Data is everywhere – but it doesn’t speak for itself. Too often, decision-makers overlook or ignore data-driven insights because no one tells the data’s story. Students in this pathway will bridge the gap between data analysis and communication, learning to gather, understand, and visualize various forms of data to solve problems and inform real-world decision-making.

The Data Exploration & Communication Pathway helps students explore the wide-ranging applications of data science, practice skills to collect and analyze data, and gain knowledge of the techniques used to effectively communicate insights for making data-driven decisions and solving real-world problems.

Students who choose this pathway will develop knowledge and skills in these areas:

  • Statistical analysis and experimental design
  • Data collection and interpretation
  • Skills in programming, computing, and modelling
  • Strategies for communicating and visualizing data insights
  • Analytical decision-making
  • Practical applications of data science and applied technology

Students on this pathway might go into public policy, business, research, or journalism. They might also become analysts, consultants, designers, engineers, developers, managers, scientists, or reporters. Whether you’re new to data science or already working toward “big data” goals, this pathway is adaptable to a wide range of student interests and life aspirations and is designed to help you gain confidence and feel empowered in your data science knowledge and skills during and after college.

Faculty & Staff

Marian Frazier

Marian Frazier

Associate Professor and Associate Chair of Statistical & Data Sciences, Co-Director, Applied Methods & Research Experience (AMRE)


Jenni Griffin

Jenni Griffin

Dean of APEX; Liaison for Data Exploration & Communication Pathway


Shelley Judge

Shelley Judge

Department Chair and Associate Professor of Earth Sciences; Liaison to the Data Exploration & Communication Pathway


Jillian Morrison

Jillian Morrison

Assistant Professor of Statistical and Data Sciences


Erzsebet Regan

Erzsebet Regan

Whitmore-Williams Associate Professor of Biochemistry and Molecular Biology; Biology (on leave Fall 2022)


Greg Shaya

Greg Shaya

Henry J. and Laura H. Copeland Professor of European History; Global Media & Digital Studies; Liaison for Data Exploration & Communication Pathway


Sofia Visa

Sofia Visa

Professor of Computer Science and Co-Chair of Mathematical & Computational Sciences


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Nathan Sundheimer ’17

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Mike Frazier ’03 analyzes information to put the Kansas City Chiefs in position to succeed 

As statistical analysis coordinator for the Kansas City Chiefs, Mike Frazier ’03 uses player statistics and data generated by coaches, the NFL, and third-party […]

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Experiential Learning Opportunities (One Experience)

Approved experiences should (1) directly integrate classroom theories and principles with situational real-world analysis, decision-making, and communication; and (2) foster soft-skills, such as listening, presentation, public speaking, verbal communication, visual communication, and writing skills. These could include:

  • Project-based/Problem-based Learning (Examples: AMRE; Pathways Assistant; Sophomore Research; Significant course EL projects (with either on- or off-campus partners); Policy Academies Program)
  • Internships (on- or off-campus)
  • Service-oriented Activities (Examples: Volunteer work; Leadership roles in clubs and organizations)


Reflections guide students to articulate meaningful connections between the skills and knowledge they are gaining and the experiences in their coursework, experiential learning opportunities and career goals. Reflections take place along five points in the pathway:

First Reflection Touchpoint: At the Start of the Pathway

The first opportunity to reflect is when the student declares their Pathway.  Responses to prompts asked at this moment establish a baseline from which student moves forward.

Second Reflection Touchpoint: An Opportunity to Investigate

This is an opportunity for students to dig deeper to articulate what they are learning along the Pathway in classes and about experiential learning options related to the interests they shared in the first reflection.   It is also a point at which to prepare for experiential learning/career exploration.

Third Reflection Touchpoint: Before Experiential Learning Opportunity

This reflection takes place as a student is learning about experiential learning opportunities related to their pathway.

Fourth Reflection Touchpoint: After Experiential Learning Opportunity<

This reflection takes place after the student has completed an experiential learning opportunity and asks them to consider how the work they have done connects with their pathway.

Fifth Reflection Touchpoint: At the End of This Pathway – and the Start of New Ones

At this touchpoint, students engage with questions that help them build connections between theory and practice, their career goals, and how they plan to extend their Pathway beyond Wooster.

Coursework (Four Courses)

Students will complete four courses, with at least one course from three of the following skill fields. Student must complete courses from at least two different divisions:

Skill Field #1: Statistical Analysis

These courses offer instruction on introductory to intermediate methods and techniques of statistical analysis that can be applied to a variety of fields and disciplines. Coursework in this skill field has students practice skills in experimental design, data collection and interpretation, and analytical decision-making relative to their intended discipline.

  • BIOL 20300 – Research Skills in Biology*
  • DATA 23100 – Applied Statistical Methods*
  • ESCI 29901 – Statistics for Earth Sciences
  • MATH 10200 – Introduction to Statistics (MNS)
  • ECON 11000 – Quantitative Methods*
  • ECON 21000 – Ecônmetrics*
  • PSCI 40101 – Research Methods and Design
  • PSYC 25000 – Intro to Statistics and Experimental Design*
  • SOAN 34100 – Social Statistics*

*Course has pre-requisite or requires instructor permission to register

Skill Field #2: Data Science and Technology Skills

These courses encourage students to make practical connections between data sciences and applied technology. Coursework in this skill field will assist students in practicing skills in programming, computing, modelling, and/or problem-solving.

  • CSCI 10000 – Scientific Computing
  • CSCI 10200 – Multimedia Computing
  • CSCI 11000 – Imperative Problem Solving*
  • CSCI 12000 – Data Structures and Algorithms*
  • DATA 10600 – Introduction to Data Science
  • DATA 32500 – Applied Data Science*
  • MATH 22300 – Graph Theory & Combinatorics

*Course has pre-requisite or requires instructor permission to register

Skill Field #3: Data-Driven Communication and Visualization

These courses provide insight into how data are communicated and visualized in a wide range of forms. Students in this skill field may receive introductions to foundations and techniques of information visualization, explore dynamics of effective communication, and practice producing and visual statements to communicate data insights.

  • ARTS 15700 – Introduction to 2D Design and Color
  • ARTS 17100 – Introduction to Digital Imaging*
  • COMM 23700 – Visual Rhetoric*
  • COMM 22500 – Small Group Communication
  • COMM 33200 – Visual Communication*
  • DATA 20100 – Data Visualization*
  • ESCI 25000 – Introduction to GIS
  • GMDS 23100 – Visualizing Information
  • HIST 20207 – Visualizing Information* (Workshop)

*Course has pre-requisite or requires instructor permission to register

Skill Field #4: In-depth Explorations

These courses have students practice skills and techniques in data, computing, and/or research in order to more deeply explore their intended discipline or prospective career field(s).

  • BIOL 34500 – Computational Biology*
  • COMM 35300 – Quantitative Methods*
  • CSCI 31000 – Machine Intelligence*
  • CSCI 23200 – Software Engineering-Databases*
  • EDUC 31000 Assessment & Intervention in Teaching Reading*
  • EDUC-26000 Curriculum: Math/Science/Social Studies in the Early Childhood Years*
  • EDUC-32000 Advanced Methods & Assessment in Langauge Arts, Integrated Mathematics, or Integrated Social Studies*
  • MATH 22900 – Probability Theory*
  • MATH 32900 – Statistical Theory*
  • PHIL 22000 – Logic and Philosophy
  • PHYS 23000 – Computational Physics*
  • PSCI 21900 – Public Opinion and Voting Behavior

*Course has pre-requisite or requires instructor permission to register