Anton Ovchinnikov, Visiting Professor of Technology, Operations and Decision Sciences

The abundance of data revolutionizes many industries, and creates new, data-intensive business models. To take advantage of this trend, today’s executives need to be more comfortable with “data science” – an emerging discipline that combines data analytics and business, as well as modern machine learning methods that provide powerful business solutions. The goal of this course is to build your capability in data science so that you can effectively add value through the intelligent management and use of data in your organizations.

The course will combine four key elements: (introduction to) the process of data analytics, (introduction to) various machine learning and AI methods, business applications, and basic coding/programming (in R, one of the leading open-source tools for analyzing data that you will be able to use in your jobs). The emphasis will be not on the technicalities or theory, but rather on applications to various business cases in finance, marketing, and operations, among other disciplines.

This class is a follow up class to your required core class Uncertainty, Data and Judgment (UDJ). UDJ ended with regression for forecasting and decision making. In this course we will continue with more tools for data science that are widely used in business. A pre-requisite for the course is the material covered in UDJ. No prior coding experience is required: for most classes you will receive a starter code, by running and modifying which you will learn analytics techniques and coding principles, and which you will also be able to use in your jobs. Because of that, much of the course will be in a form of a “hands-on” workshop; you will be expected to bring your laptop to class (with all the necessary software tools installed) and actively participate in the learning process.

Learning Outcomes:
• Understand key principles for analysing data and managing analytics projects;
• Learn to better identify new business opportunities for data analytics, machine learning and AI, and the specific strategies for extracting business value from data
• Get introductory understanding of several cutting-edge machine learning and AI techniques: generalized linear models (logistic regression), CART, random forests, methods for segmentation and clustering, and neural networks
• Get an introductory exposure to coding (in R) which you will be able to scale-up in your jobs
• Get an introductory understanding of data science, “data scientists”, and how to work with them
• Go over several use cases for advanced analytics in various functional settings such as customer analytics (e.g., predicting churn), fin tech (e.g., predicting defaults), mergers and acquisition (e.g., valuing startup’s customer base), and others.

The course is built around specific business cases that we will solve in a step-by-step approach, while getting introduced to the topics above. This is not a course to become “data scientists” or even to become “experts in analytics”. The goal is to familiarize participants with what is available and possible for analytics, and to become conversant in the basic terminology. It is meant to be a starting point.