In the Business + Data Science major, you learn about business on a broad scale while also focusing on one of four specializations within the major. Paired with the data science curriculum, the Business + DS major can prepare you for many careers that allow you to manage and interpret datasets, helping you make informed decisions to improve business.

Specializations

As a student in the Business + DS major, you choose from four specializations, offered by the Department of Business Administration. You can explore courses from more than one specialization, but you must complete the coursework for one entire specialization to earn your Business + DS degree. 

Information Systems

With an information systems education, you prepare to design and implement technology that businesses can use to organize data and make decisions. By adding data science to the mix, the information systems specialization in Business + DS can help you go a step further – so you can analyze and interpret the data you’re helping to organize. 

International Business

As a student specializing in international business, you prepare for the opportunities, challenges, and complexities of global business while also learning about data science. This will help prepare you to use data to make decisions on a global scale. 

We also offer an international business minor, which requires two extra courses. If you complete the two additional advanced courses, you’ll be eligible for both the minor and the Business + DS degree with the international business specialization.

Management

With the management specialization, you prepare to become a leader and innovator, learning more about leadership, planning, organization, and other valuable skills for management positions. Plus, you get the data science knowledge you’ll need to use data effectively in a managerial role.

Operations Management

With the operations management specialization, you learn skills for developing, making, and delivering goods and services by studying analytical decision-making, logistics, quality control, and supply chain management. With the data science component, you combine this knowledge with learning how to use data to inform your actions and decisions.

Learn from renowned faculty 

Our reputation for excellence in research and education is rooted in our long-standing commitment to engaging top-notch academics and thought leaders in conversations about timely topics that move business administration forward. The Department of Business Administration faculty consistently engage in symposia, lectures, workshops, and other events, inviting speakers from other universities and programs to benefit students, faculty, and the broader community.

Business + DS Core Courses

Business Core
Data Science Core

Calculus: Fulfilled by MATH 234 (MATH 220 or 221 can also be used) 
First course in calculus and analytic geometry; basic techniques of differentiation and integration with applications including curve sketching; antidifferentation, the Riemann integral, fundamental theorem, exponential and trigonometric functions. 

Linear Algebra for Data Science: MATH 227
Linear algebra is the main mathematical subject underlying the basic techniques of data science. This course provides a practical computer-based introduction to linear algebra, emphasizing its uses in analyzing data, such as linear regression, principal component analysis, and network analysis. We will also explore some of the strengths and limitations of linear methods. Students will learn how to implement linear algebra methods using Python, making it possible to apply these techniques to large data sets. The course assumes an introductory knowledge of Python, such as students acquire in STAT 107.

Data Science Discovery: STAT/CS/IS 107
Data Science Discovery is the intersection of statistics, computation, and real-world relevance. As a project-driven course, students perform hands-on-analysis of real-world datasets to analyze and discover the impact of the data. Throughout each experience, students reflect on the social issues surrounding data analysis such as privacy and design. 

Data Science Exploration: STAT 207
This course explores the data science pipeline from hypothesis formulation, to data collection and management, to analysis and reporting. Topics include data collection, preprocessing and checking for missing data, data summary and visualization, random sampling and probability models, estimating parameters, uncertainty quantification, hypothesis testing, multiple linear and logistic regression modeling, classification, and machine learning approaches for high dimensional data analysis. Students will learn how to implement the methods using Python programming and Git version control. The course assumes an introductory knowledge of statistical concepts and Python, such as students acquire in STAT 107.

Modeling and Learning in Data Science: CS 307
Introduction to the use of classical approaches in data modeling and machine learning in the context of solving data-centric problems. A broad coverage of fundamental models is presented, including linear models, unsupervised learning, supervised learning, and deep learning. A significant emphasis is placed on the application of the models in Python and the interoperability of the results. 

Algorithms and Data Structures for Data Science: CS 277
An introduction to elementary concepts in algorithms and classical data structures with a focus on their applications in Data Science. Topics include algorithm analysis (ex: Big-O notation), elementary data structures (ex: lists, stacks, queues, trees, and graphs), basics of discrete algorithm design principles (ex: greedy, divide and conquer, dynamic programming), and discussion of discrete and continuous optimization. 

Ethics and Policy for Data Science: IS 467
Learn about common ethical data challenges, including privacy, discrimination, and access to data. These challenges will be explored through real-world cases of corporate settings, non-profits, governments, academic research, and healthcare. The course will also cover common ethical principles, providing a framework to analyze these cases. Students will also be introduced to a range of policy responses. The course is suitable for anyone who plans to work in a professional setting that will involve handling data, or who is seeking a grounding for future study of data and information ethics. 

Data Management, Curation, and Reproducibility: IS 477
We introduce and use the Data Science Life Cycle as an intellectual foundation for understanding Data Management, Curation & Reproducibility in the Data Science context. The Data Science Life Cycle allows us to study how data, software, workflows, computational environments, scientific findings,and other artifacts form linked foundational components of data science research. Topics include research artifact identification and management, metadata, repositories, economics of artifact preservation and sustainability, and data management plans. 

One of the most important skills a student will gain in a Finance +DS degree will be the ability to present data in meaningful ways. Meaningful research and experience are as much a pillar of this degree program as both the core coursework and the area of specialization. This capstone experience will be fulfilled through BUS 301. This course is an active learning, real-client experience that will allow students to join their data science skills with their business skills.

BUS + DS Core

Choose one of the four specializations below.

Information Systems Specialization

International Business Specialization

Management Specialization

Operations Management Specialization

Rewarding careers and successful outcomes

The number of data science roles is projected to increase rapidly this decade as companies seek employees who can make informed decisions backed by data, according to the US Bureau of Labor Statistics. 

With a Business + DS degree, you can prepare to meet that demand in areas like the following and many more:

  • Sales forecasting
  • Systems analysis
  • Responsible spending and forecasting
  • Browser design and data analysis
  • Leading teams of data analysts
  • Data security
  • Sport data analytics

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