Courses for the interdisciplinary program in data analytics and science at Beloit come from many departments.
Interdisciplinary Course Examples
MATH 110 Calculus I
MATH 115 Calculus II
MATH 175 Linear Algebra
MATH 160 Discrete Math
MATH 205 Math Statistics I
MATH 310 Math Statistics II
CSCI 111 Introduction to Object-Oriented Programming
CSCI 204 Data Structures and Algorithms
CSCI 367 Database Capstone
ANTH 208 Ethnographic Methods
ANTH 240 Quantitative Theory and Technique
ECON 302 Marketing Research Workshop
ECON 303 Econometrics
ECON 251 Quantitative Methods in Economics
POLS 201 Research Methods in Political Science and Health
POLS 207/PSYC 207 Political Psychology of Identity
SOCI 205 Social Statistics
SOCI 211 Research Methods
BIOL 247 Biometrics
PSYC 161 Research Methods and Statistics I
PSYC 162 Research Methods and Statistics II
ART 325 Graphic Design
CPLT 215/WRIT 215 Counting, Writing, Seeing
ENVS 260/JOUR 350/MDST 350 Media and the Anthropocene
Course information found here includes all permanent offerings and is updated regularly whenever Academic Senate approves changes. For historical information, see the Course Catalogs. For actual course availability in any given term, use Course Search in the Portal.
In this course students learn what data work involves, including a discussion of data ethics, and get introduced to popular data tools such as R, Tableau, SQL. Students also learn what a career in data work looks like, and they get to connect with an alumnus/a in data science/analytics to learn more about the field from a practitioner.
Data visualization is the process by which information is displayed in graphical form, to investigate patterns in datasets and communicate results. This course covers methods of data visualization, centering on two areas: data visualization as exploration and data visualization as communication. Students discuss univariate, bivariate, and multivariate comparisons and use multiple programs to generate visualizations. Each student will create a final portfolio project on a topic of their choice. Prerequisite: None, but preference given to data science and data analytics majors.
As the senior seminar in data science and data analytics, this course provides a synthesis of concepts and skills learned by DSDA majors and minors during their time at Beloit. Affiliate faculty in departments across the college discuss the importance and meaning of data in their disciplines. Students complete a senior portfolio showcasing their work in data science and analytics and preparing for post-Beloit education and employment. (CP) Prerequisite: senior standing.
This course discusses several data mining techniques to identify novel patterns from large scale databases that might not be available at the current level of process. Topics related to data processing, data visualization, data exploration, prediction, classification, anomaly detection, association analysis, and clustering are covered. Students work on several projects in order to employ data mining tools and techniques such as decision trees, support vector machine, Bayesian classifiers, and neural networks-mean clustering to solve some problems in the field of data science. Offered odd years, spring semester. Prerequisite: junior standing and Computer Science 204. Recommended: Mathematics 175 and 205.
An introduction to the three types of machine learning: 1) supervised learning, 2) unsupervised learning, and 3) reinforcement learning. Students work individually or in teams on real world datasets from different fields to implement machine learning algorithms/approaches and evaluate their performance, including presentations of work oriented to audiences in the related field. Students study professional, ethical, and social issues related to data science. Python is used as the main programming language in this course. (CP) Offered even years, spring semester. Prerequisite: Computer Science 345.