Business Analytics
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Department Information
Business Analytics
The Analytics and Operations Department provides instruction in this area.
Doug Bosse, Chair
Professors Ashworth, Bosse, Eynan, Harrison, Ho, Tallman
Associate Professors Coughlan, Thekdi, Thompson, Whitaker
Assistant Professors Aronson, Chen, Courtney, Cruz, Mattson, Sutton
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Concentration
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:
Data Analysis Software
Advanced Applied Statistics
SQL and Process Optimization for the Business Analyst
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:
Accounting Information Systems
Advanced Econometrics
Advanced Macroeconomics
Cases and Financial Modeling
Marketing Analytics
Courses
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INFO 201 Data Analysis Software
Units: 1
DescriptionSoftware 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.
PrerequisitesBUAD 202.
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INFO 301 Advanced Applied Statistics
Units: 1
DescriptionRegression 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.
PrerequisitesMGMT 225 and INFO 201.
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INFO 302 SQL and Process Optimization for the Business Analyst
Units: 1
DescriptionIntroduces 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.
PrerequisitesMGMT 225 and INFO 201.
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INFO 303 Machine Learning for the Business Analyst
Units: 1
DescriptionProcess 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.
PrerequisitesMGMT 225 and INFO 201.