About the Series

This Data Science series is structured to connect technical learning with your interest and expertise in the community-based domains in need of data insights. Our collective experience through 2020 has called out for data analysis and understanding of social justice issues -- through an ongoing global pandemic, its unequal economic impact on women, and the scope of police brutality on black and brown individuals.

Through four two-hour workshops and one final sharing session, we’ll learn the basics of data science programming with the Python programming language, and use what we learn to we extract meaning from community-based data to enhance knowledge, inform decision-making, and make more accurate predictions. After participating in the series, students will be able to:

• Analyze and interpret data using an ethically responsible approach
• Identify meaningful questions to ask about a community-based dataset
• Design and develop Python code that helps to answer those questions
• Summarize and contextualize the results of a programmatic data analysis

Everything we learn will work towards a group project that leverages community-based data. We’ll work our way up to the project with mini-assignments that we’ll complete during each meeting. The first four meetings are split into two parts: a lecture/discussion where we learn about data science concepts and the programming skills required to bring them to life, followed by a short programming assignment to put everything into practice. We’ll conclude with the fifth meeting, a presentation session for students’ community-based group projects. We’ll also have short readings to do each week so that everyone can participate in informed discussion.

This series is designed for students from all majors who are beginning programmers. Our introduction to Python is accessible and non-intimidating by design, and we assume no previous knowledge of programming or of data science. The learning in each session builds upon the previous ones, so students must commit to participating in every session. The sessions will be held on-ground at the Boston campus, but we’ll make them available synchronously and remotely for students who are unable to be there in-person.

Links

• Zoom for meetings: https://northeastern.zoom.us/j/99076560784
• Download Python: http://www.python.org
• Instructor: Dr. Laney Strange

Assignments & Assessment

Students will complete a short assignment during the second half of each meeting. They will complete and submit a short self-assessment to assess their level of confidence and mastery in materials covered so far. Students will also complete a group project and present their work to one another and to course staff. Students receive honors credit for:

• Participating in all meetings
• Completing all assignments and self-assessments
• Completing and presenting a group project

Look here for the mini-assignments in each class, course materials, and weekly readings.

Week	Date	Topics	Links
1	9/24 In-person rotation	Introduction to data science; introduction to programming. Assignment #1 Python Reference Sheet #1 Self-Assessment #1	Sample solutions: Assignment #1 Slides: Data science + social justice Slides: Service learning Reading #1 (for 10/1)
2	10/1 In-person rotation	Ethics in data science. Python variables and lists. Assignment #2 Sample code Self-Assessment #2	Ethical Qs: Discussion Reading #2 + Week 2 Writeup Sample solution: Assignment #2
3	10/8 In-person rotation	Formulating meaningful questions. Files and graphs in Python. Assignment #3 Sample code Police Dataset Self-Assessment #3	Data Resources Sample solution: Assignment #3 Reading #3
4	10/15	Final topics and projects Project Assignment Project Slides Self-Assessment #4	Reference: Python Syntax
5	10/29	Project presentations Self-Assessment #5	Handout: Tech for Good Resources

We'll wrap up our Honors Series with a short group project. Once we have a nice solid grounding in programming and data science, you'll connect with a classmate or two to identify a community-based dataset that's meaningful to you and use Python to extract meaning and answer pertinent questions about it.

Students are not expected to devote time to the project outside of class; the fourth meeting is specifically designed to set up the parameters of the project and guide students towards data that is meaningful to them. Each group will choose a dataset, identify a question to delve into, and develop Python code to analyze and visualize the data. At the fifth and final meeting, groups will present their work.