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.

On completion of the program, students will be able to:

• formulate and build statistical models for various real-life applications
• apply relevant programming skills using professional data mining software such as R, Python and SAS
• analyze big data using various data science techniques
• demonstrate skills in interpreting and communicating the results of data analysis, orally and in writing

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.

The following rules apply:

• The minor consists of a minimum of 18 credit hours, including at least nine credit hours in courses at or above the 300 level.
• At least nine credit hours of the 18 credit hours required for the minor must be successfully completed in residence at AUS.
• At least six credit hours of the nine credit hours at or above the 300 level must be successfully completed in residence at AUS.
• A minimum GPA of 2.00 must be earned in courses completed to satisfy the minor.

Students seeking a minor in data science must successfully complete the following courses or their equivalent. All course prerequisites must be satisfied.

Minor Requirements (12 credit hours)

• CMP 120 Programming I or MIS201 Fundamentals of Management Information Systems
• one of the following:
  • STA 201 Introduction to Statistics for Engineering and Natural Sciences
  • STA 202 Introduction to Statistics for Social Sciences
  • QBA 201 Quantitative Business Analysis
  • NGN 111 Introduction to Statistical Analysis, plus MTH 243 Introduction to Mathematical Programming or a one-credit CMP or COE directed study in data science
• 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. A minimum of three credit hours must be successfully completed in courses at the 300-level or above.

• CMP 305 Data Structures and Algorithms
• CMP 320 Database Systems
• CMP 433 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
• ELE 456 Pattern Recognition
• FIN 430 Financial Forecasting
• INE 415 Design of Experiments
• MCM 311 Mass Communication Research Methods and Data Analytics
• MIS 301 Fundamentals of Database Management
• 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 233Introduction to Survey Sampling and Analysis
• UPL 302 Analysis of Spatial Phenomena