Storytelling with Data

Welcome! This repository contains course materials for the Dartmouth Course Storytelling with Data (PSYC 81.09). The syllabus may be found [here]. Feel free to follow along with the course materials (whether you are officially enrolled in the course or just visiting!), submit comments and suggestions, etc. An outline of the course materials, including links to lecture and discussion videos and assignments may be found here. A YouTube playlist of students' data stories for the 2020W term may be found here.

Dartmouth student instructions

If you are officially enrolled in this course as a Dartmouth student, please sign up for access to the course's Slack workspace (you need to join using your @dartmouth.edu email address). You can ask questions and get help with all aspects of the course via Slack. You'll also submit your first two assignments using Slack. Please also ensure that you have access to our course's Canvas page. I'll post a link to our recurring Zoom meetings on Canvas, along with recordings of each "live" course meeting.

A note about this Open Course

This course is taught as an Open Course, meaning that the course is designed from the ground up to be shareable and accessible to anyone. To that end, all code for this course should be written in Python and organized in a Jupyter notebook. Any data you analyze must be shareable with all other students in the course, and ideally it should be shareable with the public. All code and other student-generated materials will be shared publicly.

Getting help

Data science is a tricky, rewarding, and often frustrating business. Luckily for us data scientists, there are many places to get help! Examples include:

• Google-- searchable portal to of all human knowledge. Most Internet things are reachable through here, and it's a great place to start your search. You can often find code that other people have written that solves a similar problem to the one you're working on, or a tutorial that teaches you how to solve a particular class of problems.
• Wikipedia-- community-curated encyclopedia. Wikipedia is a good resource for learning about the background of a technique, looking up equations, etc. It's not a good source for tutorials.
• Slack-- course chatroom for Dartmouth students. A good place for to ask questions, post ideas, etc., to other members of the class.
• My lab also maintains a public repository of tutorials on a variety of topics here.
• The last (but hopefully not least) option if you're feeling stuck, unhappy with how things are progressing, looking for fun new ideas to revitalize your project and get you interested in science again, etc. is to reach out to me. If you're a Dartmouth person you attend to my regular office hours on Zoom, email me or message me on Slack.
• Important-- chances are good that if you're feeling lost, you're not the only one! If you learn something useful, please share it via Slack or by opening a GitHub issue.

Where to find nice datasets

In todays "Big Data" world, there are an abundance of high-quality, free datasets to enjoy and explore. Below is a short list of websites that are great resources for data (each contains links to many datasets):

• Kaggle
• FiveThirtyEight
• Awesome Public Datasets
• World Bank Open Data
• World Health Organization
• Google Public Data Explorer
• Registry of Open Data on Amazon Web Services