A bachelor’s degree that is appropriate for the selected specialization, from a regionally accredited institution.
Computational Data Analytics students are required to have a bachelor’s degree in computer science, computer engineering, information technology, mathematics, statistics, or a related discipline. Students seeking to specialize in other tracks are required to have the appropriate academic background.
Business Analytics students are required to have a highly quantitative undergraduate business degree, including Accounting, Finance or Information Systems. The program also encourages applicants with degrees in Computer Science, Industrial Engineering, Mathematics, and Statistics. Applicants with several years work experience in a quantitative role would also be competitive absent a relevant undergraduate degree and coursework completed.
Biostatistics Data Analytics students are required to have the appropriate background as judged by the track’s Admissions Committee.
‘B’ average or better in all coursework attempted while registered as an upper-division student in the bachelor’s program (3.0 on a 4.0 scale). Applicants should have achieved undergraduate grades of B (at a minimum) in all undergraduate mathematics, statistics, and quantitative methods coursework.
For applicants whose native language is not English, English proficiency exam scores of at least 550 (paper-based) or 80 (internet-based) on the TOEFL or 6.5 on the IELTS. English proficiency exam exemptions are based on the country in which the applicant completed their undergraduate degree, not on nationality. See the list of TOEFL exempt countries here: http://gradschool.fiu.edu/toefl-exempt-countries/.
GRE scores with a minimum quantitative score of 148. The GRE requirement will not be waived.
Students must select a specialization when applying. Further information on each specialization is available under the “Requirements” tab and the “Courses” tab.
Online graduate application/application fee.
Official university/college transcripts from all institutions previously attended, mailed in a sealed institution envelope. Applicants who graduated from FIU do not need to request FIU transcripts.
Official translations of university/college transcripts (if in a language other than English).
If applicable, official TOEFL or IELTS scores reported by the testing agency. TOEFL scores can be sent to FIU using the code 5206. IELTS scores may be verified directly by the admissions officer if the applicant provides FIU with a copy of the score report.
Official GRE scores sent to FIU using code 5206.
Official proof of degree/diploma. The student may provide this upon arrival to FIU (within one term) if admitted. Applicants who graduated from U.S. universities are typically only required to submit final transcripts as the proof of degree; some exceptions may apply.
Translation of proof of degree/diploma (if in a language other than English).
Through the website, applicants will have the opportunity to upload supporting documents, such as a statement of purpose, resume, residency documents for tuition purposes, immigration documents, and the like. They can also provide contact information for recommenders who are then prompted to submit the letters of recommendation through the online portal.
Once submitted, Graduate Admissions will promptly acknowledge receipt of the application via email and will provide a Panther ID as well as further instructions on how to access the MyFIU portal. On MyFIU, students can view the status of their application, including any missing documents. Missing documents are listed under the “To Do List” on the top right-hand corner of the screen.
Once the applicant has been issued a Panther ID, they should include the Panther ID in all communications to the unit representatives, Graduate Admissions, International Student and Scholar Services, and Student Health Services.
The mailing address for couriers (typically used for delivering documents from outside the United States):
Office of International Admissions
Florida International University
11200 SW 8th Street, Miami FL 33199
SASC Building Room 440
FIU requires official documents, even for the initial review of the applications. Required official documents include transcripts, proof of degree, translations, and test scores (if applicable).
Transcripts must be received in a sealed university envelope issued by the academic institution, from all institutions previously attended. FIU conducts evaluations of foreign transcripts internally. Any transcripts that have been evaluated by a third party (e.g. WES, Josef Sinly, etc.) are considered unofficial by Graduate Admissions and will not fulfill the official transcript requirement. FIU will use evaluated documents for translation purposes only.
Official test scores must be reported to the School by the testing agency. The School’s code is 5206. IELTS scores may be verified directly by the admissions officer if the applicant provides FIU with a copy of the score report.
Please note that applications are not referred to the unit for review until official transcripts, translations, and test scores (if applicable) have been received. Uploaded copies of any of these documents are considered unofficial and will not be used in evaluating the application.
Once admitted, international students will also be required to show an official proof of degree, typically a diploma, but can do so upon their arrival to FIU.
This program admits for the fall term only.
All international applicants must abide the international applicant deadline. This includes international applicants residing in the United States and/or international applicants who do not require student visas.
When applying, students will select a specialization track among the following: Computational Data Analytics, Business Analytics, and Biostatistics Data Analytics. Students will complete the core and specialization curriculum according to their selected track.
Required Coursework: 12 credits
CAP 5768 Introduction to Data Science (new course)
CAP 5771 (or COP 5577) Principles of Data Mining
STA 6244 Data Analysis I (or equivalent course)
STA 6247 Data Analysis II (or equivalent course)
For Biostatistics Students only: Replace STA 6244 and STA 6247 with the following Biostatistics equivalents:
PHC 6052 Biostatistics 1 (equivalent course to STA 6244 Data Analysis I)
PHC 6091 Biostatistics 2 (equivalent course to STA 6247 Data Analysis II)
Capstone: 3 credits
ISM 6930 Special Topics in Management Information Systems (for Business Analytics Only), or
IDC 6940 Capstone Course in Data Science
Students in the Computational Data Analytics track can select the “Capstone” tab for more information. Students in other tracks should contact the faculty member associated with their track; see the “Contact” tab.
Specialization Tracks (15 credits)
Several specialization tracks have been developed to cater to enrolled students with different backgrounds, needs, and program specializations. Five elective courses are to be selected from a set of elective graduate courses per chosen track. With the permission of the academic advisor, students may be allowed to combine courses from one or more elective sequences if it enables better thematic specialization.
Computational Data Analytics: Within this track, students with computing majors can readily design course sequences that help them specialize in Bioinformatics, Medical Informatics, Financial computing, Network Traffic Analysis, Computing Forensics, Big Data algorithms, and much more.
Choose 5 from the list below
CAP 5109 Advanced Human-Computer Interaction
CAP 5510C Introduction to Bioinformatics
CAP 5602 Introduction to Artificial Intelligence
CAP 5610 Introduction to Machine Learning
CAP 5640 Graduate Introduction to Natural Language Processing
CAP 5738 Data Visualization
CAP 6776 Advanced Topics in Information Retrieval
CAP 6778 Advanced Topics in Data Mining
CEN 5082 Grid Enablement of Scientific Applications
CIS 5372 Fundamentals of Computer Security
CIS 5374 Information Security and Privacy
CIS 6931 Special Topics: Advanced Topics in Information Processing
COP 5725 Principles of Database Management
COP 6727 Advanced Database Systems
COT 6405 Analysis of Algorithms
COT 6936 Topics in Algorithms
TCN 6420 Modeling and Performance Evaluation of Telecommunications Networks
EEL 6803 Advanced Digital Forensics Engineering (taught as special topics course)
STA 6636 High Dimension Data Analysis
EEL 5820 Digital Image Processing
EEL 5813 Neural Networks-Algorithms and Applications
Choose 5 from the list below
ISM 6136 Business Analytics Applications
ISM 6208 Data Warehousing OR ISM 6404 Business Data Visualization and Reporting
CAP 6778 Advanced Topics in Data Mining
STA 6636 High Dimension Data Analysis
COP 6727 Advanced Database Systems
Biostatistics Data Analytics
Choose 5 from the list below
PHC 6064 Models for Binary Public Health Outcomes
PHC 6067 Probabilistic Graphical Models
PHC 6056 Longitudinal Health Data Analysis
PHC 6060 Principles and Approaches to Biostatistical Consulting
PHC 6059 Cohort Studies and Lifetime Events in Public Health
PHC 6093 Biostatistical Data Management Concepts and Procedures
This is a capstone project course using Python, SQL, R, and/or other specialized analysis toolkits to synthesize concepts from data analytics and visualization as applied to industry-relevant projects.
The goal of IDC 6940 is to carry out an industry-relevant project in applied Data Science that synthesizes concepts from databases, modeling, analytics, visualization and management of data. Given the professional nature of the MS degree program in Data Science, it is essential that students have experience with analyzing real data sets.
Every capstone project requires a project mentor. The project mentor can assist in identifying, planning and/or executing the data science project. Students will meet periodically with their project mentor(s) to discuss project progress and results, and to troubleshoot. Projects will be implemented in Python, SQL, R, and/or using other specialized analysis toolkits used by Data Scientists.
Projects may involve individual or team effort.
Students will be evaluated by a committee of faculty members and assigned a letter grade. The course will have a coordinator in addition to the mentors/supervisors for individual projects.
The class will meet biweekly to learn from analysis case histories, monitor project progress, have class presentations, and evaluate project progress reports.
The MS-DS degree requires 3 credits of IDC 6940. Currently, the course has been approved only as a 3-credit course. However, we anticipate that in the near future, the course may be taken for 1-3 credits in any given semester. In other words, the required 3 credits IDC 6940 may be spread out over more than one semester. Students are encouraged to spread out IDC 6940 over more than one semester to enable completion of substantial and meaningful projects.
Students will synthesize concepts from data science, including data analytics and visualization. Students will learn to identify good data sets and good questions to explore the data.
Students will learn to strategize how to address the goals of the data exploration. Students will learn to apply the concepts to industry-relevant projects.
Students will learn how to communicate the results via oral presentations and written reports.
The class will meet biweekly to learn from case histories of data analysis and will have invited speakers from the industry.
The class will also be used to monitor project progress, have class presentations, and evaluate project progress reports.
Students will be provided a list of faculty members who can be faculty mentors for the capstone project in IDC 6940. This will also be provided on the course website.
Students are encouraged to identify an external mentor in addition to their project mentor from FIU. The external mentor may be from the industry and may be more knowledgeable about the project domain. The external mentor may help in identifying good data sets, may help in guiding the student to ask industry-relevant questions and may help in interpreting and evaluating results of the project.
At the end of the project, students will make a 15 to 30 minute oral presentation and submit a detailed written project report, including links to relevant data sets and code (which can be shared via a service such as github). If the students are working in teams, only one joint presentation and report is required.
A committee of three will evaluate the projects. This committee will include the track coordinator, the faculty mentor and the external mentor. If the project does not have an external mentor or if the track coordinator is the faculty mentor, then a third committee member will be invited from the list of approved project mentors.
The following is a suggested timeline for students to complete the capstone project. Note: students should plan to complete the capstone course in their final 1 to 2 semesters before graduation.
Step 1: Selecting Mentors (Semester 1)
Step 2: Selecting a Dataset (Semester 1)
Step 3: Planning the Project (Understanding the domain, identifying data analysis questions, identifying analysis tools, writing a proposal) (Semester 1)
Step 4: Pre-Project Review (oral presentation of planned project and incorporating feedback into project) (Semester 1)
Step 5: Project Implementation (Applying analysis tools, preparing initial report, meeting with domain experts for preliminary evaluation of results, interpreting results with help of domain experts, re-analyzing data after discussion and feedback with experts) (Semester 2)
Step 6: Oral Presentation (Semester 2)
Step 7: Final Report Submission (Semester 2)
Students are encouraged to find projects from their professional area or from their domain of interest. This is best achieved by talking to domain experts from industry. Faculty mentors may assist in this process.
Projects need to be substantive and meaningful. Data Analysis projects may be designed to test one or more hypotheses (e.g., does factor X cause event Y), or may be exploratory in nature (e.g., what factors may be responsible for event Y). Data analysis projects must explain the choice of approach, tools and visualization. In many cases, different approaches applied to the same data may shed different light on the datasets and it may be reasonable to apply more than one approach. In many cases, different visualization approaches can help highlight different results and conclusions. Where appropriate, statistically sound analyses should be performed. Statistical significance of conclusions should be inferred, where appropriate.
Domain-specific interpretations must be made from the results with the help of the mentors. Re-analysis of the data may be necessary after discussion with the domain experts. Sufficient time should be set aside to allow for an iterative process of refining the data analysis and interpretation.
The Mid-point Review will involve a presentation of the proposed data set and the analytical questions that will be pursued in the project. A one-page proposal will be submitted by each project team and will orally defend the proposal in front of the evaluation committee. The committee will examine the proposal for the nature of the project, the tools to be used, and the potential for successful completion, and will provide feedback to the project team. The oral presentation for the mid-point review should explain the tools and methods to be used and the processing for arriving at the conclusions. The final oral presentation should explain the methods used and the conclusions made. The final report must be detailed and comprehensive and written in a form that the work can be reproduced. Supplementary material, including source code, executables and results must also be submitted for evaluation.
The rules for plagiarism will be discussed and provided at the start of the class or on the course website.
Score 1 – 10
Student provides a high-level overview of the project in layman’s terms. Background information such as the problem domain, the project origin, and related data sets or input data is given.
The problem which needs to be solved is clearly defined. A strategy for solving the problem, including discussion of the expected solution, has been made.
Metrics used to measure the performance of a model or result are clearly defined. Metrics are justified based on the characteristics of the problem.
If a dataset is present, features and calculated statistics relevant to the problem have been reported and discussed, along with a sampling of the data. In lieu of a dataset, a thorough description of the input space or input data has been made. Abnormalities or characteristics about the data or input that need to be addressed have been identified.
A visualization has been provided that summarizes or extracts a relevant characteristic or feature about the dataset or input data with a thorough discussion. Visual cues are clearly defined.
Algorithms and techniques used in the project are thoroughly discussed and properly justified based on the characteristics of the problem.
Student clearly defines a benchmark result or threshold for comparing performances of solutions obtained.
All preprocessing steps have been clearly documented. Abnormalities or characteristics about the data or input that needed to be addressed have been corrected. If no data preprocessing is necessary, it has been clearly justified.
The process for which metrics, algorithms, and techniques were implemented with the given datasets or input data has been thoroughly documented. Complications that occurred during the coding process are discussed.
The process of improving upon the algorithms and techniques used is clearly documented. Both the initial and final solutions are reported, along with intermediate solutions, if necessary.
Model Evaluation and Validation
The final model’s qualities — such as parameters — are evaluated in detail. Some type of analysis is used to validate the robustness of the model’s solution.
Score 1 – 10
The final results are compared to the benchmark result or threshold with some type of statistical analysis. Justification is made as to whether the final model and solution is significant enough to have adequately solved the problem.
A visualization has been provided that emphasizes an important quality about the project with a thorough discussion. Visual cues are clearly defined.
Student adequately summarizes the end-to-end problem solution and discusses one or two particular aspects of the project they found interesting or difficult.
Discussion is made as to how one aspect of the implementation could be improved. Potential solutions resulting from these improvements are considered and compared/contrasted to the current solution.
Project report follows a well-organized structure and would be readily understood by its intended audience. Each section is written in a clear, concise and specific manner. Few grammatical and spelling mistakes are present. All resources used to complete the project are cited and referenced.
Code is formatted neatly with comments that effectively explain complex implementations.
Output produces similar results and solutions as to those discussed in the project.
Oral Presentation Rubric (to be developed)
Note: This section lists mentors associated with the Computational Data Analytics track only. Students in other tracks should contact the faculty member associated with that track for more information.
Artificial Intelligence and Machine Learning
Image and Multimedia Processing
Physical Sciences and Astronomy Data
Education and University Data
Financial and Economic Data
Cultural, Social, Behavioral, and Public Opinion Data
M. Hadi Amini
Smart Cities, Machine Learning for Power Systems, Optimization
Geospatial, environmental and Ecological Data
Transportation Networks, Sensor Networks and Network Traffic
Natural Language Processing (NLP)
High-Performance Computing in Data Analytics
Disaster Recovery and Extreme Events and Government Data
Computing Systems Performance; Biomedical and Public Health Data; Cultural, Social, Behavioral and Public Opinion Data; Disaster Recovery and Extreme Events and Government Data; Education and University Data; High-Performance Computing in Data Analytics; Social Network Data and Network Science
Computing Systems Performance; Biomedical and Public Health Data; High-Performance Computing in Data Analytics; Image and Multimedia Processing
Bryan Lagae Data Analyst- Florida International University
Cultural, Social, Behavioral and Public Opinion Data; Education and University Data
CAP 5768 Introduction to Data Science (3). Foundations of databases, analytics, visualization and management of data. Practical data analysis with applications. Introduction to Python, SQL, R, and other specialized data analysis toolkits. Prerequisites: STA 3164 or equivalent.
CAP 5771 Principles of Data Mining (3). Introduction to data mining concepts, knowledge representation, inferring rules, statistical modeling, decision trees, association rules, classification rules, clustering, predictive models, and instance-based learning. Prerequisites: COP 4710 and STA 3033.
STA 6244 Data Analysis I (3). Exploratory data analysis; testing of distributional assumptions; Chi-square tests, tests for means, variances, and proportions. Prerequisites: STA 3033, STA 4322, or STA 6327.
STA 6247 Data Analysis II (3). Analysis of variance, regression analysis. Analysis of covariance, quality control, correlation, empirical distributions. Prerequisites: MAS 3105 and STA 6244.
PHC 6052 Biostatistics I (3). An introduction to basic biostatistical techniques for MPH students majoring in Biostatistics, but also open to those seeking a thorough understanding of and ability to use the essential biostatistical procedures. Prerequisites: Familiarity with basic algebra and basic calculus is important. Biostatistics Data Analytics only
PHC 6091 Biostatistics 2 (3). Continuation of Biostatistics I. Covers advanced methods for ANOVA, different regression and correlation techniques and survival analyses. Prerequisite: PHC 6052. Biostatistics Data Analytics only
IDC 6940 Capstone Course in Data Science (3). Projects course using Python, SQL, R, and/or other specialized analysis toolkits to synthesize concepts from data analytics and visualization as applied to industry relevant projects. Prerequisite: CAP 5768
ISM 6930 Special Topics in Management Information Systems (IS) (1-6). To study the recent developments in the MIS field not otherwise offered in the curriculum, such as office automation, computer graphics, etc. Prerequisites: Advanced standing and department chairman approval. Business Analytics only
Elective courses are classified as CDA (applicable to Computational Data Analytics), BA (applicable to Business Analytics), and BDA (applicable to Biostatistics Data Analytics). Students must request permission before taking a course outside of their track.
CAP 5109 Advanced Human-Computer Interaction (3). Fundamental concepts of human-computer interaction, cognitive models, user-centered design principles, evaluation techniques, and emerging technologies in various contexts and domains. CDA
CAP 5510C Introduction to Bioinformatics (3). Introduction to bioinformatics; algorithmic, analytical and predictive tools and techniques; programming and visualization tools; machine learning; pattern discovery; analysis of sequence alignments, phylogeny data, gene expression data, and protein structure. Prerequisites: COP 3530, or equivalent and STA 3033 or equivalent. CDA
CAP 5602 Introduction to Artificial Intelligence (3). Presents the basic concepts of AI and their applications to game playing, problem solving, automated reasoning, natural language processing and expert systems. Prerequisite: COP 3530. CDA
CAP 5610 Introduction to Machine Learning (3). Decision trees, Bayesian learning reinforcement learning as well as theoretical concepts such as inductive bias, the PAC learning, minimum description length principle. Prerequisite: Graduate standing. CDA
CAP 5640 Graduate Introduction to Natural Language Processing (3). The concepts and principles of computer processing of natural language, including linguistic phenomena, formal methods, and applications. Students will conduct an independent research project. Prerequisites: M.S. or Ph.D. standing or permission of the instructor. CDA
CAP 5738 Data Visualization (3). Advanced class on data visualization principles and techniques. Students propose, implement, and present a project with strong collaborative and visual components. CDA
CAP 6776 Advanced Topics in Information Retrieval (3). Information Retrieval (IR) principles including indexing and searching document collections, as well as advanced IR topics such as Web search and IR-style search in databases. Prerequisite: COP 5725. CDA
CAP 6778 Advanced Topics in Data Mining (3). Web, stream data, and relational data mining, graph mining, spatiotemporal data mining, privacy-preserving data mining, high-dimensional data clustering, social network, and linkage analysis. Prerequisite: CAP 5771 or permission of the instructor. CDA, BA
CEN 5082 Grid Enablement of Scientific Applications (3). Fundamental principles and applications of high performance computing and parallel programming using OpenMP, MPI, Globus Toolkit, Web Services, and Grid Services. Prerequisites: Graduate standing or permission of the instructor. CDA
CIS 5372 Fundamentals of Computer Security (3). Information assurance algorithms and techniques. Security vulnerabilities. Symmetric and public key encryption. Authentication and Kerberos. Key infrastructure and certificate. Mathematical foundations. Prerequisite: Graduate standing. CDA
CIS 5374 Information Security and Privacy (3). Information Security Planning, Planning for Contingencies, Policy, Security Program, Security Management Models, Database Security, Privacy, Information Security Analysis, Protection Mechanism. Prerequisite: CIS 5372. CDA
CIS 6931 Special Topics: Advanced Topics in Information Processing (3). This course deals with selected special topics in information processing. Prerequisite: Permission of the instructor. CDA
COP 5725 Principles of Database Management Systems (3). Overview of Database Systems, Relational Model, Relational Algebra and Relational Calculus; SQL; Database Applications; Storage and Indexing; Query Evaluation; Transaction Management. Selected database topics will also be discussed. CDA
COP 6727 Advanced Database Systems (3). Design, architecture and implementation aspects of DBMS, distributed databases, and advanced aspects of databases selected by the instructor. Prerequisite: Graduate standing CDA, BA
COT 6405 Analysis of Algorithms (3). Design of advanced data structures and algorithms; advanced analysis techniques; lower bound proofs; advanced algorithms for graph, string, geometric, and numerical problems; approximation algorithms; randomized and online algorithms. Prerequisite: Graduate standing. CDA
COT 6936 Topics in Algorithms (3). Advanced data structures, pattern matching algorithms, file compression, cryptography, computational geometry, numerical algorithms, combinational optimization algorithms and additional topics. Prerequisite: COP 3530. CDA
TCN 6420 Modeling and Performance Evaluation of Telecommunications Networks (3). Covers methods and research issues in the models and performance evaluation of high-speed and cellular networks. Focuses on the tools from Markov queues, queuing networks theory and applications. Prerequisites: TCN 5030 or equivalent. CDA
EEL 6803 Advanced Digital Forensics (3). This course provides students with the advanced skills to track and counter a wide range of sophisticated threats including espionage, hacktivism, financial crime syndication, and APT groups. Prerequisite: EEL 4802. CDA
EEL 5813 Neural Networks-Algorithms and Applications (3). Various artificial neural networks and their training algorithms will be introduced. Their applications to electrical and computer engineering fields will be also covered. Prerequisite: Permission of the instructor. (SS) CDA
ISM 6136 Business Analytics Applications (IS) (3). This course covers business analytics skills required to conduct both pattern discovery (e.g., segmentation and association) and predictive modeling (e.g., decision trees and neural network mining). Prerequisites: Permission of department and introductory statistics. BA
ISM 6208 Data Warehousing (IS) (3). Data Warehousing and Online Analytical Processing tools will be utilized to organize and analyze large volumes of data in order to explain the past, monitor the present, and anticipate the future. BA
ISM 6404 Business Data Visualization and Reporting (3). Introduction to reporting and data visualization principles and techniques to support business decision making and information reporting needs utilizing operational, accounting and financial data. BA
STA 6636 High Dimension Data Analysis (3). Statistical techniques used to analyze high dimensional data sets. Topics include machine learning, high dimensional data, discriminant analysis and clustering. Prerequisites: STA 6246 and STA 5236 or equivalent. CDA, BA
PHC 6064 Models for Binary Public Health Outcomes (3). This course will offer students a focused introduction to statistical models for the analysis of binary medical and public health data. The course will provide an introduction to the application of statistical models for PH outcomes in epidemiology, dietetics and nursing. Prerequisite: PHC 6052 or permission of the instructor. BDA
PHC 6067 Probabilistic Graphical Models (3). Concepts and implementation of Probabilistic Graphical Models, comparative study the models, and their suitability for various datasets. Prerequisites: PHC 6052, PHC 6091, or permission of the instructor. BDA
PHC 6056 Longitudinal Health Data Analysis (3). Applied longitudinal health data analysis; methods to compare different health treatments and behavioral interventions. Focus will be on models for single and multiple correlated public health outcomes. Prerequisites: PHC 6052, PHC 6091 or permission of the instructor. BDA
PHC 6060 Principles and Approaches to Biostatistical Consulting (3). The course specifically addresses the process of providing biostatistical consulting support for public health, medical and clinical research. Prerequisites: PHC 6052, PHC 6091, PHC 6093. BDA
PHC 6059 Cohort Studies and Lifetime Events in Public Health (3). Concepts of lifetime events and survival data in Public Health; modern methods used to analyze time-to-event data; non-parametric and parametric models. Prerequisites: PHC 6065; PHC 6013. Corequisite: PHC 6091. BDA
PHC 6093 Biostatistical Data Management Concepts and Procedures (3). Covers procedures and tools for data management, including data collection, transfer, handling, quality and security issues for research projects for public health, medicine, and related fields. BDA
Currently, the cost per credit hour for a graduate-level course is $455.64 for Florida residents and $1001.69 for non-Florida residents. The estimated cost of a full-time spring or fall semester (9 credits) is $4,295.15 for Florida residents and $9,209.60 for non-Florida residents. The M.S. in Data Science consists of 30 credits. The estimated total cost for a full-time student is $14,446.76 for Florida residents and $30,828.26 for non-Florida residents. These estimates do not include online course fees. Tuition and fees are paid on a semester basis.
Tuition, fees, and the above estimates are subject to change. Estimated costs may not reflect costs paid.