Program at a Glance
- Program
- Master
- College(s)
- College of Engineering and Computer Science
- Department(s)
- Computer Science
Help companies use large amounts of data in innovative ways.
What is the MS in Data Analytics?
The Master of Science in Data Analytics program at the University of Central Florida prepares students with the technical skills needed to manipulate, manage, and interpret data. There is an increasing need to turn large and complex amounts of data into knowledge to drive business decisions. The program will utilize the technical aspects of big data analytics, including algorithm design, programming, acquisition, management, mining, analysis, and interpretation of data.
Program Track Option
The AI track in the Master of Science in Data Analytics (MSDA) program at UCF is a new and innovative track that will equip you with the technical skills of artificial intelligence and data analytics. You will learn the fundamentals and applications of data science, machine learning, deep learning, and artificial intelligence from experts in the field. You will also have the opportunity to work on real-world problems and projects in collaboration with industry partners. The AI track is a 30-credit hour program that offers a comprehensive and rigorous curriculum, a variety of elective courses, an applied and integrated capstone project, and a supportive and collaborative learning environment. The AI track will prepare you for high-demand and lucrative careers in artificial intelligence and data analytics.
Highlights
Total Program Cost
Includes credit hours, fees, and administrative costs. (Textbooks not included)
Application Deadlines
- Fall
- July 1
Application deadlines subject to change, see the Graduate Studies website for up-to-date deadlines.
Ready to get started?
Course Overview
Machine Learning
Origin/evaluation of machine intelligence; machine learning concepts and their applications in problem solving, planning and “expert systems” symbolic role of human and computers.
Data Mining Methodology I
Supervised data mining tools including boosting trees, SV machine, regression, and neural network will be covered. The Enterprise Miner (R or Python) will be used.
Interactive Data Visualization
Principles and techniques for interactive data visualization that are useful for analyzing, presenting and exploring information are covered. The emphasis will be on algorithmic aspects of developing interactive visualization. The students will receive practical experience of building interactive visualization systems.
Network Science
Undergraduate degree in CS, EE, or CpE. The emerging science of complex networks and their applications. Focus will be on algorithms, mathematical theories, and computational methods that analyze complex networks and predict their behavior.
Parallel and Distributed Database Systems
Storage manager, implementation techniques for parallel DBMSs, distributed DBMS architectures, distributed database design, query processing, multidatabase systems.
Parallel and Cloud Computation
The course introduces students to parallel computing across the hardware-software stack. Special emphasis is placed on parallel programming using emerging architectures and technologies.
Statistical Analysis
Data analysis; statistical models; estimation; tests or hypotheses; analysis of variance, covariance, and multiple comparisons; regression and nonparametric methods.
Data Mining Methodology II
Unsupervised learning methods such as cluster analysis, association analysis and newly developed tools will be covered. The Enterprise Miner (R or Python) will be used.
Project in Data Analytics
A project-focused course that demonstrates mastery of data analytics through development of novel algorithms or innovative application of existing techniques for data mining applications.
Text Mining I
Extracting knowledge from unstructured text collections. Document indexing, similarity and summarization, clustering, classification, named entity recognition and relation extraction, text stream processing. Several programming assignments.
Social Media and Network Analysis
Techniques developed by the computer science research community for analyzing social networks and social media datasets.
Computational Analysis of Social Complexity
Computational concepts, principles, modeling and simulation approaches used to analyze complex social and economic phenomena, leveraging the availability of large amounts of data, and elements of complexity theory.
Machine Learning Methods for Biomedical Data
Summarize computational techniques for bridging two fields: machine learning and biomedical science to illustrate successful data mining and knowledge discovery in an interdisciplinary context.
Data Preparation
Variable selections, missing value imputation, text, time series, and new data preparation method will be covered. The Enterprise Miner (R or Python) will be used.
Big opportunities in big data
- Glassdoor has ranked Data Scientist as the #1 career in 2016, 2017, 2018, and 2019. Source: Glassdoor.com
- The average salary for a Data Scientist is $121,235 per year in the United States. Source: Indeed.com
- By 2020 the number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings to approximately 2,720,000. Source: Forbes.com
Career Opportunities
- Data Scientist
- Data Analyst
- Data Architect
- Data Engineer
- Data Mining Specialist
- Business Intelligence Analyst
- Big Data Engineer
- Big Data Scientist
- Database Administrator
Admission Requirements
The requirements for admission include an undergraduate degree, official GRE/TOEFL scores, a resume, letters of recommendation (encouraged but not required) and the demonstration of understanding in fundamental concepts. The program’s director, assisted by the program’s faculty, will evaluate student applications. Face-to-face or telephone interviews may be conducted for admission. The details of the admission requirements are as follows:
Bachelor’s Degree
Applicants must have earned a bachelor’s degree from an accredited institution with a GPA of 3.0 or better. One official transcript (in a sealed envelope) is required from each college/university attended.
Test Scores
GRE not required. TOEFL (if applicable) scores taken in the last five years must be provided with the application.
Working Knowledge of Fundamental Concepts
Applicants without a strong undergraduate background in Computer Science or Statistics must demonstrate an understanding of the material covered in the following undergraduate courses:
- COP 3503 Computer Science II – Algorithms, Data Structures
- COP 3330 Object-Oriented Programming – Object-Oriented Programming Concepts, Expression of Concepts in a Language
- COP 4710 Database Systems – Relational Databases, Structured Query Language
- STA 2023 Statistical Methods I – Probability Distributions, Data Organization
- STA 4164 Statistical Methods III – Regression Analysis
Understanding of these concepts can be demonstrated by a combination of the following:
- Taking these courses; OR
- Convincing the program director that the student work experience covers these materials; OR
- Having taken these courses at UCF or equivalent courses at another institution; OR
- At the recommendation of the program director, registering in the statistics Bridge Course, the Computer Science Bridge Course or both. These 4-week bridge courses are free.
International Admissions Requirements
International Students are eligible for paid internships via the UCF Office of Experiential Learning as well as for Optional Practical Training (OPT) status after graduation (Curriculum Practical Training – CPT).
Please review required information to start admission process and complete steps before the deadlines.
Step 1: Apply Online
Your admission to UCF starts with the UCF application form, which has a cost of U.S. $30.00 for a graduate application.
Step 2: Submit required academic documents
- Submit transcripts from all universities attended. Please review the transcript and evaluation information website.
- Applicants applying to this program who have attended a college/university outside the United States must provide a course-by-course credential evaluation with GPA calculation. Credential evaluations are accepted from World Education Services (WES) or Josef Silny and Associates, Inc. only.
- For details on your country’s required documents, including academic credentials and equivalency, please visit the Sample Country Requirements website.
- Test of English as a Foreign Language (Proof of English Language Fluency) is required for international students. Please review Test Requirements information website.
- A resume and two letters of recommendation required.
Step 3: Submit required immigration documents
Please visit International Affairs and Global Strategies website for required documents downloads and instructions.
International students coming from abroad will need:
- I-20 Request Form
- Copy of Passport
- Certification of Finances and supporting financial documents
- Dependent Documents (if applicable)
International students transferring from a college or university in the United States:
- Transfer Clearance Form
- Copy of F-1 Visa or Notice of Action
- Copy of Form I-94 (to print your Form I-94, please visit www.cbp.gov/I94)
- Copy of Original SEVIS I-20 and all other previous I-20’s
Meet Your Personal Success Coach
Success Coaches will help you succeed from application to graduation.
Lorie Elrod
Lorie.elrod@ucf.edu
BENEFITS OF A SUCCESS COACH:
- Coordinates applications/forms
- Develops with you a plan to align courses with career goals
- Delivers personalized nonacademic assistance
- Connects you to student resources
- Creates strategies to graduate on time
