-
Analytics 2
The objective of Analytics 2 is to provide you with a strong background in predictive analytics. We will emphasize learning how to be an intelligent "consumer" of analytics. This, in turn, will help you make effective decisions as a manager using predictive analytics. We will use sophisticated statistical and predictive analytics methods to understand and anticipate the effects of our actions. These methods include confidence intervals, hypothesis testing, A/B testing, multiple regression, and supervised and unsupervised machine learning models such as neural networks and cluster analysis. We will apply these methods to problems from all organizational functions including management & strategy, operations, economics, marketing, finance & accounting, and to a variety of industries. In addition, you will become skilled in performing various analyses via hands-on experience using the R statistical programming language.
-
Fundamentals of Web Programming
This hands-on minicourse will teach the basics of web programming (Javascript, HTML, and CSS). During the course, you will build a web-based e-commerce site from scratch. By the end of the course you will be able to reason effectively about designs for and potential capabilities of web applications. There are no prerequisites for the course.
-
Tools for Improving Operations
This course introduces frameworks and tools designed to enhance operational performance. Our view of operations is very broad in that we explore how firms execute in a variety of contexts (hospitals, quick service restaurants, retailers, hotels, professional service organizations, and more). Our main focus will be to identify simple, powerful approaches to improving operations.
In addition to learning how to identify and execute improvement opportunities, our objectives include:
• Equipping future general managers, consultants, and operations managers with the perspectives and skills needed to effectively use operations as a competitive weapon.
• Exploring new operating models that allow for the optimal use of resources – including the building of sustainable capabilities (e.g. circularity).
• Discovering the power of experimentation and innovation tournaments to identify and measure operational improvement opportunities.
-
Digital Operations
This course explores the transformative impact of digital technologies on operations management across various industries. Students will delve into the strategies, tools, and frameworks essential for implementing and managing digital operations. Key topics include digital operating model frameworks, digital platforms, automation and digital manufacturing, digital twin, cloud computing, and artificial intelligence and machine learning in operations. Through real-world case studies and practical applications, students will gain insights into how digital technologies and digital transformation can enhance operational efficiency, improve customer experience, and drive competitive advantage. The course will culminate in project presentations, allowing students to apply their knowledge to real-world scenarios and receive feedback from peers and the instructor. The course is roughly 70% qualitative and 30% quantitative. Simple linear regressions are needed for completing some assignments and can be implemented in Excel.
Note: This course was formerly titled “Data-Driven Analytics for Innovative Operating Models (DDA).” Students who have already completed that course should not enroll in Digital Operations, as there is considerable overlap in content.
-
Predictive Analytics with Generative AI
This course builds on the core Analytics I & II courses and will primarily focus on predictive analytics methods. This is a short survey course and is best suited for those who want a general understanding of the data analytics process and familiarity with several common algorithms for predictive analytics. We will deepen our knowledge and skills in data analysis and will “get our hands dirty” by preparing data, exploring and then experimenting with a variety of predictive models. We will prepare data by combining data sets, dealing with missing data and detecting outliers. Exploration will include tabular output and data visualization. For predictive modeling we will learn tools such as classification trees and logistic regression, among others.
The default tool for data analysis in this course will be the Python programming language. We will assume no prior coding experience and will use Generative Artificial Intelligence (GenAI) to write the Python code. Therefore, this course will also explore the potential and limitations of GenAI for data analytics. We will discover how GenAI may embed certain assumptions about the data into the code and will make other assumptions – often not obvious to the user – about how to assess the performance of statistical models.
This course was formerly titled Data Mining for Business Analytics (DMBA).
-
AI-Driven Analytics and Society
The last decade has brought us tremendous advances in the power and sophistication of data- driven decision-making techniques at our disposal. Encouraged by this progress, we are witnessing a broad deployment of these techniques across the world. They now touch on and sometimes even govern just about every aspect of how businesses, and even people, operate on a day-to-day basis.
However, as much as these techniques were deployed with the promise of bringing a decisively positive change, it has become abundantly clear that they often are a mixed blessing, at best. Indeed, it turns out that the interface of algorithmic decision-making and society is rife with subtle and non-obvious interactions, undesirable feedback loops, and unintended consequences. How should we make sense of and navigate these issues?
The goal of this seminar is to survey some of the key challenges emerging in the context of the societal impact of data-driven decision making, as well as to create a forum where the students can discuss potential approaches to addressing these challenges. We will aim to discuss questions such as: How should businesses responsibility use, interpret, and make decisions from the output of machine learning models? How can we ensure big data sets are unbiased, fairly labeled, and being used responsibly by companies? What challenges will we face as businesses shift toward using generative AI and large language models for operations such as online customer service? Why does fake news spread faster than truthful news on online platforms like social media, and how do we combat it? Will automation replace unskilled labor, or simply make workers more productive?
Intense student involvement in both the presentation and the class discussion of the scientific papers is required. Like all RTP seminars, there will be a focus on using academic research to help you learn the analytical rigor to be an effective manager in an increasingly complex world.
This course meets the Ethics and Social Responsibility (ESR) requirement.
-
Management of Service Operations
In today's dynamic business landscape, the effective management of service operations is crucial for sustainable success. Building upon foundational knowledge from first-year MBA operations management courses, this elective explores some of the unique challenges inherent to service-based industries connecting to concepts learned in marketing and human resources. We will use an operational lens to understand how companies achieve and sustain service excellence discussing key topics such as customer behavior, change management, employee engagement, and cultivating a learning culture. Taking a hands-on approach, we will use the case method to examine real-world challenges faced by companies in healthcare, financial services, hospitality and others, and will engage with current businesses to enhance learning. Students will develop critical skills to improve service delivery processes, enhance customer satisfaction, and drive operational excellence in service-driven environments.
-
Optimization Modeling for Prescriptive Analytics
This mini course expands on the core Analytics sequence through a deeper dive into the art of modeling, centered around essential tools in optimization. The goal of the course is to extend beyond predictive analytics and to develop richer prescriptive models for optimizing business operations in various marketplaces. We will focus on the modeling of prescriptive questions in a way that can be simply stated, solved efficiently, and whose insights can be communicated clearly.
Every class will be entirely “hands on” with an emphasis on active problem solving for more value-added learning. Each week will focus on a central topic in optimization, with the first session of each week centered around modeling techniques and the second session putting these skills into action with a data-driven case. Throughout the course we will focus on both classical management problems, such as production planning and commodity shipping, as well as modern operational problems, such as COVID-19 testing, cloud resource allocation, premium ride-hailing services, and personalized advertising through generative AI.
Topic wise, the course first provides an overview of the major types of linear programs, of the sort featured in the core, proceeding to more general models of strategic planning and control. Next, the course examines network models, including familiar problems such as optimal transportation routing (``special'' network models) as well as general network-flow models. Then, we cover the formulation and solution of optimization models with logical constraints. We conclude by tackling non-linear models and analyzing algorithms for heuristic solutions to optimization models.
-
VBA Programming
This minicourse teaches the fundamentals of computer programming using MS Excel's macro language, Visual Basic for Applications (VBA), as the language of instruction. The course starts by teaching students to simplify and extend code generated by Excel's macro recorder, and then builds on that base toward developing applications that analyze information and enhance decision making. Special attention will be given to mastering good programming style and building a solid base to continue learning.
-
Professional Decision Modeling
This minicourse extends core Analytics with a series of weekly case assignments using Excel and (to a limited extent) Tableau. The first case blends data analysis and spreadsheet modeling to examine global energy and development. The second case evaluates a Tuck alum’s investment, subsequent development, and recent sale of a small hydropower generation asset in Scotland. The third case builds a structured finance cash flow waterfall to generate insight into mortgage asset securitization risk. The fourth case uses Excel's PowerQuery to organize U.S. wine industry survey data with the aggregate results analyzed with pivot tables and pivot charts to create a comprehensive survey report. Students choose most weeks whether to work as individuals or in teams (but with intermediate, individual deliverables) to design and build spreadsheet models, analyze results, and advise management.
-
Supply Chain Management
A supply chain is comprised of all parties involved, directly or indirectly, in fulfilling a customer demand. The integrated management of this network is a critical determinant of success in today’s competitive environment. Companies like Amazon, Inditex, Intel, Johnson & Johnson, Lenovo, and Walmart are proof that excellence in supply chain management is a must for financial strength and industry leadership.
With increasing competition around the globe, supply chain management is both a challenge and an opportunity for companies. Hence a strong understanding of supply chain management concepts and the ability to recommend improvements should be in the toolbox of all managers. The objective of this course is to introduce you to the key concepts and techniques that will allow you to analyze, manage and improve supply chain processes for different industries and markets. At completion of this course, you will have the skills to assess supply chain performance and make recommendations to increase supply chain competitiveness.
The course covers a wide range of supply chain topics including supply chain network design, inventory management, strategic sourcing, supply chain contracting, and supply chain disruptions. Each topic will be discussed using a combination of models, case discussions, and readings. We will use a data-driven approach where tools and analysis start with realistic data.