The list below offers a representative sample of the courses you can expect in the study of business analytics at DePauw. From theoretical foundations to practical experiences, these courses provide a full range of educational opportunities at various levels of mastery. For more information about current course offerings or registration details, please consult the Office of the Registrar.
A first course in applied business analytics that assumes no prior experience in the field. Explores uses of business analytics and ways to successfully use analytics in business decisions, including ethical aspects of data analysis. Focuses on gathering, organizing, and describing information. May include introductory topics such as data visualization and interpretation through use of simulation, case studies, and guest speakers. The course will include content from each of the four specializations in the Business Analytics major at DePauw: mathematics, computer science, financial analytics, and business & economics. Prerequisites: None.
Social Science
None
1 course
110A: Gateway to Business Analytics
Professor: Humberto Barreto
Waitlist for BUS 110 - https://tiny.cc/bus110
110B: Gateway to Business Analytics
Professor: Yanchao Yang,
Humberto Barreto
Waitlist for BUS 110 - https://tiny.cc/bus110
110C: Gateway to Business Analytics
Professor: Vince Aguirre,
Humberto Barreto
Waitlist for BUS 110 - https://tiny.cc/bus110
110A: Gateway to Business Analytics
Professor: Humberto Barreto
Waitlist for BUS 110 - https://tiny.cc/bus110
110B: Gateway to Business Analytics
Professor: Amy Eremionkhale
Waitlist for BUS 110 - https://tiny.cc/bus110
110C: Gateway to Business Analytics
Professor: Yanchao Yang,
Humberto Barreto
Waitlist for BUS 110 - https://tiny.cc/bus110
An off-campus Extended Studies course on business analytics.
1/4-1/2 course
An on-campus Extended Studies course on business analytics.
1/4-1/2 course
An exploration of selected topics in business. May be repeated for credit with different topics.
1 course
190A: Tps:GIS Basics Across Disciplines
Professor: Beth Wilkerson
A basic introduction to the concepts and tools of Geographic Information Systems (GIS) with a focus on how these tools can be applied across various disciplines. Students gain a foundational understanding of basic GIS concepts including spatial data analysis, mapping techniques, and data management. The course emphasizes practical applications by integrating GIS tools and techniques into diverse fields such as environmental science, business, public health, social justice, etc.
A seminar focused on a theme related to the study of business analytics. Open only to first-year students. Does not count toward the major or into the major GPA.
1 course
An intermediate course in business analytics for students who have completed a statistics course. Develops data management, programming, and analytical skills to guide business decision-making. May cover tools such as Python, R, Julia, and Tableau and topics such as LASSO, random forests, and spreadsheet models. Prerequisites: A statistics course (choose from: MATH 141 or PSY 214 or ECON 350 or BIO 375) and BUSA 110 or consent of the instructor.
Science and Mathematics
A statistics course (choose from: MATH 141 or PSY 214 or ECON 350 or BIO 375) and BUSA 110 or consent of the instructor.
1 course
210A: Business Analytics II
Professor: Amy Eremionkhale
210B: Business Analytics II
Professor: Amy Eremionkhale
210A: Business Analytics II
Professor: Amy Eremionkhale
210B: Business Analytics II
Professor: Amy Eremionkhale
The course surveys fundamental principles of risk, the risk management process, and insurance as a systematic approach to transfer and finance risk. It examines how insurance offers protection against major risks that firms and individuals face, how the insurance market is structured, and how and why the industry is regulated. This course also delves into theories and philosophies that provide insights into how the risk management industry functions in the larger society. Emphasis is placed on understanding that insurance is just one of the techniques to be relied upon in planning a comprehensive risk management program.
Social Science
1 course
240A: Principles of Risk Management and Insurance
Professor: Zhixin Wu
This course uses microdata (from the Current Population Survey) to explore inequality in the distribution of income and wealth in the United States. It is grounded in numbers and data analysis, but we will also study philosophical arguments (e.g., Rawls and Nozick) and theories about inequality. We will focus mainly on differences between rich and poor, but also examine racial, gender, health and other gaps. Prerequisite: Elementary statistics (such as ECON 350, BIO 275, MATH 141, MATH 247 or PSY 214) or consent of the instructor.
Science and Mathematics-or-Privilege, Power And Diversity
Elementary statistics (such as ECON 350, BIO 275, MATH 141, MATH 247 or PSY 214) or consent of the instructor
1 course
250A: Inequality via Analytics
Professor: Humberto Barreto
An introductory course on creating graphical representations of data in Tableau. Emphasizes both static graphics and animations that clarify complex situations and support data-driven decision-making. Includes basics of data cleaning and preparation using spreadsheets. Applications in business will be included. Other applications may include analyzing voting patterns, financial data, demographic trends, climate data, and public health policy. Prerequisites: None.
1 course
260A: Data Visualization in Tableau
Professor: McKenzie Lamb
This course focuses on the critical role of social media analytics in driving business analytics. Students will learn about the principles and practices of social media analytics and how to leverage social media data to inform business strategy and decision-making. The course will cover various topics, including data collection and analysis, social media platforms, and tools and techniques for social media analytics. Throughout the course, students will develop the skills to effectively communicate their findings to others and make data-driven recommendations for business analysis. They will also be exposed to case studies of businesses that have successfully used social media analytics to drive strategic planning and decision-making. Finally, they will be encouraged to think critically about the ethical and social implications of social media analytics in business analysis. Prerequisites: None.
Social Science
None
1 course
285A: Social Media Analytics
Professor: Amy Eremionkhale
Topics are chosen from business analytics topics that extend explorations of content in existing courses or allow exploration of content not duplicated in regular course offerings. May be repeated for credit with different topics. Prerequisites: Open to students by permission of instructor or to those who satisfy prerequisites determined by the instructor.
Open to students by permission of instructor or to those who satisfy prerequisites determined by the instructor.
1/4-1/2-1 course
290A: Tps:Data Visualization in Tableau
Professor: McKenzie Lamb
290B: Tps:Human Nature and Free Market Capitalism
Professor: Erik Wielenberg
Traditional economics seems to assume that human beings have generally stable preferences, that we are well-off to the extent that those preferences are satisfied, and that we always act so as to maximize the satisfaction of our preferences. Behavioral economists argue that this is an inaccurate (or at least incomplete) view of human nature. We will first briefly examine the origins and (some of the) central principles of traditional economics. We will then consider some of the ways that, according to behavioral economists, traditional economics rests on a mistaken view of human nature. Finally, we will draw on ideas from behavioral economics to explore some important ways in which the free market and human nature interact, including: (1) the on-going "obesity epidemic", (2) the impact of American-style free market capitalism on families and children, (3) the rise of "bullshit jobs".
290C: Tps:Ethics and Business
Professor: Tucker Sechrest
The course examines the ways the market impacts our social and political relations and the ways in which our legal institutions constrict and enable the market. Is the market a friend or foe of equality? What kind of freedom does the free market give us? Do businesses have an obligation to support socially desirable ends? Much of the coursework will be dedicated to tying Supreme Court case opinions to classical and contemporary political philosophy.
290D: Tps:AI & Analytics in Business
Professor: Amy Eremionkhale
290A: Tps:The Ethics Project
Professor: Jeffrey Dunn
The highlight of this class is a semester-long, experiential project called the Ethics Project. The idea is simple: Think of something good to do and that adds value to the world. Then do it. To help you implement your project, the Prindle Institute for Ethics will make available to each group at least $600 in funding. This project gives you great freedom to be entrepreneurial, but also great responsibility. At the end you will need to justify the way you spent your time and money. How do you know you added value to the world? Why does it matter? The course content will complement the Ethics Project. In class we will think about different kinds of value, about how values might be measured, and the promise and dangers therein. We will address questions about cooperation and self-interest, as well as foundational questions about the role of business, the role of government regulation, and the role of markets. Thinking about these foundational questions and then implementing the Ethics Project is excellent career preparation. In some jobs, people tell you what to do. But as you advance in your career, you will have jobs where you have to identify what the most important problems are, and then solve them. That is what we will do in this class.
290B: Tps:Ethics and Business
Professor: Tucker Sechrest
An experiential course for students who complete a business analytics internship at an organization outside the University. This course does not satisfy major core or specialization requirements.
1/4-1/2-1 course
This course uses microdata from complex surveys (e.g., the Current Population Survey) and hypothetical data with Monte Carlo simulation to explain regression analysis, interpret results, and answer research questions with data. Special emphasis is placed on understanding sampling variability and the standard error. Excel is used at an advanced level and combined with other statistical software such as Stata or R. Prerequisite: Elementary statistics (such as ECON 350, BIO 275, MATH 141, MATH 247 or PSY 214) or consent of the instructor.
Science and Mathematics
Elementary statistics (such as ECON 350, BIO 275, MATH 141, MATH 247 or PSY 214) or consent of the instructor
1 course
305A: Regression with Microdata
Professor: Humberto Barreto
An advanced course in predictive and prescriptive business analytics for students who have completed a regression course. May include algorithms such as neural networks and support vector machines, and applications such as text mining. Prerequisite: A regression course (any regression course such as BUSA 305, MATH 341, ECON 450) and BUSA 210 or consent of the instructor.
A regression course (any regression course, such as BUSA 305, MATH 341, ECON 450) and BUSA 210 or consent of the instructor.
1 course
310A: Business Analytics III
Professor: Yanchao Yang
310A: Business Analytics III
Professor: Yanchao Yang
(Cross-listed with MATH 331) Dive into the world of financial mathematics and unlock the power of money over time. This course equips students with advanced mathematical skills to navigate complex financial landscapes. Master the art of calculating the time value of money, analyzing investment opportunities, and understanding the intricacies of loans and bonds. Explore yield curves, portfolio management, and asset-liability matching techniques used by financial professionals. Gain hands-on experience with real-world applications in investment analysis, capital budgeting, and risk management. Upon completing this course, students will be able to navigate financial decisions confidently and strategically in an ever-evolving economic landscape. Prerequisite: ECON 100 and MATH 136 or MATH 151 or ECON 375
ECON 100 and MATH 136 or MATH 151 or ECON 375
1 course
331A: Financial Mathematics
Professor: Zhixin Wu
(Cross-listed with MATH 332) This is a problem-solving seminar. The problems discussed in the seminar provide students with a better understanding of the actuarial field by exposing students to the professional application of actuarial science and by providing resources for students taking actuarial exams. Techniques and strategies for solving difficult problems are also introduced in the seminar. The seminar also includes an introduction of financial instruments, the determinants of interest rates, an alternative way to approximate the effect of change in interest rates, and interest rate swaps. This course is of great assistance for students who are preparing for the actuarial exam Financial Math. Prerequisite: MATH 331 or BUSA 331 which may be taken concurrently.
MATH 331 or BUSA 331 which may be taken concurrently.
1/2 course
332A: Seminar in Financial Math
Professor: Zhixin Wu
(Cross-listed with MATH 336) This course in Quantitative Risk Analysis provides students with a comprehensive interdisciplinary foundation in quantitative techniques for financial risk assessment and management. The curriculum encompasses the principles of fixed-income securities, Modern Portfolio Theory (MPT) and advanced topics like the Black-Scholes formula and the Binomial Tree method for derivatives pricing. The course emphasizes practical skills in identifying, calculating, and mitigating risks associated with fixed-income securities, equities, options, and futures. Students will work on projects that simulate real-world scenarios, gaining experience in managing the interest rate risk of fixed-income securities by controlling durations, applying MPT to create efficient portfolios, using stock index futures to manage market exposure, and leveraging stock options to hedge individual stock risks. Prerequisite: MATH 136 or MATH 151 or ECON 375, ECON 100, and either MATH 141 or ECON 350.
Math 136 or MATH 151 or ECON 375, Econ 100, and either MATH 141 or ECON 350
1 course
Topics are chosen from business analytics topics that extend explorations of content in existing courses or allow exploration of content not duplicated in regular course offerings. May be repeated for credit with different topics. Prerequisites: Open to students by permission of instructor or to those who satisfy prerequisites determined by the instructor.
Open to students by permission of instructor or to those who satisfy prerequisites determined by the instructor.
1/4-1/2-1 course
390A: Tps:Machine Learning for Healthcare
Professor: Yanchao Yang
This course covers how machine learning is applied in healthcare. Students will learn about using machine learning for predicting risks, modeling disease progression, making diagnoses, and improving clinical workflows. Topics include causality, interpretability, time-series analysis, and deep learning.
The integrated capstone for the business analytics curriculum with emphasis on cases, research methodology, and writing. Group discussion and criticism of research methods, including ethical considerations. Prerequisite: a major in business analytics or permission of the instructor and BUSA 310. Not open for pass/fail credit. BUSA 480 or BUSA 485 is required of all senior Business Analytics majors.
A major in business analytics or permission of the instructor and BUSA 310.
1 course
480A: Business Analytics Seminar
Professor: John Clarke
Outstanding students in business analytics may complete an intensive independent project in their senior year. The project culminates in a written thesis and a public presentation of their research. The thesis is directed by a Business Analytics faculty member. Thesis proposals must be approved by the program before a student can register for BUSA 485. Prerequisite: Permission of the program. May be taken for 1 semester (1 credit) or in two consecutive semesters (1/2 credit each semester). Not open for pass/fail credit.
Permission of the program.
1 course
Topics are chosen from business analytics topics areas that extend explorations of content in existing courses or allow exploration of content not duplicated in regular course offerings. May be repeated for credit with different topics. Prerequisites: Open to students by permission of instructor or to those who satisfy prerequisites determined by the instructor.
Open to students by permission of instructor or to those who satisfy prerequisites determined by the instructor.
1/4-1/2-1 course
Leveraging the resources of the School of Business and Leadership, the business analytics minor at DePauw is an interdisciplinary program that integrates the expertise of multiple departments to develop the knowledge and skills needed to excel in a rapidly changing world.
