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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.

Subject:
Computer Science
Material Type:
Activity/Lab
Provider:
CUNY Academic Works
Provider Set:
John Jay College of Criminal Justice
Author:
Hunter. R Johnson
Date Added:
02/02/2019
Cryptography Module" by Hunter R. Johnson
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This archive contains a series of lessons on cryptography suitable for use in a CS0 course. The only requirement is familiarity with Python, particularly dictionaries, lists, and file IO. It is also assumed that students know how to create stand-alone Python programs and interact with them through the terminal. Most of the work is done in Jupyter notebooks.

The material found in the notebooks is a combination of reading material, exercises, activities and assignments. Below are descriptions of each lesson or assignment and links to notebooks on Cocalc. The same files are available for batch download in this archive.

Subject:
Computer Science
Material Type:
Activity/Lab
Lecture Notes
Provider:
CUNY Academic Works
Provider Set:
John Jay College of Criminal Justice
Author:
Johnson Hunter R
Date Added:
06/04/2019
Guidelines on how to analyze a social science article (or social science documentary film)
Conditions of Use:
No Strings Attached
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A guide on how to read an article, for undergraduate students. It’s designed for anthropology classes but might work for other social sciences as well.

Subject:
Social Science
Material Type:
Reading
Provider:
CUNY Academic Works
Provider Set:
John Jay College of Criminal Justice
Date Added:
01/01/2017
List Comprehensions
Conditions of Use:
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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

Subject:
Computer Science
Material Type:
Activity/Lab
Lecture Notes
Provider:
CUNY Academic Works
Provider Set:
John Jay College of Criminal Justice
Author:
Johnson Hunter R
Date Added:
06/04/2019
Machine Learning Module
Conditions of Use:
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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

Subject:
Computer Science
Material Type:
Activity/Lab
Lecture Notes
Provider:
CUNY Academic Works
Provider Set:
John Jay College of Criminal Justice
Author:
Johnson Hunter R
Date Added:
06/04/2019
Natural Language Processing Project
Conditions of Use:
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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

Subject:
Computer Science
Material Type:
Activity/Lab
Lecture Notes
Provider:
CUNY Academic Works
Provider Set:
John Jay College of Criminal Justice
Author:
Hunter R Johnson
Date Added:
06/04/2019