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Analytics and Operations

The Business Analytics Concentration

The Analytics and Operations Department offers a concentration in business analytics. This four-course concentration provides students with the tools and technologies to analyze data for business applications. The business analytics concentration includes coverage of software such as SAS (Statistical Analysis Systems), SQL (Structured Query Language), and Python programming language, with applications in business process optimization and machine learning.

Business analytics can only be taken as a secondary concentration by students who have a primary major in accounting or economics (business), or another primary concentration in business administration (accounting, economics, finance, international business, management, marketing).

Requirements:

INFO 201 Data Analysis Software

INFO 301 Advanced Applied Statistics

INFO 302 SQL and Process Optimization for the Business Analyst

INFO 303 Machine Learning for the Business Analyst

In addition to the four required INFO courses in this concentration, students are recommended to take one related course in their primary concentration or major such as:

ACCT 307 Accounting Information Systems

ECON 370 Advanced Econometrics

ECON 372 Advanced Macroeconomics

FIN 461 Cases and Financial Modeling

MKT 423 Marketing Analytics

Courses
INFO 201 Data Analysis Software
Units: 1
Description
Software tools and technologies to analyze data for business and economics applications. Topics include SAS (Statistical Analysis System), SQL (Structured Query Language), and Python programming language.
Prerequisites
BUAD 202.

INFO 301 Advanced Applied Statistics
Units: 1
Description
Regression and simulation methods to solve complex problems in business, society, and the public sector. Selection of the correct statistical technique for the particular problem being solved, running statistical analyses using analytics tools that are commonly used in industry (e.g. SAS, SQL, and Python), and proper interpretation of the results to support data-driven decisions. Topics include regression analysis, limited dependent variable estimation, survival functions, and simulation.
Prerequisites
MGMT 225 and INFO 201.

INFO 302 SQL and Process Optimization for the Business Analyst
Units: 1
Description
Introduces common techniques for relational data management, including conceptual modeling and Structured Query Language (SQL). Additionally covers topics from business process re-engineering, with a focus on process modeling, performance assessment and how process improvement influences database design.
Prerequisites
MGMT 225 and INFO 201.

INFO 303 Machine Learning for the Business Analyst
Units: 1
Description
Process of investigating data through a machine learning lens. Application of machine learning techniques to real-world business use cases. Extract and identify useful features that best represent data, some of the most important machine learning algorithms, evaluate the performance of machine learning algorithms, and presentation (visualization) of results to business stakeholders.
Prerequisites
MGMT 225 and INFO 201.

Undergraduate Management

Robins School of Business
102 UR Drive
University of Richmond, VA 23173

Department Chair
Steve Thompson, Ph.D.
Q375
(804) 287-6643
sthomps3@richmond.edu 

Department Coordinator
Donna Ruff
RSB 315
(804) 662-3166  
druff@richmond.edu