the programs are in the Activities_and_Assignments.rar archive.

Material Type: Lecture Notes

Author: Natalia Novak

CS04ALL: Command Line Python

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Students are presented with information relating to stand alone Python programs, stdin, stdout, and command line arguments. This is a lab exercise. After completion students should be able to create executable Python programs which can accept input from stdin or command line arguments.

Material Type: Activity/Lab

Author: Hunter. R Johnson

Python if statements

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Python If-else branches, equality and relational operators, and some additional topics: Boolean operators and expressions, membership and identity operators. Prior knowledge of variables, assignments, and expressions is recommended. For CS0 students. Part of the CUNY CS04All project. Comments Lecture slides come in three formats and separate files. In-class work, HW assignment, assessment questions together with all the programs are in the Activities_and_Assignments.rar archive.

Material Type: Lecture Notes

Author: Natalia Novak

Python input output

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The following topics are discussed: Development Environment Basic input and output Variables and assignments Python expressions Division and modulo Math module For CS0 students. Part of the CUNY CS04All project. Comments Lecture slides come in three formats, examples of programs are included in Instructor Materials.rar. In-class work and assessment questions together with all the programs are in the Activities_and_Assignments.rar archive.

Material Type: Lecture Notes

Author: Natalia Novak

List Comprehensions

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List Comprehensions This is a tutorial on list comprehensions in Python, suitable for use in an Intro or CS0 course. We also briefly mention set comprehensions and dictionary comprehensions. https://cocalc.com/share/bde99afd-76c8-493d-9608-db9019bcd346/171/list_comprehensions?viewer=share/ This OER material was produced as a result of the CS04ALL CUNY OER project

Material Type: Activity/Lab, Lecture Notes

Author: Johnson Hunter R

Python loops

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The following topics are covered: While Loops For Loops Nested loops Break and continue Loops else enumerate() Applications: Turtle library with loops and decision procedures. Prior knowledge of variables, assignments, expressions, input-output, lists, and conditionals is recommended. For CS0 students. Part of the CUNY CS04All project. Comments Lecture slides come in three formats, are are packed into Instructor_Materials.rar along with programs accompanying the lecture slides. In-class work, HW assignment, assessment questions together with all the programs are in the ActivitiesAndAssignments.rar archive.

Material Type: Lecture Notes

Author: Natalia Novak

Python string

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An introduction to Python strings and string formatting. Proposed lecture slides are supplied with in-class activity, homework assignment, and assessment. No loops, no decision structures. For CS0 students. Part of the CUNY CS04All project.

Material Type: Activity/Lab, Assessment, Homework/Assignment, Lecture Notes

Authors: Natalia Novak, Novak Natalia

Machine Learning Module

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These are materials that may be used in a CS0 course as a light introduction to machine learning. The materials are mostly Jupyter notebooks which contain a combination of labwork and lecture notes. There are notebooks on Classification, An Introduction to Numpy, and An Introduction to Pandas. There are also two assessments that could be assigned to students. One is an essay assignment in which students are asked to read and respond to an article on machine bias. The other is a lab-like exercise in which students use pandas and numpy to extract useful information about subway ridership in NYC. This assignment uses public data provided by NYC concerning entrances and exits at MTA stations. This OER material was produced as a result of the CS04ALL CUNY OER project

Material Type: Activity/Lab, Lecture Notes

Author: Johnson Hunter R

Natural Language Processing Project

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In this archive there are two activities/assignments suitable for use in a CS0 or Intro course which uses Python. In the first activity, students are asked to "fill in the code" in a series of short programs that compute a similarity metric (cosine similarity) for text documents. This involves string tokenization, and frequency counting using Python string methods and datatypes. https://cocalc.com/share/bde99afd-76c8-493d-9608-db9019bcd346/171/Proj1?viewer=share/ In the second activity (taken directly from Think Python 2e) students use a pronunciation dictionary to solve a riddle involving homophones. https://cocalc.com/share/bde99afd-76c8-493d-9608-db9019bcd346/171/Dicts2?viewer=share/ This OER material was produced as a result of the CS04ALL CUNY OER project

Material Type: Activity/Lab, Lecture Notes

Author: Hunter R Johnson

How to Think like a Computer Scientist with Python Interactive Edition

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This interactive book is a product of the Runestone Interactive Project at Luther College, led by Brad Miller and David Ranum. There have been many contributors to the project. Our thanks especially to the following: This book is based on the Original work by: Jeffrey Elkner, Allen B. Downey, and Chris Meyers Activecode based on Skulpt Codelens based on Online Python Tutor Many contributions from the CSLearning4U research group at Georgia Tech. ACM-SIGCSE for the special projects grant that funded our student Isaac Dontje Lindell for the summer of 2013. NSF The Runestone Interactive tools are open source and we encourage you to contact us, or grab a copy from GitHub if you would like to use them to write your own resources.

Material Type: Textbook

Authors: Allen B. Downey, Chris Meyers, Jeffrey Elkner