Best practices for teaching an introductory data science course

eCOTS 2022

Nicholas J. Horton and Benjamin S. Baumer

May 19, 2022

Welcome, introductions, and overview

Ice breaker #1

What are you looking forward to at eCOTS 2022?
Please write your response in the chat but hold off on pressing the RETURN/ENTER key until we ask you to do so.

Ice breaker #2

Please share your name and affiliation plus a question for us if you have one.
Please write your response in the chat but hold off on pressing the RETURN/ENTER key until we ask you to do so.

Plan for the day

Overview of inclusive practices and the principles
Exploration of several of the principles (breakouts to share thoughts)
Share reflections, resources, and experiences with the whole group
Identify next steps to continue this work

The Data Science Corps WAV

Experiential Learning with Local Community Organizations
4 years, 9 institutions, $1.2 million budget
25-30 students per semester doing projects, 5 cohorts begun January 2020
working in parallel with three community colleges
Thanks to NSF #1924017 and 1923388 for funding this work

The Data Science Corps WAV

See:

Horton et al. (2021), The Data Science Corps Wrangle-Analyze-Visualize program: building data acumen for undergraduate students
Harvard Data Science Review
Legacy et al. (2021), Facilitating Team-Based Data Science: Lessons Learned from the DSC-WAV Project
special issue on “Data Science Education Research”
Foundations of Data Science
More later…

Goals for the workshop

Motivate the importance of inclusive pedagogies and student-centered approaches
Familiarize participants with the Dana Center Data Science Course Design Framework
Explore several of the principles
Integrate them into an introductory data science course
Share resources and best practices
Explore next steps

Dana Center Design Principles

Inclusive practices

Simply implementing new courses is not enough
Need to eliminate institutional and system barriers to student success
Design effective, inclusive practices from the start

Dana Center Data Science Course Framework

Copyright 2021
The Charles A. Dana Center at The University of Texas at Austin
Educators granted a nonexclusive license to reproduce and share copies

Dana Center Data Science Design Principles

Support students’ Social, Emotional, and Academic Development (SEAD)
Explicitly attend to system inequities in school and society
Develop students’ ability to engage in data science practice and process
Remain true to the core of the course
A range of pedagogical strategies
Already doing much of this work
But more is needed!

DP1: Active Learning

The course provides regular opportunities for students to actively engage in data explorations using a variety of different instructional strategies (e.g., hands-on and technology-based activities, projects, small group collaborative work, facilitated student discourse, interactive lectures).

DP2: Growth Mindset

The course supports students in developing the tenacity, persistence, and perseverance necessary for learning data science, for using mathematics and statistics to tackle authentic problems, and for being successful in post-high school endeavors.

Example:

DP3: Problem Solving

The course provides opportunities for students to engage in the entire statistical problem-solving process.

DP4: Authenticity

The course presents data explorations that allow students to address relevant questions that arise in their communities.

Example:

Open Case Studies

DP5: Context and Interdisciplinary Connections

The course presents data science in context and connect data science to various disciplines and everyday experiences.

Example:

Predictive Policing

DP6: Communication

The course develops students’ ability to communicate insights from their data explorations and findings in varied ways, including with words, data visualizations and numbers.

DP7: Technology

The course introduces students to current technologies appropriate for data exploration and visualization, and prepares them to learn and use new ones.

DP8: Assessment

The course uses project-based assessments both as formative assessments and to evaluate student progress.

SHOPPING and WORKING

Plan for the workshop

Introduce several technologies used to facilitate communication and computation
Review specific design principles, one at a time
Consider student-centered aspects
Consider instructor-centered aspects
Making it real: propose an activity or set of activities with this principle in mind
Caveats and reflection by the workshop participants
Repeat several times

Technology and tools

if we were in-person and if we were a smaller group, we could take more time and move more slowly
our goal is to paint a broad landscape and point you towards promising options
we don’t expect you to follow everything or adopt our full approach
success would be if you learned about some new things and generated a list of approaches to explore

Working together today

CAUSE code of conduct:

“All individuals must behave responsibly in CAUSE activities in which they participate.”

Zoom and the chat

Please post questions, comments, and reflections in the chat when (not if!) you get stuck or we say something that doesn’t make sense to you.
Please respond in the chat if you have insights or can assist.
(Same for Slack channel)

Slack 101

https://CAUSEweb.slack.com
#ecots-workshop-best-practices-teaching-intro-data-science channel
interact during (and after) the workshop

Slack 101

Best practices: organize discussions in threads

Slack 101

Best practices: organize discussions in threads

Slack 101

Best practices: direct messages (DM)

Slack pedagogical notes

encourage students to set notifications
use other systems (e.g., Course Management System) for more formal communication
encourage use of Slack for less formal interactions
respond to individual DM to the entire class (while anonymizing the student who posed the question)
support for simple polls
Slack particularly compelling if your institution uses it for other purposes
Other technologies for discussion worth considering
https://slack.com/resources/using-slack/your-guide-to-slack-for-higher-education
https://towardsdatascience.com/6-data-science-slack-workshops-you-need-to-join-b0c00952105d

Slack pedagogical notes (cont.)

a contrarian view: https://rtalbert.org/the-rise-and-fall-of-slack-in-my-teaching-a-cautionary-tale/

GitHub 101

https://github.com/DSC-WAV/best-practices

GitHub 101

https://github.com/DSC-WAV/best-practices

GitHub 101

GitHub is a powerful and expert friendly version control system
We will be using it today as a way to explore collaborations
We will also make files available using GitHub

https://github.com/DSC-WAV/best-practices

More on using GitHub in the classroom in Beckman et al, JSDSE

RStudio cloud

We wanted to provide you with the ability to engage with some of our materials even if you didn’t have a full RStudio setup
Big advantage of the cloud: simplifies package installation and setup
Not my typical approach (we utilize cloud-based servers for our introductory students, then transition them to their own machines after they are up and running)
We hope that the free account will work for you
We suggest that use your GitHub account to log in

https://rstudio.cloud/project/4064758

Making this happen

Shared Google Drive folder
Editable file to share Resources
Suggestion: open these files in a new tab
Share your ideas and reflections in these documents during the workshop
Use the chat or Slack to share your thoughts (or alert people to additions)

Where do we go from here?

Recap of workshop goals

Our goal was to help you to engage with these design principles
Our plan was to share examples of how we have integrated these principles into our courses and spark discussion
End of workshop survey/reflection: https://forms.gle/UPKkUwPYmDZLYV2S7

What’s next?

Sharing resources
Slack channel: please share your best idea and questions
What eCOTS session are you most excited about?
What ideas will you be bringing back to your colleagues?
What do you wish we’d discussed but didn’t?
What question do you still have?