Analytics for Decision Making

Обучение бесплатное
Сертификация платная
2 часа курса
О курсе

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.

We 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?

This course will be 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.

This course is part of the Business Principles and Entrepreneurial Thought XSeries.

Analytics for Decision Making
Discover the foundational concepts that support modern data science and learn to analyze various data types and quality to make smart business decisions.
Что Вы изучите?
  • Variability in the real world and implications for decision making
  • Data types and data quality with appropriate visualizations
  • 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?)
Rick Cleary
Rick Cleary
Professor and Chair, Division of Mathematics and Science Babson College
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. Prior to coming to Babson College in 2013, Professor Cleary taught at St. Michael’s College in Vermont, Cornell University, Bentley University, and Harvard University. He has held many leadership positions in the Mathematical Association of America, including six years on the Executive Committee as Associate Treasurer and a term as chair of the Joint Data Committee. He is currently on the Nominations Committee and the Polya lecturer selection committee. Professor Cleary enjoys working with mathematics teachers at all levels to improve statistics education and he gives frequent talks and workshops on ways to encourage statistical thinking.
Nathan Karst
Nathan Karst
Assistant Professor of Applied Mathematics Babson College
Dr. Nathan Karst received his B.S. in electrical and computer engineering from Franklin W. Olin College of Engineering in 2007 and his doctorate in applied mathematics from Cornell. He 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.
Davit Khachatryan
Davit Khachatryan
Assistant Professor of Statistics and Analytics Babson College
Dr. Davit Khachatryan is an Assistant Professor of Statistics and Analytics at Babson College. He is an applied statistician with research interests in analyzing intellectual property data to study the formation and diffusion of knowledge in emerging industries. Davit’s current and former research has produced publications in academic, peer-reviewed journals such as Journal of the Royal Statistical Society (Series C), The American Statistician, IEEE Transactions on Engineering Management (forthcoming), and Quality and Reliability Engineering International. Prior to joining Babson College, Davit was a Senior Associate at the National Economics and Statistics practice of PricewaterhouseCoopers (PwC). In the latter role he consulted in the area of predictive modeling and advanced data analytics, helping clients from financial, healthcare, and government sectors with building automatic predictive models and enhancing business intelligence solutions. Davit has earned his B.S. in Applied Mathematics and Informatics from Yerevan State University, M.S. in Statistics and Ph.D. in Management Science from the University of Massachusetts, Amherst.
George Recck
George Recck
Senior Lecturer & Director of the Math Resource Center Babson College

Mr. Recck has taught at Babson College since 1984. He currently serves as the Chair of the Business Analytics/Statistics Education special interest group for the American Statistical Association (ASA). Mr. Recck is also the founder of Total Information, Inc., an consulting firm specializing in providing information service to small businesses.

Babak Zafari
Babak Zafari
Assistant Professor of Analytics and Statistics Babson College
Dr. 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. Prior to joining Babson College, he was a Visiting Assistant Professor at The George Washington University School of Business teaching courses in Data Analysis and Decisions, Business Analytics and Data Mining. He was also a senior statistician consultant at Integrity Management Services where he was responsible for developing statistical models for fraud detection in Medicare and Medicaid programs. He received his B.S. in Applied Mathematics from Sharif University of Technology, M.S. in Operations Research/Computer Science from Bowling Green State University and Ph.D. in Decision Sciences from The George Washington University School of Business.
Эта платформа предоставляет все курсы бесплатно. Авторами выступают топовые университеты и корпорации, которые стараются удерживать стандарты качества. За несоблюдение дедлайнов, невыполнение домашнего задания студенты теряют баллы. Как и в других платформах, лекционные видео чередуются с практическими заданиями. Обучение проводится на английском, китайском, испанском, французском и хинди.