# Data Science Modules

## Overview

Data Science Modules ("DS Modules") are short explorations into data
science that give students the opportunity to work hands-on with a data
set relevant to their course and receive some instruction on the
principles of data analysis, statistics, and computing. DS Modules are
designed and taught in an existing course from any discipline or field
with the help and collaboration of the Data Science Modules Development
Team and the course instructor. These DS Modules are often presented as
interactive lectures or labs that allow students to learn from data sets
that pertain to their course material.

The target audience for DS Modules are students with little to no
previous data science knowledge or experience. Providing DS Modules
within existing courses and in a range of disciplines, allows students
who may never sign up for a data science course on their own the
opportunity to gain skills in the field of data science.

The DS Modules Development Team collaborates with instructors across
different departments and with a range of technical skills in order to
connect with a wide variety of students. Cross-departmental connection
helps to build the DS Modules program presence on campus and attract
other faculty. Instructors work closely with the Development Team so
that they can either teach the DS Modules themselves or assist while the
DS Modules Development Team leads the class.

DS Modules are an excellent way for students to have a smooth and
supported introduction to computing, statistics, and vital tools used in
data science, which are becoming increasingly relevant across academic
disciplines. They empower students to do research and pose their own
questions using data, as well as enable instructors to apply new lenses
to their area of expertise. By the end of a DS Module, students will
learn to process data in real-time, write and execute code, make
visualizations, develop analytical skills, and learn to apply these
skills to their relevant field or course work.

## Key Pedagogical or Curricular Strategies

DS Modules vary widely and are customized based on each instructor's
objectives and course content. A DS Module might simply be one or two
lectures on how to interpret data and statistics in news media reports
or a multiple-session research workshop for students working on a
data-centered project. Students in DS Modules learn to use the
[[Python]](https://www.python.org/) programming language and
complete assignments in [[Jupyter
Notebooks]](http://jupyter.org/), both gold-standard data
science tools used in the [[Data Science Undergraduate
Studies]](https://data.berkeley.edu/academics/undergraduate-programs).
Instructors use these tools to share data with students, assign homework
and write instructions in the cloud-based notebook, and then ask for
students' interpretation of the results, all within the same
environment.

As mentioned, DS Modules bring data-driven instruction into courses by
providing Jupyter Notebooks, an open-source web application that allows
you to create and share documents that contain live code, equations,
visualizations, and narrative text. Jupyter Notebooks are used for data
cleaning and transformation, numerical simulation, statistical modeling,
data visualization, machine learning, and much more.

Undergraduate data science students on the Jupyter Notebook Development
Team work with instructors to create the content in a set of 1 to 3
notebooks to deploy into an existing class. The instructor may already
be teaching about a topic extensively so that students are primed for
the content. For example, if implicit bias is covered heavily in a few
sections of a psychology course, implementing a DS Module allows
students to collect and explore data, and analyze trends in the research
field. This hands-on data analysis often includes computational or
critical thinking assignments.

*Implementation Steps*

If an instructor would like to incorporate a DS Module into their
course, the first step in the process is to hold a one-on-one meeting
with a DS Modules Development Team member to outline the deliverables,
curricular strategies, and develop a plan for collaboration between the
DS Modules Development Team and the instructor, which should include a
timeline for mid-term and long-term deliverables. In a subsequent
planning meeting, the specific data set is discussed and the
instructor's desired outcomes are further clarified.

Depending on the faculty member and the graduate students supporting the
course, the DS Modules Development Team may offer more or less support.
Sometimes the faculty is familiar with Python and prefers deploying the
Jupyter Notebook themselves. Alternatively, the DS Modules Development
Team can teach the DS Modules to the class.

Another implementation method can include a Graduate Student Instructor
(GSI). The GSI can learn how to use the Jupyter Notebook and implement
it within their lab section. The Jupyter Notebooks are created with an
entry-level coding approach and support documentation so that they can
be straightforward for the GSIs to support within the context of the DS
Module lesson. In this case, the DS Module Development Team will train
the GSI and attend the first lab to support during its deployment. Then,
the second time the GSI offers the class they can either instruct
independently or have continued support from the DS Modules Development
Team.

After implementing a DS Module, it is important for them to collect
feedback from the instructor and the students. The Data Science
Undergraduate Studies likes to have constructive discussions with
instructors about what worked well, what didn't work, and to receive
ideas for improvement. They collect student feedback by sending out
evaluations that ask questions about both the content and pacing.

Faculty have had a difficult time following-up over time for further
discussions and continued iterative development, implementation, and
ongoing use of the Jupyter Notebook. The Jupyter Notebooks need to be
regularly updated both for content and for technical updates. Therefore,
if the Notebooks are not regularly updated, then there is a loss of
efficiency. It is important to have a regular schedule to refresh the
materials and to ensure they continue to be a helpful resource.

*Training & Outreach*

The DS Modules Program Coordinator executes a flow of data science
instruction and recruitment for the DS Modules program. It can begin
with the summer workshop where the DS Modules program teaches faculty
from various departments data science methods and gets them ready to
adapt data science teaching tools to their own subject area. This
training serves as a way to connect with faculty by working together to
create new DS Modules for their courses and teaching them key
preparation and implementation methods.

The Data Science Undergraduate Studies at Berkeley has created a
[[Curriculum
Guide](https://github.com/ds-modules/modules-textbook)] to
help instructors with set-up, workflow, and pedagogy in teaching data
sciences courses connected to Data 8. Much of the content in the
[[Curriculum
Guide]](https://github.com/ds-modules/modules-textbook) is
useful for instructors teaching with Jupyter Notebooks and JupyterHub
deployments.

Program representatives speak at the [[Academic Innovations
Studio]](https://ais.berkeley.edu/home), a campus space that
supports pedagogy through the collaboration of faculty, researchers,
graduate students, and staff. The organization is part of [[Research,
Teaching, and Learning]](https://rtl.berkeley.edu/), a
larger organization that also houses the [[ Research
IT](https://research-it.berkeley.edu/)] group.

The [Data Science Undergraduate Studies](https://data.berkeley.edu/) is
continuously looking for faculty interested in developing a DS Module
for their course. They often give presentations to departments, groups
of faculty, or hold one-on-one meetings with instructors to give them an
idea of how implementing a DS Module would be beneficial for their
students.

In terms of recruiting students for the DS Module teams, sometimes it is
not difficult finding students who want to be involved in the DS Module
development teams, and in others, additional outreach is needed.

Key Diversity and Inclusion Practices and Strategies

The focus of conducting DS Modules is to (a) give students exposure to
data science through cross-disciplinary instruction and (b) offer data
science tools to students who may otherwise never have the opportunity.
DS Modules have been taught to everyone from first-year students with no
coding or statistics experience to seniors taking upper-division
econometrics. The [[Data Science Undergraduate
Studies]](https://data.berkeley.edu/) has developed Data
Science Modules for courses in:

-   Sociology

-   Legal Studies

-   Economics

-   Psychology

-   Information Studies

-   Medieval Studies

-   Rhetoric

-   Gender and Women's Studies

-   Linguistics

-   Education

-   Economics

-   Education

-   Gender and Women's Studies

-   Information Studies

-   Legal Studies

-   Linguistics

-   Medieval Studies

-   Psychology

-   Rhetoric

-   Sociology

The [[Data Science Undergraduate
Studies]](https://data.berkeley.edu/) partner with D-Lab,
and often collaborate with [[Cal
NERDS]](https://calnerds.berkeley.edu/), an organization
that is "comprised of a suite of programs and initiatives that provide
faculty-mentored research opportunities, specialized tech training,
graduate school preparation, career coaching, community building, and
professional development to high achieving STEM undergraduates and
graduate students." [[Cal
NERDS](https://calnerds.berkeley.edu/)] gain expertise,
build community, and contribute to the STEM workforce.

The DS Modules team works with [[the Summer Bridge
Program]](https://slc.berkeley.edu/summer-bridge) to
increase the diversity of UC Berkeley students working on DS Module
development. The Summer Bridge program is a six-week, academic
residential program, serving 300+ entering undergraduates every summer.
Scholars take a full course load, including two educational courses and
a mentorship program that acclimates students to the research
university.

The [[Data Science Undergraduate
Studies]](https://data.berkeley.edu/education/data-science-education-opportunities)
is working to get High School students involved through a summer
program. This program is currently being developed by \_\_\_\_\_\_.

Given that all UC Berkeley undergraduates have an American Cultures
graduation requirement, there has been a big push to develop DS Modules
within [[American
Cultures]](https://americancultures.berkeley.edu/students/courses)
classes. This provides another great opportunity to expand data science
approaches into topic areas that have not traditionally employed them.
This collaboration has produced a portfolio of social justice DS Modules
(within the American Cultures courses). In some cases, the DS Modules
team reached out to AC instructors, whereas in other cases motivated
instructors reached out independently to the team with a request to
develop a DS Module. To begin the process, an instructor can make a
straightforward request using the
[form](https://docs.google.com/forms/d/12u2lyW18ifZhl2bSxWbAVqHAFP9v0iioXb86acIb3LE/viewform?edit_requested=true)
on the [Data Science Undergraduate
Studies](https://data.berkeley.edu/education/data-science-education-opportunities)
website.

For example, a DS Module was developed for Ethnic Studies 21, a class on
mass incarceration, by a student who had taken the course in a previous
semester. This DS Module explored prison overcrowding and realignment
data. In a full cycle of its development and implementation, this
studentled the DS Module in the class for its initial deployment.

## Links to Key Cyber Resources

-   Data Science DS Modules
     [[website]](https://data.berkeley.edu/education/modules)

-   Data Science Modules informational
     [[GitHub]](https://ds-modules.github.io/)

-   [[Course Listings]](https://github.com/ds-modules)

-   [[GitHub]](https://github.com/ds-modules): Jupyter
     notebooks are developed and stored. The public materials are for
     students. The private materials are for the faculty and GSIs.

    -   Answer Keys and information for instructors is kept behind a
         password online on GitHub

-   [[Deployment
     Calendar]](https://calendar.google.com/calendar/embed?src=berkeley.edu_gka2us8b56n33cqvch528gt650%40group.calendar.google.com&ctz=America%2FLos_Angeles)

-   [[Data Peer
     Consultants](https://docs.google.com/document/d/1F66WKf7dYEglM9tUexe5MWYtqkO8qZz1G_zGYr7dlOk/edit)]

    -   Located in the Moffit Library

    -   They can assist GSIs if they are not comfortable with the DS
         Module's content.

    -   DS Module students can work with a Data Peer Consultant during
         their [[drop-in hours from 11am to 4pm on Monday through
         Friday]](https://berkeley.zoom.us/j/7296681990?status=success)
         or [[access them
         online]](https://data.berkeley.edu/consulting).

    -   The DS Modules Program will alert the peer consultants at
         Moffitt before the DS Module's deployment so that they can
         prepare to serve as alternative office hours.

-   Publicity

    -   Building Data Science Education Together
         [[article]](https://data.berkeley.edu/news/building-data-science-education-together)

    -   [[Short Video]](https://youtu.be/6J_bvxWXibM)
         explaining DS Modules

## Examples

Course listing on UCB DSEP
[[GitHub]](https://ds-modules.github.io/DS-Modules/)![](../media/image2.png)

Course GitHub
[[website]](https://github.com/ds-modules/PSYCH-167AC)

![](../media/image16.png)

Course DataHub
[[website]](https://datahub.berkeley.edu/user/rstarowi/tree/PSYCH-167AC)![](../media/image19.png)

Course Binder
[[website]](https://mybinder.org/v2/gh/ds-modules/PSYCH-167AC/master)

![](../media/image9.png)