Data Analytics Graduate Certificate

Data Analytics Graduate Certificate

Graduate Certificate Program in Data Analytics

The Graduate Certificate Program in Data Analytics is a three-course + (research or internship) sequence that graduate students can take instead of or in combination with one of the Master of Science programs offered by the Department of Computer Science and Information Technology. This program prepares students for practical, applications-oriented work in data science in industry or academia.

Many modern academic and commercial endeavors apply the techniques of data mining and predictive analytics to their data sets. Students who complete this certificate program will learn concepts, methods, and software tools for locating and obtaining data of interest, for preparing data for semi-automated analysis, for interacting with software tools in analyzing data for patterns, for visualizing structural and dynamic patterns in data, and for designing systems that respond to patterns in data. Successful completion of the program requires a capstone independent study course or internship, in addition to the required courses. After successful completion of program coursework, students will be able to demonstrate the following knowledge and skills:

1. Demonstrate the ability to locate useful massive data sources, download targeted data, evaluate it for integrity and completeness, and format it for use in data analysis software tools.
2. Filter, organize, and manage prepared data for use in analysis.
3. Construct formal analyses of data using multiple machine learning mechanisms and processes, including standard approaches from the following list.
        a. data association rules
        b. data classification trees
        c. statistical regression techniques
        d. data clustering techniques
        e. instance-based machine learning
        f. ensemble machine learning
        g. neural networks
4. Create meaningful graphical visualizations of patterns in data relationships.
5. Evaluate the integrity and completeness of the results of data analyses.
6. Communicate the results of analyses using written, graphical, and oral communication
techniques.
7. Integrate the results of analyses into automated mechanisms for responding to patterns in
data.

Students can attend classes in person or via interactive, streaming classroom presentations at class times.

Master's candidates must register for the Certificate Program in addition to registering for their MS programs. Certificate Program students who are not pursuing a Master's degree are not available for federal financial tuition aid.

Courses in the Certificate Program:

CSC 458 - Data Mining and Predictive Analytics I (3 sh)
Many academic and commercial endeavors apply the techniques of data mining and predictive analytics to their data sets. Students taking this course will learn methods and software tools for locating and obtaining data of interest, for preparing data for semi-automated analysis, for interacting with software tools in analyzing data for patterns, for visualizing structural and dynamic patterns in data, and for designing systems that respond to patterns in data. Data cleaning and formatting require some programming in a modern scripting language. Other course activities include learning to use off-the-shelf software tools to accomplish the tasks of data analysis.

CSC 459 - Introduction to Big Data (3 sh)
This course explores key data management and analysis techniques, which deal with massive datasets to enable real-time decision-making in distributed environments, business intelligence in the Web, and scientific discovery in a large scale. In particular, map-reduce parallel computing paradigms and associated technologies, such as distributed file systems, noSQL databases, and basic machine learning methods, will be explored.

CSC 558 - Data Mining and Predictive Analytics II (3 sh)
This course covers advanced study and practice in data mining and predictive analytics. Topics include understanding, configuring, and applying advanced variants of data association, classification, clustering, and statistical analysis engines, analyzing and applying underlying machine learning algorithms, exploring instance-based, support vector, time-series, ensemble, graphical, and lazy learning algorithms, meta-learning, neural nets, genetic algorithms, and validating results. The course examines topics specific to very large data sets. Data cleaning and formatting require some programming in a modern scripting language. Other course activities include using, extending, and customizing off-the-shelf machine learning software systems to accomplish the tasks of data analysis.

CSC 570 - Independent Study and/or Projects in Computer Science (3 sh)
OR
CSC 590 - CS Cooperative Internship (3 sh)
as approved by the student's program advisor.

  • Admissions Requirements and Deadlines
    • Application
    • Official transcripts verifying completion of a baccalaureate degree; as well as official transcripts from all universities and colleges attended whether a degree was earned or not
    • Resume

    * Prerequisites for the certificate:
            A Statistics Course (C or better) and a Course Programming in a Scripting Language (C or better)
                OR
           Equivalent industry / professional experience
    Please reach out to the Computer Science and Information Technology Department with questions about prerequisite courses/experience.

    Deadlines

    • Fall semester: August 1st
    • Spring semester: December 1st
    • Summer sessions: May 1st

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