Courses ofInstruction
Data Analytics
Program Faculty
Kendralin Freeman, Associate Professor, Sociology
Jonathan Forde, Professor, Mathematics and Computer Science, Chair
Rob Beutner, Director, Digital Learning Team
Jim Capreedy, Associate Professor, Greek and Roman Studies
Nan Crystal Arens, Professor, Geoscience
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.
Offerings
Data Analytics Minor
Interdisciplinary, 6 courses
Requirements:
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 251; DATA 271; 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) MATHd 100 or MATH 130 with a grade of C- or higher or a score 20 or higher on the Math Placement Test. DATA 127 substantially fulfills the Goal 3 (Quantitative Reasoning). (Staff, offered each fall) [Prerequisite: MATH 100 with a grade of C- or better, or a score of 20 or higher on the Math placement test.]
DATA 211 Statistics for Everyone The course is intended for students from any discipline interested in learning how to use data to help draw conclusions about ideas. Students will learn about what makes a good research question, different types of study designs used in the sciences and non-sciences alike, and basic strategies for collecting high quality data. Students will learn basic principles of causal inference, using graphical causal models to clearly articulate research questions and inform aspects of study design and data analysis. Basic principles of probability will be introduced as a tool for quantifying uncertainty in all aspects of data analysis. Students will be introduced to both frequentist and Bayesian approaches to estimation, providing a balanced perspective on parameter inference, hypothesis testing, and uncertainty quantification. General linear models will be introduced as a flexible tool to test hypotheses with data. Students will gain practical skills in R for organizing and analyzing data largely in the context of statistical analysis. Quantitative analysis and reasoning are emphasized throughout the course, but this is not a science or mathematics class, and students only need a background in basic algebra to succeed.
DATA 251 Exploratory Data Analysis and Visualization 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. (Staff, offered each fall) [Prerequisite: DATA 101, and DATA 127 or MATH 131 with grades of C- or better.]
DATA 271 Probability and Modeling for Data Analytics DATA 277 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. DATA 277 substantially fulfills the Goal 3 (Quantitative Reasoning). (Staff, offered spring semester) [Prerequisite: DATA 101, and DATA 127 or MATH 131 with grades of C- or better.]
DATA 353 Data Analytics Capstone This capstone course for the Data Analytics minor centers on a client-focused project. Students will work in teams and collaboratively with a 'client' to produce an app that provides visualizations and analysis to meet the client's needs. Students will experience the complete data science life cycles as they develop domain knowledge, assemble or gather data, clean, visualize and explore it, model, test and present a product to the client. Throughout the process, students will reflect on the ethical concerns embedded in their project and propose safeguards. Students will build programming skills in Shiny and SQL queries. (Staff, offered each spring) [Prerequisite: DATA 101, DATA 127, DATA 251, and DATA 271.]
DATA 495 Honors