Catalogue PDF Version

Catalogue - PDF Version

Data Analytics

Program Faculty
Kendralin Freeman, Associate Professor, Sociology, Co-Chair
Jonathan Forde, Professor, Mathematics and Computer Science, Co-Chair
Nan Crystal Arens, Professor, Geoscience
Jocelyn Bell, Associate Professor, Mathematics and Computer Science
Kristen Brubaker, Associate Professor, Environmental Studeis
T. Alden Gassert, Director of Institutional Research
Leslie Hebb, Associate Professor, Physics

In the digital age, we are gathering data at unprecedented rates. Across almost all areas of inquiry, data (continuous and categorical) are deployed to gain new and deeper insights into fundamental questions. The data revolution also opens new questions in virtually all fields. This minor provides students with a range of skills and perspectives that will help them gather, visualize, analyze, interpret, and tell stories with data in a responsible, just, and ethical way. Coursework will include foundational mathematical principles used in data processing and analysis, and extensive practice with basic and applied coding in open-source computing languages. Students will explore the nature of data and how it is gathered across a variety of disciplines. They will consider the biases and limitations of data. The minor capstone is a semester-long individual project in which students will build a web application in their field of expertise to allow users to query, visualize and analyze an underlying dataset. Students will document their workflow in a digital portfolio suitable for linking to their resume. The Data Analytics minor complements a wide range of academic foci in the natural and social sciences, and in some areas of the arts and humanities.

Mission Statement

The Data Analytics minor will equip students to evaluate gathered data and the biases, limitations, and power structures encoded in all data; develop questions, apply data, and tell stories using data; and code in open-source languages to clean, transform, model, visualize, and present data.


Data Analytics Minor

Interdisciplinary, 6 courses
DATA 101; DATA 127 or MATH 131 or equivalent; a disciplinary statistics course chosen from the following list: BIOL 212, ECON 202, ENV/GEO 207, GEO 107, HIST 200, PSY 201, or SOC 212; DATA 227; DATA 251; and DATA 353. All courses for the minor must be taken for letter grades. A grade of C- or better is required to count a course toward the minor and to have it serve as a prerequisite for subsequent coursework. A maximum of one transfer course may be applied to the minor.

Course Descriptions

DATA 101 Introduction to Data Analytics  Introduction to Data Analytics introduces students to answering questions with large datasets. We explore data types, obtaining data, integration, management, visualization, and examples of data modeling. We will also explore questions of data privacy, the ethics of collecting, storing and manipulating data, and the specter of bias. Students will also begin to acquire fluency in the R statistical computing language and will fine tune professional skills including effective communication, presentation, and storytelling with data. Students will develop a working knowledge of data analytics through hands-on projects and case studies in a variety of domains. Class sessions will be a combination of lecture, demonstration, independent coding work, and group collaboration. This introductory course is open to all students interested in the applications of data analytics and is the first course in the Data Analytics minor. The course partially satisfies the quantitative reasoning goal. (Staff, offered each semester)

DATA 127 Mathematical Foundations of Data Analytics  DATA 127 covers the key mathematical tools for data analytics and other quantitative fields. Topics covered include limits, derivatives, definite integrals, optimization, matrix algebra, and vector spaces. A special emphasis is placed on practical applications in the interpretation of large data sets. Students will explore the uses of these mathematical tools through computer coding. Prerequisites: (1) Math 100 with a grade of C- or higher or a score 15 or higher on the Math Placement Test; (2) DATA 101 with a grade of C- or higher, a declared Data Analytics minor, or permission of the instructor. DATA 127 substantially fulfills the Goal 3 (Quantitative Reasoning). (Staff, offered annually)

DATA 227 Probability for Data Analytics  DATA 227 covers the key mathematical tools for data analytics and other quantitative fields. Topics covered include sets and relations, combinatorics, discrete probability, random variables, probability distributions and the Central Limit Theorem. A special emphasis is placed on practical applications in the interpretation of large data sets. Students will explore the study, interpretation and visualization of probabilistic information through computer coding. Prerequisites: DATA 101; DATA 127 or MATH 131 with a grade of C- or higher, or permission of the instructor. DATA 227 substantially fulfills the Goal 3 (Quantitative Reasoning). (Staff, offered annually)

DATA 251 Data and Context  Data matter! But how do we build datasets? How do the data from experiments and observational research differ? How are data from different studies aggregated to perform meta-analysis? What choices do we make to group, merge, collapse, link, and represent abstract concepts with data? How do we cope with missing data? What information do we gain or lose by making these choices? How do we answer questions once we've constructed and cleaned our data? How do these choices alter the stories we tell with the data that we've collected? This course will explore these questions with real-world applications from a variety of disciplines. Our focus will be on the ethics and consequences of the choices we make when working with real-world data sets. Prerequisites: DATA 101, DATA 127, and DATA 227 with a grade of C- or higher, or permission of the instructor. (Staff, offered annually)

DATA 353 Data Analytics Capstone  The capstone course for the Data Analytics minor centers on client-focused projects. Students will work collaboratively with a “client” to provide an analysis to meet their specific needs. Students begin with a thematic question or problem that can be addressed with a large data set—generally larger than 1,000 records. Students may assemble a dataset as part of the project or use existing data. The question will be drawn from the student’s major academic focus and involve visualization, analytics, and modeling. Each student will produce a web-based Shiny application that will permit users to interact with the underlying data to address the thematic question/problem posed. Class meetings will be structured as workshops to support the student through each stage of the process of developing their question, assembling, cleaning, and linking data, developing a concept for the dashboard, and developing/testing the needed code. Language acquisition will focus on the Shiny package in R. Workshops will encourage collaborative and self-directed problem solving and provide practice giving and receiving peer feedback. At the end of the semester, students will host their Shiny dashboard and a process blog that will describe the student’s development process and workflow. This blog will describe the question/problem and provide context for it. Students will then report on their workflow, highlighting problems encountered and how they solved them. Completed dashboard and blog posts will be hosted publicly so that students can link their work to their resume as a portfolio of practice. Requires DATA 251 with a grade of C- or higher, or permission of the instructor. (Staff, offered annually)