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  Program Learning Outcomes

  Upon completion of the Applied Data Science Graduate Certificate, graduates will be able to:

  1. apply data science concepts and methods to solve problems in real-world contexts;
  2. demonstrate competencies in data mining and machine learning techniques, including classification, clustering, feature extraction, visualization, and dimensionality reduction;
  3. use statistical software packages to apply a variety of statistical techniques to diverse data types and structures;
  4. demonstrate an understanding of the strengths, limitations and assumptions of specific statistical methods;
  5. develop the ability to build and assess data-based and machine learning models as well as an understanding of model implementation in real-world settings.
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  Certificate Requirements (15 credits)

  This is a 15-credit (five-course) certificate program. Nine credits (three courses) are required core courses, and six credits (two courses) are electives, which may be chosen from the list below.

  Students must maintain a minimum 3.0 GPA in both the certificate program and overall. Grades below "C" will not advance a student towards graduation.

  CSSE 5110 is a co-requisite or prerequisite for all other requirements, so should be taken first.

  Required Courses (9 credit hours)

  • CSSE 5110 Quantitative Foundations for Data Analysis (3 credits)
  • CSSE 5120 Introduction to Data Science (3 credits)
  • CSSE 5310 Data Mining (3 credits)

  Elective courses (choose two courses - six credit hours)

  • CSSE 5480 Artificial Intelligence (3 credits)
  • CSSE 5560 Database (3 credits)
  • CSSE 5580 Cloud Computing and Big Data (3 credits)
  • CSSE 5910 Data Science Applications (3 credits)
  • ELEE 5350 Machine Learning (3 credits)
  • ELEE 5750 Deep Learning (3 credits)