Minor in Data Science (In collaboration with the department of Mathematics and Statistics)

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