Business Analytics

Business Analytics concentration information for students enrolled at Babson before Fall 2021:

The phrase “business analytics” refers to the process of using data and analytical techniques to improve decision making in a business environment. To some extent, all business courses teach business analytics. The objective of business analytics concentration is to teach students more advanced skills and techniques applied to discipline-specific and more general business problems.

Sponsored by: Math and Science Division and Operations and Information Management (OIM) Division 

Faculty Contacts: Dessislava Pachamanova and Steven Gordon 

Prerequisites

  • ​​QTM 2000 Case Studies in Business Analytics*

Required Courses

  • OIM 3545 Business Intelligence and Data Analytics* (formerly MIS 3545)

* QTM 2000 is equivalent to QTM 3650. For students who enrolled at Babson before Fall 2013, QTM 3650 counts as one of two required courses for the concentration. To complete the concentration, students who enrolled at Babson before Fall 2013 need to take QTM 3650 (or QTM 2000), OIM 3545 and two (rather than three) electives from the list of electives approved for this concentration.

Elective Courses

Choose three (3) of the following courses:

  • ACC 3546 Accounting Analytics
  • ECN 3620 Econometrics
  • FIN 4510 Corporate Financial Modeling & Decision Tools
  • FIN 4530 Investments
  • FIN 4535 Fixed Income
  • MKT 3510 Marketing Research & Analysis
  • MKT 4506 Marketing Analytics
  • MKT 4530 Digital Analytics 
  • MOB 3573 Supply Chain Management / OIM 3573 Supply Chain Management 
  • OIM 3525 Enterprise 2.0, Building Social Networks (formerly MIS 3525)
  • OIM 3535/MOB 3536 Scaling Lean Ventures (formerly MIS 3535)
  • OIM 3580 Artificial Intelligence in Business (formerly MIS 3580)
  • OIM 3640 Problem Solving & Software Design (formerly MIS 3640) OR
    ENGR 2510 Software Design (Olin College)
  • OIM 3660 Prototyping with Information Technology (formerly MIS 3660)
  • QTM 2600 Linear Algebra
  • QTM 2622 Sports Applications of Mathematics
  • QTM 2623 Programming with R for Business Analytics
  • QTM 3605 Quantitative Analysis of Structural Injustice
  • QTM 3610 Applied Multivariate Methods
  • QTM 3615 Time Series Forecasting
  • QTM 3620 Operations Research
  • QTM 3625 Financial Simulation
  • QTM 3635 Quantitative Methods for Machine Learning
  • QTM 3675 Probability for Risk Management

Suggested Paths

Marketing Analytics

  • MKT 4506 Marketing Analytics
  • MKT 3510 Marketing Research & Analysis

Finance Analytics (Choose two)

  • FIN 4510 Corporate Financial Modeling & Decision Tools
  • FIN 4530 Investments
  • FIN 4535 Fixed Income

Industry Advisers

  • Oliver Bandte, BCG
  • Leon Barsoumian, Havas Media
  • Robert Bry, IBM
  • David Dietrich, EMC
  • Ken LeBlanc, EMC
  • Karl Rexer, Rexer Analytics
  • Dan Vesset, IDC
  • Kim Yohannan, EMC​

Business Analytics concentration information for students enrolled at Babson in Fall 2021 or later:

The phrase “business analytics” refers to the process of using data and analytical techniques to improve decision making in a business environment. To some extent, all business courses teach business analytics. The objective of business analytics concentration is to teach students more advanced skills and techniques applied to discipline-specific and more general business problems.

Sponsored by: Math and Science Division and Operations and Information Management (OIM) Division 

Faculty Contacts: Dessislava Pachamanova and Steven Gordon 

Requirements (8 or more credits in total)

All students who graduate with the Business Analytics concentration acquire advanced skills in two (2) key areas: 1) data management processes and programming concepts related to data collection, validation, and organization, and 2) advanced analytical techniques that enable extracting insights from data and modeling decisions based on such insights: machine learning, statistics, optimization, predictive modeling, forecasting, and visualization. 

Requirement 1: Data Management and Programming Concepts

Choose at least one (1) course from the following. Taking additional courses in this group is encouraged and counts towards satisfying the Elective Courses requirement of the concentration. 

  • OIM 3545 Business Intelligence and Data Analytics (formerly MIS 3545)
  • OIM 3640 Problem Solving & Software Design OR ENG 2510 Software Design (Olin College)

Requirement 2: Advanced Data and Decision Modeling

Choose at least one (1) course from the following. Taking additional courses in this group is encouraged and counts towards satisfying the Elective Courses requirement of the concentration. 

  • QTM 3635 Quantitative Methods for Machine Learning 
  • QTM 3605 Quantitative Methods of Structural Injustice 

Elective Courses (8 credits in total)

Choose two (2) of the following courses:

  • ACC 3546 Accounting Analytics
  • ECN 3620 Econometrics
  • FIN 4510 Corporate Financial Modeling & Decision Tools
  • FIN 4530 Investments
  • FIN 4535 Fixed Income
  • OIM 3545 Business Intelligence and Data Analytics*
  • OIM 3525 Enterprise 2.0, Building Social Networks
  • OIM 3640 Problem Solving & Software Design OR ENGR 2510 Software Design (Olin College)*
  • OIM 3535/MOB 3536 Scaling Lean Ventures
  • OIM 3580 Artificial Intelligence in Business
  • OIM 3660 Prototyping with Information Technology
  • OIM 3690 Web Technologies
  • MKT 3510 Marketing Research & Analysis
  • MKT 4506 Marketing Analytics
  • MKT 4530 Digital Analytics
  • MOB 3573 Supply Chain Management / OIM 3573 Supply Chain Management
  • QTM 2600 Linear Algebra
  • QTM 2622 Sports Applications of Mathematics
  • QTM 2623 Programming with R for Business Analytics
  • QTM 3605 Quantitative Analysis of Structural Injustice**
  • QTM 3610 Applied Multivariate Methods
  • QTM 3615 Time Series Forecasting
  • QTM 3620 Optimization Methods and Applications
  • QTM 3625 Financial Simulation
  • QTM 3635 Quantitative Methods for Machine Learning**
  • QTM 3675 Probability for Risk Management

*If not taken as part of Requirement 1

**If not taken as part of Requirement 2