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Hidden Fields


Program Overview

Data Analytics is concerned with statistical techniques that measure the validity of insights from big data, with computational techniques for managing data efficiently, and also with machine learning techniques to learn trends from big data in order to make accurate predictions. Excellent candidates for this program are:

  1. Graduates from computer science, information technology, computer engineering, statistics or related programs who want to hone their skills towards the “Big Data” field and connect with industry leaders via internships.
  2. Working professionals in the field of statistics, computer programming, information technology, databases, business intelligence, or related areas who would like to boost their professional opportunities by obtaining an MS in Data Analytics degree without quitting their current jobs. Many local corporations provide sponsorships to their employees to obtain advanced degrees.
  3. Students who have only partial coursework or experience in these areas but desire to pursue a degree in data analytics and be part of the “Big Data” revolution. For the preparation of these students, UCF offers undergraduate prerequisite courses or alternatively, bridge courses prior to the start of the program.

This program is not for students that are interested in a non-technical education. This program is technically oriented and emphasizes on hands-on computer programming, machine learning and statistical modeling.

This is the first program in Florida build from the ground up with the explicit goal to educate data scientist. Data scientist needs to be versed in multiple technologies including computer programming and algorithms (Hadoop, Spark, Python), parallel databases (SQL, coding), machine learning (predictive analytics), cluster and cloud tools (Map-Reduce, Amazon Web Services for Big Data) and statistical methods (SAS/R). The combination of these skills with an educational experience that emphasizes business acumen and strong communication skills, make this program unique.

This 20-month program is designed to provide a rich educational experience with several unique features:

  1. Interdisciplinary technical education, featuring faculty from the computer science and statistics departments, experts in data science
  2. Personalized cohort-based face-to-face instruction geared towards working professionals, modeled after executive educational experiences such as the executive MBA, and designed to maximize networking, teamwork and communication among students
  3. Program commitment to place 100% of inaugural graduating class on data science jobs
  4. Strong external advisory board composed of industry leaders that will help guide the program curricula toward the emerging and anticipated industry needs as well as provide internship opportunities to the MSDA students

Application / Admission

Letters of recommendation are encouraged but not required. The letters of recommendation must explain the learner’s value as an employee or student, accomplishments, and personal qualities in an organizational or academic context.

The letters are preferred to come from a current or former employer, academic advisor, former teacher or mentor. The individuals with whom you the learner had considerable professional or academic interaction should be selected. The selected individual must be able to attest to the learner’s value as an employee or student, accomplishments, and personal qualities in an industrial or academic context.

Yes, the GRE is not required.

If your GPA is below the minimum required 3.0, then you may be considered for provisional admission into the program at the discretion of the program director. Please keep in mind that the number of Provisionals that can be admitted is limited and we will not know what that number is until after the admissions deadline.

The TOEFL is require only for international students that are not native English speakers and that are first time students at an English speaking institution.

Admission decisions will be announced 2 weeks after the application deadline.

Related fields include those that would provide a similar level of technical principles and discipline analogous to those required by a Computer Science or Statistics program. These include most other engineering disciplines, as well as mathematics, physics, quantitative management or other similar fields. Students with degrees in other disciplines such as business or economics will also be considered on a case-by-case basis, provided they have significant work experience and/or they take the four-week Bridge Courses offered for fundamental Computer Science and Statistics concepts.

Having an undergraduate degree in Computer Science, Statistics, Computer Engineering, or Information Technology will in most cases be sufficient to fulfil this requirement. If student does not have a strong undergraduate background in Computer Science or Statistics the student must demonstrate an understanding of the material covered in the following undergraduate courses:

  • COP 3503C 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:

  1. Taking these courses. In case that your background or education have not provided you with fundamental concepts needed to success in the MSDA, the program coordinator will help you put together a plan of study to take few undergraduate courses in order to succeed in the MSDA program; OR
  2. Convincing the program director that the student work experience covers these materials. Your work experience should be clearly noted in your resume and you may be asked to clarify or expand on your work experience in your interview; OR
  3. Having taken these courses at UCF or equivalent courses at another institution as demonstrated by your transcripts; OR
  4. At the recommendation of the program director, taking the Statistics Bridge Course, the Computer Science Bridge Course or both. Students that need a short refresher in fundamentals can be conditionally accepted in the program pending taking and approving one or both of the Bridge courses. For example, if a student has taken statistics classes many years ago and in his professional work experience the student has not been involved in statistics, then the student may be required to take the Bridge course in statistics as a condition to be admitted in the MSDA.

All student admitted to the MSDA program are encouraged but not required to take these two bridge courses. Only students conditionally admitted with the express requirement of taking and approving one or both of these bridge courses are required to take them.

No, the bridge courses are only for students accepted or conditionally accepted into the MSDA.

Yes, the capstone course “Project in Data Analytics (CAP 6942)” is structured to be an internship program. In this class students will earn credit while interning with an industry partner of the MSDA program. In this internship students will identify and solve a meaningful real world problem in Big Data.

Previous approval of the course instructor, students currently employed can opt for fulfilling their internship requirement with their current employers.

Curriculum / Coursework

The program will be delivered by face to face instruction on the main campus of UCF in classes scheduled after 5:00pm or on weekends.

Yes, the program will be delivered by face to face instruction on the main campus of UCF.

Students will take two courses per academic semester of 3 credit hours each. As a result, the workload will be equivalent to a part-time student. This program is designed to be highly demanding but appropriate for working professionals.

The program is a 30 credit hour interdisciplinary program that prepares students to develop algorithms and computerized systems to facilitate the discovery of information from large amounts of data. It will utilize the technical aspects of big data analytics, including algorithm design, programming, acquisition, management, mining, analysis, and interpretation of data. The students will learn to:

  • Use state-of-the-art software tools to perform data mining and analysis on large structured and unstructured data sets, and transform such data into knowledge.
  • Design and implement new algorithms for data mining and analysis, and study their time-, space-, and energy-efficiency.
  • Perform data acquisition and management for extremely large and dynamic databases.
  • Present and communicate knowledge derived from data in an unambiguous and convincing manner.

The course of study is as follows:

Core Courses:

  • Statistical Analysis (STA 5206)
  • Parallel and Distributed Database Systems (COP 5711)
  • Machine Learning (CAP 5610)
  • Text Mining I (CAP 6307)
  • Network Science (COT 6938)
  • Data Mining Methodology I (STA 5703)
  • Data Mining Methodology II (STA 6704)
  • Project in Data Analytics (CAP 6942)

Electives (must choose 2):

  • Parallel and Cloud Computation (COP 6526)
  • Social Media and Network Analysis (CAP 6315)
  • Computational Analysis of Social Complexity (CAP 6318)
  • Interactive Data Visualization (CAP 6737)
  • Data Preparation (STA 6714)
  • Machine Learning Methods for Biomedical Data (CAP 6545)

Students are expected to finish the program in 20 months following the cohort model. Special accommodations can be made for a student that can not continue in a given cohort to joint the next cohort. However, our program has been designed to be completed in 20 months and students will be taught in cohorts starting a year apart. As a result, a student that drops from a cohort may need to wait a year before having an available next cohort to join.

Students will enroll in cohorts that start each fall semester. Each semester they take two courses; thus all students are part-time students and it will not be possible to take courses full-time in the program. The students would all take the first 6 required courses in the first year of the program. The second year would be devoted to taking one more required course, two electives and the project course.

The plan of study is precisely the ten course series listed on the Program website.

Residency / Employment

Yes, and it is expected. The classes are scheduled on evenings or weekends to accommodate the schedules of working professionals. You are also welcome and encouraged to integrate your studies into your work environment where possible, although the Program has no requirement that you be working in data analytics during enrollment. Learners already working the data analytics sector can often take advantage of coursework on-the-job.

No. The program will be delivered by face to face instruction on the main campus of UCF.

There are no residence or citizenship requirements for this program. However, the program will be delivered by face to face instruction on the main campus of UCF at Orlando Florida.

Data Analytics is an emerging discipline that seeks to infer insights from large amounts of data (“big data”) by using various statistical techniques and algorithms. The discipline is concerned with both statistical techniques that measure the validity of such insights and with computational techniques for managing data and resources efficiently. There is a great need for people with technical skills in these areas, prompted by the large amounts of information that governments and businesses are collecting. Thus, this degree program aims to train people to develop algorithms and computerized systems to facilitate the discovery of information from big data.

There is an increasing demand for data analysts who can create, adapt, and use state-of-the-art tools to obtain insight from large structured and unstructured data sets, converting them into knowledge. Usually people with this training have the title of “data analyst” or “data scientist.” The US Bureau of Labor Statistics may classify people in these roles as statisticians, computer programmers, or other existing categories (such as “database administrator” or “software developer”). Graduates may go on to complete a Ph.D. in Computer Science, Statistics, or a related area and may also seek professional distinction.

Some examples of the job titles associated with this field can be given as:

Data Scientist • Data Analyst • Data Architect • Data Engineer• Data Mining Specialist • Business Intelligence Analyst• Big Data Engineer• Big Data Scientist• Database Administrator