Business Analytics
Business Analytics Program Courses
The list below offers a representative sample of the courses you can expect in the study of business analytics at 91传媒. 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.
Course Description
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 91传媒: mathematics, computer science, financial analytics, and business & economics.
Distribution Area
Social Science
Prerequisites
None.
Credits
1 course
Course Description
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 91传媒: mathematics, computer science, financial analytics, and business & economics.聽
Distribution Area
Social Science
Credits
1 course
Course Description
On-Campus Extended Studies course in Business Analytics.
Credits
0.5 course
Course Description
Student-initiated independent project under faculty guidance. Offered as a co-curricular (0 credit) Extended Studies experience.
Course Description
An exploration of selected topics in business. May be repeated for credit with different topics.
Credits
1 course
Course Description
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.
Distribution Area
Science and Math
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.
Credits
1 course
Course Description
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.聽
Distribution Area
Science and Math
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.
Credits
1 course
Course Description
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.
Distribution Area
Social Science
Credits
1 course
Course Description
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.
Distribution Area
Science and Math-or-Power Privilege and Diversity
Prerequisites
Elementary statistics(such as ECON 350, BIO 275, MATH 141, MATH 247 or PSY 214) or consent of the instructor.
Credits
1 course
Course Description
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.
Credits
1 course
Course Description
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.
Distribution Area
Social Science
Prerequisites
None.
Credits
1 course
Course Description
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.聽This course will help students develop the presentation of logical arguments and refutation; the ability to distinguish and identify important substantive arguments; the ability to skillfully analyze, evaluate, and integrate supporting material; the selection and implementation of effective presentation style; the ability to adapt the manner of delivery to specific audiences and situations; the demonstration of critical listening skills; the demonstration of effective and reflective listening; and the knowledge of the ethical obligations of speakers, discussants, and listeners.
Distribution Area
Social Science
Prerequisites
None
Credits
1 course
Course Description
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.
Credits
1 course
Course Description
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.
Distribution Area
Science and Math
Prerequisites
Elementary statistics (such as ECON 350, BIO 275, MATH 141, MATH 247 or PSY 214) or consent of the instructor.
Credits
1 course
Course Description
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.聽 This course will encourage the logical development of argument, clear and precise diction, and a coherent prose style; the development of expository writing skills as they apply to business analytics; and the responsible, appropriate, and effective use of sources and special or technical language.
Distribution Area
Science and Math
Prerequisites
Elementary statistics (such as ECON 350, BIO 275, MATH 141, MATH 247 or PSY 214) or consent of the instructor.
Credits
1 course
Course Description
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.
Prerequisites
A regression course (any regression course such as BUSA 305, MATH 341, ECON 450) and BUSA 210 or consent of the instructor.
Credits
1 course
Course Description
(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.
Prerequisites
ECON 100 and MATH 136 or MATH 151 or ECON 375
Credits
1 course
Course Description
(Cross-listed with MATH 332) This problem-solving seminar emphasizes professional development in actuarial science and risk management. Students engage with complex actuarial problems, enhancing their understanding of the field through practical applications and exam preparation resources. The seminar introduces financial instruments, explores determinants of interest rates, and examines methods to approximate the effects of interest rate changes. Participants also analyze and discuss actuarial risk management articles. This course is particularly beneficial for students preparing for the Financial Mathematics (FM) actuarial exam.
Prerequisites
MATH/BUSA 331 which may be taken concurrently.
Credits
0.5 course
Course Description
(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.
Prerequisites
MATH 136 or MATH 151 or ECON 375, ECON 100, and either MATH 141 or ECON 350.
Credits
1 course
Course Description
(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.聽This course will encourage the logical development of argument, clear and precise diction, and a coherent prose style; the development of expository writing skills as they apply to business analytics; and the responsible, appropriate, and effective use of sources and special or technical language.
Prerequisites
MATH 136 or MATH 151 or ECON 375, ECON 100, and either MATH 141 or ECON 350.
Credits
1 course
Course Description
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.
Credits
1 course
Course Description
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. This course will encourage the understanding of quantitative and mathematical concepts, representational formats, and methodologies of Business Analytics; the evaluation of quantitative evidence and arguments; the use of quantitative information to make decisions; and the use of problem-solving, laboratory experiments, or projects for deeper learning.
Prerequisites
Open to students by permission of instructor or to those who satisfy prerequisites determined by the instructor.
Credits
1 course
Course Description
This course provides students with a hands-on, project-based experience, where they apply data analysistechniques to solve real-world business problems. Working in teams, students collaborate with industrypartners, using advanced analytics tools to develop ctionable insights and specific recommendations. Students produce written reports and present their updates during the semester, culminating in a final presentation in an open forum that includes faculty and firm stakeholders.
Prerequisites
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 for completion of a Business Analytics major.
Credits
1 course
Course Description
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.
Prerequisites
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.
Credits
1 course
Course Description
New ventures are critical to bringing about societal change as well as driving growth and opportunity in the economy. Venture creation plays a vital role in economic growth because it creates new businesses and expands existing ones, it plays a major role in new job creation and fuels a virtuous cycle of economic development. This course introduces some of the key concepts of the entrepreneurial process and an understanding of the role of key players in the entrepreneurial ecosystem. Students will assess how strategic frameworks can be applied to opportunity assessment and the development of new business models. Students will explore the different stages in new venture creation and the strategies and competencies required to support each stage along with sources of funding. This is a demanding action learning course and students will work in teams on in-class and out-of-class assignments.
Credits
1 course
Contact Us
Business Analytics
Leveraging the resources of the School of Business and Leadership, the business analytics minor at 91传媒 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.
- businessanalytics@depauw.edu
-
Harrison Hall
7 E. Larabee St.
Greencastle, IN 46135