• 数学110微积分I
  
• 数学115微积分II
  
• 数学175线性代数
  
• 数学160离散数学
  
• 数学205数学统计
  
• 数学310数学统计II

• CSCI 111 Introduction to Object-Oriented Programming
  
• CSCI 204 Data Structures and Algorithms
  
• CSCI 367数据库顶点

• 民族志方法
• ANTH 240 Quantitative Theory and Technique
• ECON 302 Marketing Research Workshop
• 经济学303计量经济学
• ECON 251 Quantitative Methods in Economics
• POLS 201 Research Methods in Political Science and Health
• POLS 207/PSYC 207 Political Psychology of Identity
• 205社会统计
• 社会学211研究方法

• 生物计量学
• PSYC 161 Research Methods and Statistics I
• PSYC 162 Research Methods and Statistics II

• ART 325平面设计
• ART 280以人为本的设计
• CPLT 215/WRIT 215 Counting, Writing, Seeing
• engl265数据叙述
• ENVS 260/JOUR 350/MDST 350 Media and the Anthropocene

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, 表, 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 数据可视化, centering on two areas: 数据可视化 as exploration and 数据可视化 as communication. 学生讨论单变量, 二元, 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, 数据可视化, 数据探索, 预测, 分类, 异常检测, 关联分析, 和聚类. Students work on several projects in order to employ data mining tools and techniques such as decision trees, 支持向量机, 贝叶斯分类器, and neural networks-mean clustering to solve some problems in the field of data science. Offered odd years, spring semester. Prerequisite: junior standing and 计算机科学 204. Recommended: Mathematics 205 and 275.

Special topic in data science/analytics as chosen by instructor. Depending on the instructor and course content, 先决条件不同, but normally students should have finished at least CSCI 111 “Intro to Programming”, and one intro-level statistics or quantitative methods course.

An introduction to the three types of machine learning: 1) supervised learning, 2)无监督学习, 3)强化学习. 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: 数据科学 and 数据分析 345.

Students work on a real data project for a “client” under the supervision and guidance of a faculty member. This project counts as a capstone experience. Prerequisites: at least junior status.

Student works on an independent data project under faculty supervision. 没有先决条件, but ideally the student should have foundational skills in courses such as CSCI 111 “Intro to Programming” and at least one statistics or quantitative methods course.

Student assists faculty in classroom instruction. 分级学分/无学分.