An introduction to the essential tools and techniques of data mining/machine learning. Students will learn (1) how to execute the key steps in the data mining process - including data preprocessing, data exploration and visualization, supervised and unsupervised learning, model selection and validation, and complexity control; and (2) how to build reliable predictive models. The data mining methods covered are Linear Regression, Logistic Regression, K-nearest neighbors, Classification and Regression Trees, Ensemble methods, K-Means and Hierarchical Clustering, and Association Rules. The focus will be on business applications - realistic data from Marketing, Finance, Operations, and other functional areas will be used to illustrate the breadth of applications.

Causal inference is required to answer diverse substantive questions: How does the demand for a product change when the price does or the online ratings do? How can we measure whether introducing technology in schools, universities and/or classrooms affects the performance of students? How will different IT-enabled features on digital platforms impact user behavior, engagement, and purchase? Many companies and governmental agencies ask and try to answer these questions of this type -which require causal inference--every day. This course introduces the fundamentals of causality and the appropriate language to ask and answer causal questions in a business context. The course materials focus on the ideas and on the intuition behind the fundamental tools used to measure causal effects, some hands-on work to get exposure to implementing these methods, as well as the ethical and regulatory issues around experimentation.

In this course we discuss emerging technologies which have the potential to significantly impact various industries. We will broadly study new technologies and the managerial, economical, and societal impact that they may pose from different viewpoints. These may include the managerial, organizational, industry or country level. We may also discuss technologies and their impact as a disruptive innovation through case studies. Central to the class will be an emerging technology that we will discuss at a deeper level. For example, this class may discuss the technical foundations for Blockchain or the hands-on applications of augmented reality (AR) and virtual reality (VR).

Introduces students to scientific paradigms and research perspectives relating to the changes brought about by digitization and digital technologies. Digital technologies have fundamentally transformed businesses, markets, and societies. Businesses that build on emerging digital and information technologies have several unique and interesting characteristics that set them apart from traditional businesses. Examining the fundamental characteristics of these digital businesses, platforms and ecosystems will help us understand the transformative potential of digital technologies and their impacts on the structure and competitive dynamics in various sectors. This course is designed to foster an in-depth understanding of fundamental concepts relating to the digital economy and will explore a series of research questions about the consequences of these developments for consumers, organizations, and society at large.