# Data Scholars Pathways Seminar

## Overview

This course is one of the three sequence seminars for students in the
Data Scholars program. After finishing the Foundations Course, students
take the Pathways Seminar. This course exposes Data Scholars to
opportunities specific to future employment in data science. The Seminar
attracts a broad range of students, such as people curious to know more
about data science outside of the Foundations Course to students who
know they want to major and work in the field.

## Target Audience

The Data Science Pathways Seminar is one part of the Data Scholars
Program. Data Scholars take it after they have completed the Foundations
Course and the Foundations Seminar for the Data Scholars Program. The
course offers a deeper understanding of future employment opportunities
for students interested in pursuing data science.

## Goals

The Pathways Seminar focuses on five goals.

-   Understand multidisciplinary career opportunities within data
     science.

-   Build an understanding of essential data science tools.

-   Network with cohort, speakers, mentors, etc.

-   Envision long-term research/internship efforts & build portfolio.

-   Boost confidence in a planned/flexible format.

## Key Pedagogical or Curricular Strategies

In Pathways, students learn about the opportunities of real-world data
science applications through three main avenues. First, guest speakers
from industry, research, and academia talk with students on what they do
with data science, how they got there, and advice for undergrads.
Secondly, workshops led by the instructor, D-Lab staff, and guest
speakers give students valuable professional and technical skills to
secure relevant internships and research positions. Finally, self-guided
exploration and reflection assignments will empower students to gain
desired skills and connections in data science subfields of personal
interest.

The instructor encourages a welcoming course by setting group
expectations. Examples of these expectations include:

-   "It's okay not to know." This guiding principle acknowledges the
     acceptance and importance of questions at any time.

-   "Growth mindset." Specifically, in data science, abilities can be
     developed through dedication and hard work across time instead of
     a "fixed mindset," assuming that you're either good at data
     science or not.

-   "Everyone has something to offer in the discussion." All of us have
     interacted with and been affected by "Big Data." Everyone in the
     seminar brings valuable questions, opinions, and insights,
     regardless of their technical experience or conceptual knowledge.

-   "Step up/step back." Discussion is more vibrant when you both add
     your unique point of view ("step-up") AND "step back" if you've
     been sharing a lot recently (or if you're having a bad day).

-   "Don't yuck my yum." No negative comments are welcome regarding
     someone else's preferred workflow/tools/language/etc. (e.g., Mac
     vs. PC, Python vs. R, tabs vs. spaces). We will respect everyone's
     preferences.

## Key Diversity and Inclusion Practices and Strategies:

Students are empowered to participate and process the information
together throughout the semester. Students think through their
understanding of employment pathways by writing start and exit
reflections (1-page write-ups of your thoughts and questions on a career
in data science, submitted in the first and last weeks of the semester).

Weekly workshops and speaker background assignments create a foundation
for students to explore background articles, videos, and or coding
assignments that will provide a foundation for speakers and workshops.
Students are expected to submit two points that stood out to them from
the background and two questions they have for the speaker/workshop to
receive credit.

The Data Science [[Exploration
Assignment]](https://docs.google.com/document/d/1-metTMvzKOWQQ5AHqxQkR1o2b1HXWjAQlY1SYPM7Db8/edit)
is an experiential assignment designed to familiarize students with the
modern data science professional landscape and build skills. Students
will complete two Explorations per semester, choosing from a [[list of
suggested
options]](https://drive.google.com/a/berkeley.edu/open?id=1-metTMvzKOWQQ5AHqxQkR1o2b1HXWjAQlY1SYPM7Db8)
or proposing their own (with instructor approval).

## Links to Key Documents

-   [[Spring 2020 Pathways
     Syllabus]](https://docs.google.com/document/d/1bY23SDYibyCu1RF9l727hKCfObDyF_5fyjm-SUS7v2g/edit)

-   [[Spring 2018 Course
     Website]](https://sites.google.com/berkeley.edu/pathways/home?authuser=0)

## Program Description

The Pathways Seminar is one part of a three-course series to support
Data Scholars' success at Cal. This seminar meets for one-and-a-half
hours, once per week. During this course, students attend talks from
speakers and workshops on data science tools. The small seminar space
has a foundation of valuing student support needs and enhancing their
ability to network with faculty and professionals.

## Best Practices for Variation Across Institutions

It is useful to develop a network of guest speakers and community
partners who can host workshops. Your focus areas for speakers and
workshops will vary depending on your student population and needs. For
example, at Cal in Spring 2018, a guest speaker from the College Futures
Foundation came to the Pathways Seminar.

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