Data-driven Modeling and Analysis of Complex Systems, PB

The Post-Baccalaureate Certificate Program in Data-Driven Modeling and Analysis of Complex Systems (DMACS) is designed to meet the growing demand for data-driven modelers and analysts. The DMACS program provides the training needed to capture, analyze, and visualize large-scale datasets for data-driven decision-making.

The DMACS certificate offers two tracks: 1) the systems data modeling and analysis track is designed for students and professionals who have no prior experience in programming or statistics and desire a more focused certificate in a specific complex system area; and 2) the system data management track is designed for students and professionals who have prior statistics and programming background and desire a deep understanding of big-data management and cloud technologies.

Certificate Requirements

Total credit hours: 13

Systems Data Modeling and Analysis Track:

  • Data Modeling & Analysis using R and Python (1 credit hour)
  • 1 course from the Statistics I course list below (3 credit hours)
  • 1 course from the Engineered Systems course list below (3 credit hours)
  • 2 courses from the Data Engineering & Science course list below (6 credit hours total)
     

System Data Management Track:

  • Data Modeling & Analysis using R and Python (1 credit hour)
  • 1 course from the Statistics II course list below (3 credit hours)
  • 2 courses from the Data Engineering & Science course list below (6 credit hours total)
  • 1 course from the Technical Elective course list below (3 credit hours)

Statistics I Course List:

MATH 623 Probability Theory and Application
ISEN 675 Design and analysis of experiments
CSE 701 Applied probability and statistics

Engineered Systems Course List:

ISEN 831 Service Operations Management 
ISEN 833 Supply Chain Systems Engineering 
ISEN 785 Data Science Graduate capstone project 

Data Engineering & Science Course List: 

ISEN 685 Fundamentals of Data-Enabled Systems Engineering Bootcamp
ISEN 785 Data Engineering & Science Graduate capstone project 
COMP 851 Big Data Analytics 
COMP 871 Social Network Analysis 
CSE 620 Introduction to Computational Software Tools 
CSE 817 Fundamentals of big data analysis 

Statistics II Course List:

MATH 712 Numerical Linear Algebra
MATH 721 Multivariate Statistical Analysis 
ISEN 821 Multivariate Statistics for Engineers 
MATH 723 Advanced Topics in Data Science 
CSE 801 Computational Statistics 
CSE 702 Computational Methods for Algebraic Systems

Technical Elective Course List:

COMP 853 Data Fusion
CSE 804 Computational Modeling and Visualization 
CSE 805 Machine Learning and Data Mining 
CSE 720 Research Computing Environments 
CSE 805 Machine learning and data mining 
ISEN 885 Predictive Modeling and Data Mining 

 

Contact Information 

Department of Industrial & Systems Engineering

Graduate Coordinator: Younho Seong
Email
Phone: 336-285-3734

Department Chair: Om Prakash Yadav
Email
Phone: 336-285-3735