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Analytics

This is an archived copy of the 2023-2024 catalog. To access the most recent version of the catalog, please visit .

The Master of Science in Analytics (MSA) is uniquely designed to equip students for the task of deriving and effectively communicating actionable insights from a vast quantity and variety of data. It is an intensive 10-month degree with a strong practical orientation focused on the tools and methods used by data scientists. It is a fully integrated course of study taught exclusively to MSA students and designed to produce well-rounded professionals. Student teams tackle genuine problems with data provided by industry and government sponsors.听

Admission Requirements

Admission to the MSA program is highly competitive. The best-qualified applicants will be accepted up to the limited number of seats available for students each year. The admissions committee evaluates candidates on criteria such as:

overall academic record and grade point average;
academic performance in analytical/quantitative subjects;
relevant employment experience and potential to succeed in the profession; and
leadership potential, integrity, and other personal character traits.听

The Institute welcomes applications from highly motivated individuals of exceptional talent regardless of undergraduate major. Applicants without prior coursework in statistics and/or experience with computer programming would need to complete a set of prerequisite courses before qualifying as a candidate for admission.

Master鈥檚 Degree Requirements

Students complete 30 credit hours of defined coursework in a period of ten months beginning in Summer Session II and ending the following Spring semester. The integrated curriculum is designed to provide a focused education in the software tools, methods and applications of data analytics.

Other Relevant Information

Students must begin the degree program in the first semester (Summer Session II) and complete all 30 credit hours of the curriculum. The program is designed for full-time students only. Applications for admission are reviewed between September and April.

2023-2024 Program Schedule

Summer II 2023: 础础听500 and 础础听501听

Start date: June 26, 2023听
Census date: June 28, 2023
End date: July 29, 2023
Communication Training (required): July 31听 - August 11, 2023

Fall 2023: AA听502 and AA听504听

Start date: August 17, 2023听
Census date: September 1, 2023
End date: November 29, 2023
Practicum project work, midpoint presentations, career and professional development activities (required): November 30 - December 15, 2023

Spring 2024: AA听503 and AA听505听

Start date: January 8, 2024听
Census date: January 22, 2024
End date: April 26, 2024
Spring Commencement: May 4, 2024

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Degrees

Analytics (MS)

Full Professors

Christopher G. Healey
Michael A. Rappa

Practice/Research/Teaching Professors

Susan Jeanne Simmons
Aric David LaBarr
Christopher West
Andrea Villanes Arellano
Sarah Egan Warren

Courses

础础听500听听Analytics Tools and Techniques听听(3 credit hours)听听

This course equips the student with basic and advanced computer programming skills needed to use industry-standard analytics tools for data analysis, including but not limited to: data access and management, data cleaning, data mining, text mining, geospatial analytics, forecasting, and optimization. Restricted to AA majors.

Corequisite: 础础听501

Typically offered in Summer only

础础听501听听Analytics Foundations听听(3 credit hours)听听

This course equips the student with basic knowledge of statistics required for further study in analytics. Topics include, but are not limited to: Exploratory Data Analysis, Linear Regression, Multiple Linear Regression, Regression Diagnostics, Logistic Regression, ANOVA, Cluster Analysis, Analysis of Tables, and Survey Data Analysis. Restricted to AA major.

Corequisite: AA 670

Typically offered in Summer only

AA听502听听Analytics Methods and Applications I听听(6 credit hours)听听

This course equips the students with the methods and applications of advanced analytics. Topics include, but are not limited to: Time Series and Forecasting, Geospatial Data Analytics, Linear Algebra, Data Mining, Survival Data Analysis and Logistic Regression Models. Restricted to AA major.

Prerequisite: 础础听501 and AA 670; Corequisite: AA听504

Typically offered in Fall only

AA听503听听Analytics Methods and Applications II听听(6 credit hours)听听

This course equips the student with the methods and applications of advanced analytics. Topics include, but are not limited to: Advanced Data Mining, Text Mining, Financial Analytics, Risk Analytics, Marketing Science and Customer Analytics, Linear and Non-Linear Programming. Restricted to AA major.

Prerequisite: AA听502; Corequisite: AA听505

Typically offered in Spring only

AA听504听听Analytics Practicum I听听(6 credit hours)听听

This course equips the student with the knowledge and skills needed to conduct and present large-scale studies based on advanced analytics. Student teams conduct analysis using large amounts of real-world data. Restricted to AA major.

Prerequisite: 础础听501 and AA 670; Corequisite: AA听502

Typically offered in Fall only

AA听505听听Analytics Practicum II听听(6 credit hours)听听

A continuation of AA听504, this course equips the student with the knowledge and skills needed to conduct and present large-scale studies based on advanced analytics. Student team conduct analysis using large amounts of real-world data. Restricted to AA majors.

Prerequisite: AA听504; Corequisite: AA听503