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HomeONGOINGBusiness Analytics for Data-Driven Decision Making

Business Analytics for Data-Driven Decision Making

Course Description

Virtually all managerial and leadership positions in the digital economy increasingly rely on data-driven decision making. Recent studies have shown companies who adopt “Data-Driven Decision Management” achieve significant productivity gains over other firms.

Having a solid grasp of the end-to-end process of making effective decisions with data will give you an edge, both in performing such analyses yourself, as well as in effectively managing teams of business analysts and data scientists.

In this course, part of both the Digital Leadership and Digital Product Management MicroMasters programs, you will learn the tools and techniques to become a data-driven or “evidence-based” manager.

You will learn the process of reframing a business question as a data question, reasoning about what data might be of assistance and how to obtain it, integrating and cleaning the data, performing the analysis, deriving and communicating insights from the analysis, and building the managerial culture to operate in this way and create competitive advantages from enterprise data.

This course is unique in the sense that it aims squarely at the needs of a manager in an analytically focused enterprise by providing both a hands-on introduction to the concepts, methods and processes of business analytics as well as an introduction to the use of analytics as the basis for creating a competitive advantage.

Click here to register

Course Team

John W. Byers

John is Professor of Computer Science at Boston University and Founding Chief Scientist of Cogo Labs, founded in 2005.  He received his Ph.D. in Computer Science from UC Berkeley in 1997 and his B.A. from Cornell University in 1991.

Areas of Expertise:

  • Data Science Analytics
  • Digital Marketing and Electronic Commerce
  • Algorithms

Major works:

  • The Rise of the Sharing Economy: Estimating the Impact of Airbnb on the Hotel Industry, G. Zervas, D. Proserpio and J. W. Byers, To appear in Journal of Marketing Research (JMR), 2017.
  • The Groupon Effect on Yelp Ratings: A Root Cause Analysis, W. Byers, M. Mitzenmacher and Georgios Zervas. Proc. of the 13th ACM Conference on Electronic Commerce (EC ’12),  June 2012.

Connect:

Website: https://www.cs.bu.edu/~byers/

Chris Dellarocas

Chris is Richard C. Shipley Professor of Information Systems at the Questrom School of Business and Associate Provost for Digital Learning & Innovation at Boston University. He received his Ph.D. in Computer Science from MIT in 1996 and his Diploma in Electrical Engineering from the National Technical University of Athens, Greece in 1989.

Areas of Expertise:

  • Online reputation and online reviews
  • Digital Marketing and Electronic Commerce
  • Data Science

Major works:

  • Credit Scoring with Social Network Data. Yanhao Wei, Pinar Yildirim, Christophe Van den Bulte and C. Dellarocas. Marketing Science, 2016.
  • The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias. C. Dellarocas and C. A. Wood. Management Science 54 (3), 2008.
  • Exploring the Value of Online Product Reviews in Forecasting Sales: The Case of Motion Pictures. C. Dellarocas, M. Zhang and N. F. Awad. Journal of Interactive Marketing 21 (4), 2007.

Connect:

Website: https://www.dellarocas.com

Andrei Lapets

Prior to departing Boston University, Andrei was a Research Scientist and Director of Research Development within the Hariri Institute for Computing, Director of the Software & Application Innovation Lab, and a Lecturer within the Department of Computer Science, all at Boston University. He earned his A.B. and S.M. degrees from Harvard University and his Ph.D. in Computer Science from Boston University.

Areas of Expertise:

  • Cybersecurity and Privacy
  • Programming Language Theory and Formal Methods
  • Graph and Network Algorithms

Major Works:

  • User-Centric Distributed Solutions for Privacy-Preserving Analytics, Azer Bestavros, Andrei Lapets, Mayank Varia. Communications of the ACM, 60(2):37-39, 2017.
  • The Filter-Placement Problem and its Application to Minimizing Information Multiplicity. Dora Erdos, Vatche Ishakian, Andrei Lapets, Evimaria Terzi, Azer Bestavros. Proceedings of the VLDB Endowment, 5(5):418-429, 2012.

Connect:

Website: https://cs-people.bu.edu/lapets

Projects

  • COMPLETED (24)
  • ONGOING (29)

Tags

Active Learning Digital Infrastructure Global Connection Lifelong Learning Mentoring & Advising MOOC Project-Based Learning

Course Information

  • Starts:Apr 25, 2017
  • Duration:8 Weeks (Estimated 3-4 hours per week)
  • Register Here

About DL&I

Digital Learning & Innovation is available to help all Boston University faculty and academic leadership. Contact us at digital@bu.edu.

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