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