Detailed Course Descriptions

Foundational Courses

All advanced data analytics courses require a solid foundation in probability and statistics. As a result, you should be completely fluent in the material taught in the following two core courses before enrolling in an advanced course:

DECS 430-5, Business Analytics I

DECS 431, Business Analytics II

Because many of the interesting applications in data analytics are in the field of marketing, Marketing Research is also a foundational course. While this course is technically an elective, students should be fluent in its material to be prepared for more-advanced courses in data analytics.

MKTG 450, Marketing Research

Description: This course provides a fundamental understanding of marketing research methods employed by well-managed firms. The course focuses on integrating problem formulation, research design, questionnaire construction, sampling, data collection, and data analysis to yield the most valuable information. The course also examines the proper use of statistical applications as well as qualitative methods, with an emphasis on the interpretation and use of results.

Competitive Advantage Courses

MGMT 469, Analytics for Strategy

Description: Most strategic decisions businesses make require an assessment of cause and effect. What will happen to prices and sales if I open a new location in a particular geographic area? How will consumers respond if I begin posting the caloric content of my food products at the point of purchase? What is the effect of seasonal bonuses on employee productivity? This course covers in depth the empirical tools that are most valuable for linking cause to effect: regression analysis and field experiments. You will learn how to perform convincing data analyses to answer specific questions, how to evaluate analyses others have done, and how to present data analysis in a clear and accessible way.

MKTG 953, Customer Analytics

Description: Firms are increasingly obtaining data not just about purchase decisions, but also about individual consumers' pre- and post-purchase behavior. But few firms have the managerial and data analytics expertise to act intelligently on such data. For the key marketing problems in customer acquisition, development, and retention, this hands-on course introduces sophisticated data analytics techniques tailored to those problems, including predictive analytics and large-scale testing. Students apply each technique to a large consumer-level database, learning how to target consumers individually and how to derive customer insights

MKTG 462, Retail Analytics, Pricing and Promotion

Description: How does a "sale" sign change customer behavior? How has the Internet changed customer price sensitivity? How has the expansion of retail stores, factory stores, and the Internet changed customer behavior? This data- and analytics-driven course seeks to answer these questions and provides a cohesive framework for studying consumer behavior. Most of the data is from real-world managerial problems, and students learn how to make informed pricing and retailing decisions using data.

MKTG 955, Digital Marketing Analytics

Description: The objective of this course is to develop a strong conceptual and practical understanding of how to use digital data to improve marketing decisions. Students will gain familiarity with key areas of digital marketing including search and display advertising, search engine optimization, and social media marketing. Working with real data sets, students will practice using quantitative tools to improve campaigns, estimate ROI, and gain customer insights.

MORS 910, Sports and People Analytics

Description: In the competitive business world, companies increasingly rely on sophisticated data analytics to make management decisions related to people (e.g., hiring and promotion decisions; team composition; compensation). The purpose of this course is to use data analytics to solve problems in the management of people, focusing specificaly on how such tools are used in the sports industry. Competitive sports helped usher in data analytics given the vast availability of data on individual and team performance. At first only a few teams, like the Oakland A's as depicted in Moneyball, used data analytics to gain a performance advantage, but since those early days, the business of sports has become dominated by data science. In this course, we will use data analytics in sports to better understand how to evaluate and solve real world problems that managers face everyday.

MORS 945, Social Dynamics and Network Analytics

Description: Social Dynamics and Network Analytics covers cutting edge research on social media, digital data, and crowdsourcing, and provides you with the tools to practically apply this research in your own career. By the end of the course you will know how to: measure volume and location of Internet search data to understand and forecast trends; measure volume and sentiment of Twitter conversations; collect network data and create meaningful network visualizations; use the wisdom of crowds, including setting up a prediction market, to create better forecasts; and use Amazon Mechanical Turk for crowdsourcing.

Deep Dive Courses

KACI 925-5, Visualization for Persuasion

Description: Be persuasive in presenting your ideas. Learn to convince your clients, customers, and colleagues of the merits of your views, using the latest breakthroughs in cognitive science, computer science, and graphic design. Through interactive exercises, the course will provide hands-on experience and tools for presenting data-based evidence with impact, across images, graphics, and visualizations of big data. Leave this course with expertise in the principles of effective data visualization, as well as a practical toolkit for conveying your ideas in ways that are convincing, catchy, and contagious.

KMCI 930-5, Technology in the Age of Analytics

Description: Technology is inextricably linked with the generation and management of data for use in analytics. This course provides an overview of enterprise and cloud computing technology building blocks and how they enable data analytics. We discuss today's technology infrastructure for data analytics and where that technology seems to be headed. Finally, we touch on the different ways of organizing a data analytics function and what you need to know about foundational data management topics such as data privacy and security. The course helps students obtain a high-level view of the technology landscape and to understand how organizational considerations around technology affects the overall performance of data analytics in organizations.

KMCI 935-5, Programming for Analytics

Description: Analytics systems are software systems. In this course, you'll learn what engineers know about building software that is reliable and accurate, integrates multiple technologies, scales and performs well, and adapts to changing requirements. We'll move beyond simply programming to design of software systems for analytics. Topics will include: typical idioms and design patterns, designing for scalability and change, testing, and performance tuning. Through rigorous examples, students will build the perspective necessary to lead technical teams and avoid costly engineering failures.

Course exercises will be conducted primarily in the Python programming language and an introductory online Python course is a prerequisite.

Experiential Courses

DECS 915, Analytical Consulting Lab

Description: The Analytical Consulting Lab provides real-world learning experiences for students to work with sponsoring companies on business questions that revolve around analytics. Students work in teams to answer current and important business questions using analytics tools learned in other data analytics courses. The course also provides an excellent opportunity to hone consulting skills such as presentation of analytics findings and recommendations, and client-relationship management. Many of the projects will benefit from having taken one or more of the “Competitive Advantage” courses.

KMCI 940-5, Data Analytics Decisions

Evidence-based decisions are no longer a luxury, as firms are capitalizing on the tremendous value that data analytics can bring to their decision-making. These analytic techniques and tools are widely applicable to myriad industries and business contexts. This course will give students the opportunity to practice their existing data analytics skills to solve diverse real-world cases. Students will also deepen their ability to select the appropriate method to solve each problem, clearly and concisely present results, and clearly articulate the strengths and limitations of their analyses.