MS in Data Science Degree Structure


Students in the MS in Data Science program are required to complete a minimum of 36 credit hours (12 courses). They include:

  • Required (Core courses) - 9 credit hours
  • Guided Electives - 15 credit hours
  • General Electives - 9 credit hours
  • Internship/Research project - 3 credit hours

Additional information and details about courses are found below:

 

*For students that enrolled before
  
Fall 2020

Required courses, 9 credit hours


  • INFO 5501 - Fundamentals of Data Analytics
  • INFO 5502 - Analytic Tools, Techniques and Methods
  • INFO 5709 - Data Visualization and Communication

Guided electives, 15 credit hours


The guided electives are courses with advanced topics in both data science and data analytics. The student can choose from the following courses which concentrate on specific methodologies and tools in data science and data analytics.

Students must take 15 hours from the following list of courses:  
(*CSCE courses are not currently available for non CSCE majors)

  • BCIS 5600 - Visual Information Technologies
  • CSCE 5300 - Introduction to Big Data and Data Science
  • DSCI 5240 - Data Mining
  • DSCI 5330 - Enterprise Applications of Business Intelligence
  • DSCI 5340 - Predictive Analytics and Business Forecasting
  • DSCI 5360 - Data Visualization for Analytics
  • INFO 5307 - Knowledge Management Tools and Technologies
  • INFO 5503 - Knowledge Management Processes and Practices
  • INFO 5810 - Data Analysis and Knowledge Discovery
  • ADTA 5130 - Data Analytics I
  • ADTA 5230 - Data Analytics II
  • LING 5360 - Studies in Descriptive Linguistics
                               or
    LING 6150 - Semantic Ontologies

General electives, 9 credit hours


Students must take 9 hours from the following list of courses. They are allowed to pursue courses from outside this list and in their areas of interest with the approval of the advisor.

  • INFO 5707 - Data Modeling for Information Professionals
  • INFO 5731 - Computational Methods for Information Systems
  • INFO 5735 - Usability and User Experience Assessment
  • INFO 5737 - Information and Cyber-Security
  • INFO 6050 - Health Research Methodology
  • LTEC 5320 - Contemporary Issues in Workforce Learning and Performance
  • LTEC 5300 - Learning and Cognition
  • LING 5410 - Computational Linguistics I
  • LING 5530 - Semantics and Pragmatics I
  • LING 5550 - Corpus Linguistics
  • LING 5560 - Discourse Analysis
  • LING 5360 - Studies in Descriptive Linguistics 
    (when the topic is “Data Analysis in Human Language Technology I”)
      or
    LING 6060 - Data Analysis in Human Language Technology (HLT) I
  • LING 5360 - Studies in Descriptive Linguistics 
    (when topic is “Natural Language Processing”)
    or
    LING 6130 - Natural Language Processing
  • LING 5360 - Studies in Descriptive Linguistics 
    (when the topic is “Data Analysis in Human Language Technology II”)
    or
    LING 6140 - Data Analysis in Human Language Technology (HLT) II

Internship/research project, 3 credit hours


  • INFO 5082 - Seminar in Research and Research Methodology
  • INFO 5090 - Practicum and Internship in the Field Study

*For students that enrolled in Fall 2020 &     subsequent semesters

Required courses, 9 credit hours


  • INFO 5501 - Fundamentals of Data Analytics
  • INFO 5502 - Analytic Tools, Techniques and Methods
  • INFO 5505 - Applied Machine Learning for Data Scientists

Guided electives, 15 credit hours


The guided electives are courses with advanced topics in both data science and data analytics. The student can choose from the following courses which concentrate on specific methodologies and tools in data science and data analytics.

Students must take 15 hours from the following list of courses:  
(*CSCE courses are not currently available for non CSCE majors).

  • BCIS 5600 - Visual Information Technologies
  • CSCE 5300 - Introduction to Big Data and Data Science
  • DSCI 5240 - Data Mining
  • DSCI 5330 - Enterprise Applications of Business Intelligence
  • DSCI 5340 - Predictive Analytics and Business Forecasting
  • DSCI 5360 - Data Visualization for Analytics
                            or
    INFO 5709 - Data Visualization and Communication
  • INFO 5307 - Knowledge Management Tools and Technologies
  • INFO 5503 - Knowledge Management Processes and Practices
  • INFO 5810 - Data Analysis and Knowledge Discovery
  • ADTA 5130 - Data Analytics I
  • ADTA 5230 - Data Analytics II
  • LING 5360 - Studies in Descriptive Linguistics
                          or
    LING 6150 - Semantic Ontologies

General electives, 9 credit hours


Students must take 9 hours from the following list of courses. They are allowed to pursue courses from outside this list and in their areas of interest with the approval of the advisor.

  • INFO 5707 - Data Modeling for Information Professionals
  • INFO 5731 - Computational Methods for Information Systems
  • INFO 5735 - Usability and User Experience Assessment
  • INFO 5737 - Information and Cyber-Security
  • INFO 6050 - Health Research Methodology
  • LTEC 5320 - Contemporary Issues in Workforce Learning and Performance
  • LTEC 5300 - Learning and Cognition
  • LING 5410 - Computational Linguistics I
  • LING 5530 - Semantics and Pragmatics I
  • LING 5550 - Corpus Linguistics
  • LING 5560 - Discourse Analysis
  • LING 5360 - Studies in Descriptive Linguistics 
    (when the topic is “Data Analysis in Human Language Technology I”)
    or
    LING 6060 - Data Analysis in Human Language Technology (HLT) I
  • LING 5360 - Studies in Descriptive Linguistics 
    (when topic is “Natural Language Processing”)
    or
    LING 6130 - Natural Language Processing
  • LING 5360 - Studies in Descriptive Linguistics 
    (when the topic is “Data Analysis in Human Language Technology II”)
    or
    LING 6140 - Data Analysis in Human Language Technology (HLT) II

Internship/research project, 3 credit hours


  • INFO 5082 - Seminar in Research and Research Methodology
  • INFO 5090 - Practicum and Internship in the Field Study