About the course
Want to know how to avoid bad decisions with data?
Making good decisions with data can give you a distinct competitive advantage in business. This statistics and data analysis course will help you understand the fundamental concepts of sound statistical thinking that can be applied in surprisingly wide contexts, sometimes even before there is any data! Key concepts like understanding variation, perceiving relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.
These big picture ideas have motivated the development of quantitative models, but in most traditional statistics courses, these concepts get lost behind a wall of little techniques and computations. In this course we keep the focus on the ideas that really matter, and we illustrate them with lively, practical, accessible examples.
The course will explore questions like: How are traditional statistical methods still relevant in modern analytics applications? How can we avoid common fallacies and misconceptions when approaching quantitative problems? How do we apply statistical methods in predictive applications? How do we gain a better understanding of customer engagement through analytics?
Requirements
This course is relevant for anyone eager to have a framework for good decision-making. It will be good preparation for students with a bachelor’s degree contemplating graduate study in a business field.
Opportunities in analytics are abundant at the moment. Specific techniques or software packages may be helpful in landing first jobs, but those techniques and packages may soon be replaced by something newer and trendier. Understanding the ways in which quantitative models really work, however, is a management level skill that is unlikely to go out of style.
What you will learn
By the end of this course, you will learn:
- Variability in the real world and implications for decision making;
- Data types and data quality with appropriate visualisations;
- Apply data analysis to managerial decisions, especially in start-ups;
- Making effective decisions from no data to big data (what should we collect and then what do we do with all this data?).
About the instructors
Professor Rick Cleary is a statistician and mathematician with research and consulting interests in a variety of fields including sports, biomechanics, and statistical approaches to fraud detection and audit risk.
Dr. Nathan Karst is an avid teacher and researcher, having won the Dean’s Award for Excellence in Undergraduate Teaching in 2014 and the Dean’s Award for Excellence in Scholarship in 2015. Most recently, his research has focused on the role of nonlinear dynamics in microvascular networks and event-scale streamflow recession variability.
Dr. Davit Khachatryan is an Assistant Professor of Statistics and Analytics at Babson College. He is an applied statistician with research interests in analysing intellectual property data to study the formation and diffusion of knowledge in emerging industries.
George Recck has taught at Babson College since 1984. Prior to that, Mr. Recck worked his way toward his undergraduate degree from Babson as a Systems Manager in Babson's Computer Centre.
Babak Zafari is an Assistant Professor of Analytics and Statistics in the Math & Science Division. His area of interests are Predictive Modeling and Data Mining Methods for Business Applications, Bayesian Statistics, Healthcare Fraud Analytics and Online Auctions.
Duration: 4 weeks, 4-6 hours per week
Language: English
Institution: Babson College
Instructors: Rick Cleary, Nathan Karst, Davit Khachatryan, George Recck, Babak Zafari
