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Course outline

PSYC 81.09 (Storytelling with Data) is organized into 2 main parts. Part I comprises four modules, and is collectively aimed at introducing students to the process of creating "data stories" using Python data science tools:

Part II is project-based, and revolves around mini data science projects. For each project, one or more students choose a question and dataset to explore and turn into a data story. Each week students and groups will report on their progress with the latest iterations of their stories. Students should aim to participate in three or more projects during Part II of the course. At students' discretion, those three (or more) projects may comprise the same questions and/or datasets (e.g., whereby each story builds on the previous story), or multiple questions and/or datasets that may or may not be related. In addition, students are encouraged to build off of each others' code, projects, and questions. Projects and project groups should form organically and should remain flexible to facilitate changing goals and interests.

Lecture recordings are denoted in bolded links below.

Introduction

Part I

Module 1: What makes a good story?

Module 2: Visualizing data

Module 3: Python and Jupyter notebooks as a medium for data storytelling

Module 4: Data science tools

Part II

We will spend Part II of the course repeating three general steps in the storytelling process (a video introduction to Part II may be found here):

  1. Pitching and brainstorming. You'll present your ideas to your classmates, form groups, workshop story ideas.
    • Brainstorm session
  2. Refinement. We'll workshop your (and your group's) ideas and code. You can also use this time to bring up new content ideas that you'd like to learn more about.
  3. Critiquing. As a class we will discuss your story and provide constructive feedback. We'll also go through your code and discuss any relevant coding issues (e.g., challenges, clever hacks, etc.) that might be relevant to the class.

You should plan to make it through this cycle at least three times during Part II of the course (i.e., you should produce at least 3 data stories).

Each data story should be contained in a single sub-folder of data-stories. Your project should comprise the following files, based on this project template:

  • A README.md markdown file based on this template. The README file should contain:
    • A project description and overview.
    • A link to a YouTube video of your (5 minute) data story. (A playlist containing the current set of data stories may be found here.)
    • Links to the data you analyzed.
    • Instructions for replicating your results.
    • A description of how someone could contribute to your project.
    • Acknowledgements and citations.
  • Your data story (hosted on YouTube and cited in your README file). Note: you don't need to upload the source video, but if you use any images or slides you should include them in a sub-folder.
  • Your project's code (e.g., notebooks, Python scripts, etc.), based on this template.
  • If under 10 MB (total), you can include your data files directly in your project folder. Otherwise you should host it on Google Drive, Dropbox, or some other cloud-based source accessible to all (current and future) students. Regardless of how your project's data files are hosted, your notebook should include code for downloading and importing the data (so from a user's perspective it shouldn't matter where the data files are hosted).

Part II Lecture recordings: