Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.

In this course, we will explore approaches for structuring and analyzing common business decisions and discuss analytical methods to improve the strategic decisions. This course aims, in part, to improve your analytical skills by gaining insight into risk and uncertainty. As we begin to work with richer datasets, you will use R to gain insights from data. Our ultimate goal is for you to become a data-informed decision maker and business leader.

In this course, students will gain exposure to and practice with the concepts and tools used to leverage data at scale and create value. The concepts and tools covered include data visualization, machine learning, and cloud computing. Through materials designed for the novice, students will be introduced to coding in Python and learn to develop predictive models from large datasets. Students will also learn data visualization in Tableau.

The goal of the course is to introduce you to machine learning, some of the world's most powerful predictive models. The course covers topics such as machine learning algorithms, overfitting, supervised learning, cross-validation, regularization, recursive partitioning, and assembling. You will be exposed to Python and learn to write Python code. In teams, you will enter forecasting competitions to develop predictions using these algorithms.

Teams will solve an analytics challenge from a sponsoring company. The company will provide the data and the problem. You and your team will design a solution in the form of a set of visualizations and a model and assess the business impact in conjunction with the sponsoring company. Key questions: How much money will the proposed solution save? How many new customers will the proposed solution attract? The core deliverable is a presentation.

Business decisions, both tactical and strategic, are frequently made difficult by the presence of uncertainty in the resulting consequences. This course presents a philosophy for framing, analyzing, and proactively managing decisions involving uncertainty, whether the uncertainty results from general conditions or the actions of competitors. The course will focus on making the uncertainty explicit so that it can be objectively analyzed. Prerequisites: Restricted to Darden students.

Business decisions, both tactical and strategic, are frequently made difficult by the presence of uncertainty in the resulting consequences. This course presents a philosophy for framing, analyzing, and proactively managing decisions involving uncertainty, whether the uncertainty results from general conditions or the actions of competitors. The course will focus on making the uncertainty explicit so that it can be objectively analyzed. Prerequisites: Restricted to Darden students.

This course is designed for the student who wants to be optimally prepared to perform quantitative analysis at a level consistent with (and exceeding) expectations for MBA interns in positions where quantitative sophistication is required. Its only prerequisite is the first-year Decision Analysis course; no additional quantitative experience or acumen is required. The course will focus primarily on data analysis, used to both gain useful insights into relationships and make better, more useful forecasts. In addition to more advanced treatment of regression analysis (the goal being for students to be able to build and apply sophisticated regression models), students will become familiar with other common approaches to forecasting, such as rudimentary time-series analysis. Students will also improve their ability to structure, analyze, and manage situations involving uncertainty and risk, using simulation (Crystal Ball), decision trees, and the other tools introduced in the required Decision Analysis course. Finally, the course will introduce students to the concepts of optimization using Excel's Solver add-in, used to determine how to optimally allocate resources in situations involving complex trade-offs.

Immense amounts, granularity of data, the pervasiveness and speed of computing power with mobility make analytics a competitive advantage. Through dialog & conversations will take a closer look at organizations seeking enhanced ability to transform data into actionable insights. Topics intended to span data science, artificial intelligence, machine learning, digitalization, analytics processes & methods, probabilistic forecasting models, and ops research.

New cases provide opportunities to learn how data science is affecting a variety of domains, from entrepreneurship and marketing to operations and finance. In this course, students will gain exposure to the concepts and tools used by managers to create disruptive business models that leverage big data.

Global Business Experience is a one-week course that focuses on business issues in variety of countries outside of the United States. The courses are offered at midterm break in March. Each section offered under the Global Business Experience heading provides the opportunity for students to visit a different country and experience business practices and cultures other than those of their native countries. Both first-year students and second-year students may participate. Based on a unifying theme and a specific geographic location, each course includes structured classes and practitioner presentations as well as visits to companies, governmental agencies, and important cultural sites. Each Global Business Experience course is intended to give students a better perspective on the countries visited and, through comparison, on their country of origin. While the countries may vary from year to year, in the recent past, programs have been offered in Argentina, Bahrain, China, Czech Republic, India, Mexico, Spain, and Sweden.

New cases will provide opportunities to forecast quantities in a variety of domains from operations to marketing to finance. In this course, students will examine big data analytics and tools that have been written about in the public press (web scrapers, SQL, Tableau, R).

A Darden Independent Study elective includes either case development or a research project to be conducted by an individual student under the direction of a faculty member. Students should secure the agreement of a resident faculty member to supervise their independent study and assign the final grade that is to be based to a significant degree on written evidence of the individual student's accomplishment.

For doctoral students.