Don’t Miss Module 00
This overview is designed to orient you to the class. Please watch the videos from this playlist and work your way through the notes. Although the module-level playlists are embedded in the course, you can find the full-course video playlist here. In addition, you can find the slides for this module here. Currently, there are seven videos in this playlist. The average video length is 12 minutes, 27 seconds. The total length of the playlist is 1 hour, 27 minutes, 10 seconds.
Data Science for Psychologists (DS4P) introduces on the principles of data science, including:
- data wrangling,
- modeling,
- visualization, and
- communication.
This class links those principles to psychological methods and open science practices by emphasizing exploratory analyses and description rather than confirmatory analyses and prediction. Through the semester, we will work our way through Wickham and Grolemund’s R for Data Science text and develop proficiency with tidyverse. This class emphasizes replication and reproducibility. DS4P is a practical skilled-based class and should be helpful to students aiming for academia and those interested in industry. Applications of these methods can be applied to a full range of psychological areas, including perception (e.g., eye-tracking data), neuroscience (e.g., visualizing neural networks), and individual differences (e.g., valence analysis).
0.1 Big Ideas
This class covers the following broad five areas:
- Reproducibility;
- Replication;
- Robust Methods;
- Resplendent Visualizations; and
- R Programming.
0.2 Course Modality
I have taught this class in practically every modality available. Technically, this course is designated as a blended course. This designation is because this course’s modality changes based on the level of COVID-19 transmission in the community. When COVID-19 community transmission is low or medium, this class will be in-person and masking will be required. However, if COVID-19 transmission is high in the community, this course may be moved entirely online in order to protect the health and safety of students and instructor. In the case that we move online, the in-person sessions will become synchronous online sessions. Any changes will be clearly and promptly communicated via email.
Pragmatically, the mask-to-mask portions of the class are – well – mask-to-mask. Or that was the idea anyway… however, during the first semester I taught this course, a few members of the class were on the other side of the planet. Accordingly, I pivoted all the planned in-class activities and labs so that the entire class could complete their degrees on-time. So obviously this last-minute pivot was a little messy, but I think it turned out ok… So again, technically, this class was blended, but effectively, it can be completed from anywhere at any time. It had to be.
0.2.1 Successful Asynchronous Learning
I’ve created a video highlighting how to be a successful asynchronous learner.
Much of this information comes from Northeastern University’s Tips for Taking Online Classes