Applied Business Analytics - Harvard Business School MBA Program

Applied Business Analytics

Course Number 2143

Visiting Associate Professor Yael Grushka-Cockayne
Visiting Professor Michael Parzen
Spring; Q3Q4; 3 credits
28 Sessions (split between hands-on lab sessions and case discussion sessions)
Final Team Project

Career Focus

Applied Business Analytics targets students who want to build an understanding of how data and analytics are being used to drive decisions in a variety of industrial and organizational contexts. The course is intended for both novices and students with some exposure to data analytics. Students will learn foundational data science concepts, tools and techniques and will have hands-on experience using the open source software R to analyze managerial problems. The course will focus on business applications, including a manager’s role in hypothesis formation, model design, interpretation of results, and formulation of actionable recommendations.
There are no prerequisites for the course. The course will review, and build upon, some material covered in HBX CORe Business Analytics course. Because there are no prerequisites, “starter code” will typically be provided to ensure that all students, regardless of background, are able to conduct appropriate analyses. The starter code provided will also facilitate student learning.

Educational Objectives

Understanding statistics and modern computing methods is a great asset, but creating the most value from this asset requires knowing how to ask and answer the right questions. Choosing the right question and solving a problem appropriately require a deep understanding of the business context as well as familiarity with the subtleties of working with data and applying statistical methods. First, one must understand the business context from a business model or operating model perspective. Second, one must figure out how to use data and analytics to help inform the solution to the problem. Finally, managers and leaders must develop the capability to communicate insights from the analysis to the various stakeholders.

Content and Organization

The course consists of two types of class sessions:
1) Laboratory sessions in which students practice analyzing assigned problems, and
2) Class sessions with case discussions.

Students will learn foundational course concepts using the Harvard Business Analytics Program (HBAP) on-line platform. Teaching fellows will be available at each lab session to answer questions and provide both technical and conceptual support. Teaching fellows will also hold office hours outside of class

Module 1 
During the first module, students will work through online HBAP material covering the basics of R, data analysis, study design, statistical inference, linear regression and logistic regression. Module 1 consists of eight units of on-line course material. There will be two class sessions associated with each unit: one lab session and one case discussion. Module 1 will end with a midterm that covers basic Module 1 concepts.

Module 2
The remainder of the course will cover additional tools and techniques that are most widely used in practice, such as SQL data queries, visualization with tableau, and regression trees. If time permits, it will provide an introduction to concepts like natural language processing and neural networks. As in Module 1, the course sessions will alternate between lab sessions and class discussions.

Course Pedagogy
The classroom sessions will utilize a problem-solving strategy that gives students a clear outline for solving any data science business problem. Emphasis will be put on communication, rigor in analysis, ethical reporting of results and dealing with messy issues such as missing data, nonresponse of participants and study design. Throughout the course, reproducibility of results will be emphasized, and the students will build a portfolio of cloud-based projects in R.

The course grade will be based on class participation, assignments, a midterm and a final team project. (Note: the course midterm is intended primarily as a self-diagnosis for students to assess their learning and hence will comprise only 10% of the course grade.)