Degree Requirements for the BDML Concentration

The MSA degree with BDML concentration requires a total of 34–36 hours taken over at least three semesters.

Required Courses (12 hours)
CSC 6710 Database Systems (4)
CSC 6780 Fundamentals of Data Science (4)
CSC 8902 Ethics (1)
STAT 8670 Computational Methods in Statistics (3)

Other Required Courses (15 hours)
CSC 6740 Data Mining (4)
CSC 6760 Big Data Programming (4)
CSC 6850 Machine Learning (4)
One of:
STAT 8310 Bayesian Data Analysis (3) OR
STAT 8561 Linear Statistical Analysis I (3) OR
STAT 8700 Categorical Data Analysis (3)

Two 8000-level Elective Courses (6–8 hours)
CSC 8530 Parallel Algorithms (4)
CSC 8710 Deductive Databases and Logic Programming (4)
CSC 8711 Databases and the Web (4)
CSC 8712 Advanced Database Systems (4)
CSC 8713 Spatial and Scientific Databases (4)
CSC 8740 Advanced Data Mining (4)
CSC 8741 Graph Mining (4)
CSC 8810 Computational Intelligence (4)
CSC 8850 Advanced Machine Learning (4)
CSC 8851 Deep Learning (4)
CSC 8910 Data Dissemination in Online Social Networks (4)
STAT 8090 Applied Multivariate Statistics (3)
STAT 8561 Linear Statistical Analysis I (3)
STAT 8610 Time Series Analysis (3)
STAT 8674 Monte Carlo Methods (3)

BDML Capstone Project (1 hour)
CSC/STAT 8930 MS Project (1)

Learning Outcomes

Upon completion of the MSA degree with BDML concentration, students will possess the following data science skills and abilities:

  • Understand the principles of data science, enhance data-driven knowledge, and be able to apply data science theory for practical analysis tasks.
  • Collect, store, search, mine and visualize big data. Learn how to transform raw data into tangible value and evaluate data in terms of volume, variety, velocity, source, etc.
  • Identify the data science tasks of an organization and design corresponding solutions. Learn how to evaluate data science options and limitations that could influence organizational needs.
  • Analyze and evaluate multiple data science models. Understand the strength and limitation of diverse data science models in terms of various data science tasks and be able to select appropriate ones to solve given data science tasks.
  • Interpret outcomes from employed data science models. Transform observations from data resources and outcomes from data science models into actionable business strategies, and persuade decision makers of the practical benefits arising from these discoveries.
  • Have the leadership skills and knowledge to lead a team of data science professionals and to provide long-term, high-quality data science services to employers.