Degree Program Links

8258
Graduate Certificate
Classroom
No
Graduate Certificate
Main Campus - Melbourne
2018-2019 Florida Tech Catalog
GCP Code: 8258 Degree Awarded: Graduate Certificate
Delivery Mode(s): Classroom Age Restriction: No
Admission Status: Graduate Location(s): Main Campus - Melbourne

The Graduate Certificate in Data Science is appropriate for students who are seeking a master's degree in a different discipline or for working professionals as an alternative to a full master's program.

Admission Requirements

Applicants must have earned a bachelor's degree from a regionaly accredited institution (or the international equivalent) with at least three semester credit hours of statistics and at least six semester credit hours of computer progamming coursework in a high-level programming language. GRE scores are not required.

Curriculum

The graduate certificate requires successful completion of three required and one elective course in data science. All courses must be completed with a minimum grade of C. A minimum cumultive GPA of 3.0 is required to earn the certificate.

Required Courses (9 credit hours)
  • CSE 5310 Management and Processing of Big Data
    Credit Hours: 3

    Provides students with theoretical knowledge and practical experience in data storage, management and retrieval for analysis or operations. Explores the transition from traditional data warehouse architectures to modern big-data architectures that robustly handle data variety, volumes and velocities.

  • CSE 5311 Numerical Methods for Data Analytics
    Credit Hours: 3

    Covers the mathematical and programming skills needed by data scientists. Focuses on mathematical concepts and their real-world application to data science. Reinforces the understanding of concepts by hands-on implementation using scientific programming languages such as Python or R, and industry-standard numerical packages.

    Prerequisite:

    CSE 5310 

  • CSE 5312 Software Engineering for Data Analytics
    Credit Hours: 3

    Presents fundamental concepts and methods required to successfully engineer the full life cycle of data-science software systems. Includes requirements and system architecture, design and construction, and testing and evaluation. Uses modern data science and software engineering tools to develop an analytics software system.

    Prerequisite:

    CSE 5311 

Restricted Elective (3 credit hours)

Select one:

  • CSE 5313 Applied Machine Learning at Scale
    Credit Hours: 3

    Introduces the basics of applied machine learning. Focuses on learning models from massive amounts of data and their use in prediction and pattern discovery. Uses modern parallel scalable frameworks to apply machine-learning methods to problems that include structured and unstructured data.

    Prerequisite:

    CSE 5312 

  • CSE 5640 Processing and Storage of Massive Data Sets 1
    Credit Hours: 3

    Introduces large-scale distributed systems. Emphasizes big-data processing and storage infrastructures. Provides hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, practical statistical and machine learning concepts, and data visualization. 

    Prerequisite:

    CSE 5312 

  • CSE 5645 Analytics for Time Series Data
    Credit Hours: 3

    Covers analysis methods for both continuous and discrete time-series data. Emphasizes discovering patterns in the signals, removing trends, forecasting and multi-scale analysis. Gives hands-on experience with selecting techniques, formulating models and performing analysis for decision support.

    Prerequisite:

    CSE 5312 

  • CSE 5646 Analytics for Textual Data
    Credit Hours: 3

    Provides the knowledge and skills needed to construct, model, apply and evaluate text analytics over massive data for a variety of applications. Requires students to choose, implement and apply graphical, statistical and numerical techniques to discover key patterns and gain insight from textual data.

    Prerequisite:

    CSE 5312 

  • CSE 5647 Analytics for Network Data
    Credit Hours: 3

    Provides the knowledge to analyze, model and apply network science methods in a variety of applications. Emphasizes choosing, applying and implementing numerical techniques to discover key patterns and gain insight from network data. Encompasses relational, sequential and other graph-based data. 

    Prerequisite:

    CSE 5312 

  • CSE 5648 Analytics for Visual Data
    Credit Hours: 3

    Covers how to detect, retrieve, categorize and search information from visual data (image, video, multidimensional signals from both visible and non-visible spectra). Also covers algorithms for object recognition, detection, tracking and segmentation. Emphasizes large-scale datasets.

    Prerequisite:

    CSE 5312 

Total Credits Required: 12