Minor in Data Science

Data science is an interdisciplinary field that is used to extract knowledge and insight from data. The minor in data science provides students with essential knowledge of data analytics and skills. The program covers a wide range of topics including data preparation and visualization, statistical modeling, programming, machine learning and data mining techniques.

Students enrolling in the data science minor should have normally completed a minimum of 30 credit hours of course work and be in good academic standing.

Students seeking a minor in data science must successfully complete the following requirements:

• a minimum of 12 credit hours including:
• six credit hours of minor requirements
• a minimum of six credit hours of minor electives
• a minimum of six credit hours of the 12 credit hours required for the minor successfully completed in residence at AUS
• a minimum of six credits hours of the courses for the minor at or above the 300 level successfully completed in residence at AUS
• a minimum GPA of 2.00 in courses completed to satisfy the minor

Courses used to satisfy the student’s degree program’s graduation requirements can be used towards meeting the requirements of the minor as follows:

• credit hours of courses used to satisfy the requirements of the minor do not double count towards meeting the core requirements, major requirements, major electives, concentration requirements, concentration electives or general education requirements of the student’s degree program graduation requirements
• free elective courses can double count towards meeting the minor’s required and elective courses

The minor is open to all majors at AUS including business, engineering, sciences, social science and others.

Students pursuing two minors must meet the requirements of both minors. Courses used to satisfy the requirements of one minor can double count towards satisfying the requirements of the second minor provided that, at a minimum, 12 credit hours are not double counting.

Minor Requirements (6 credit hours)

Students must successfully complete the following requirements:

• STA 301 Foundations of Statistics for Data Science
• STA 401 Introduction to Data Mining or CMP 466 Machine Learning and Data Mining or MIS 388 Business Analytics

Minor Electives (minimum of 6 credit hours)

Students must successfully complete a minimum of six credit hours in courses selected from the following list:

• CHE 443 Design and Analysis of Experiments
• CMP 305 Data Structures and Algorithms
• CMP 320 Database Systems
• CMP 333 Artificial Intelligence
• COE 375 Modeling and Simulation of Stochastic Systems or ELE 360 Probability and Stochastic Processes for Electrical Engineers
• ECO 351 Introduction to Econometrics
• ECO 451 Advanced Econometrics
• ECO 452 Economic Forecasting
• ELE 456 Pattern Recognition
• ELE 460 Deep Networks in Machine Learning
• FIN 430 Financial Forecasting
• INE 323 Stochastic Processes and Simulation
• INE 415 Design of Experiments
• ISA 301 Fundamentals of Database Management
• ISA 377 Data Visualization for Business
• MCM 311 Mass Communication Research Methods and Data Analytics
• MTH 221 Linear Algebra
• MTH 350 Introduction to Probability
• MTH 382 Linear Programming and Optimization
• MTH or STA 394/494 approved special topic courses in the areas of probability, optimization and statistics
• STA 233 Introduction to Survey Sampling and Analysis
• UPL 302 Analysis of Spatial Phenomena