Master of Science in Data Science & Artificial Intelligence

Deadlines

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.

	Fall
Domestic Applicants	June 1st
International Applicants	February 15th

The view the following information in a printable format, Click Here.

Degree Requirements

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)
• CAP 5602 Introduction to Artificial Intelligence (or equivalent course)

For Biostatics Students and Business Data Analytics Students only: Replace STA 6244 with the follow course equivalents:

• QMB 6357 Business Statistics Analysis (Business Data students)
• PHC 6052 Biostatics 1 (Biostatistics Data students)

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 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 (Spring only)
• EEL 5820: Digital Image Processing
• EEL 5813: Neural Networks-Algorithms and Applications

Artificial Intelligence:

Within this track, students with computing majors can readily design course sequences that help them Artificial Intelligence or Machine Learning.

Choose 5 from the list below:

• CAP 5109: Advanced Human-Computer Interaction
• CAP 5507: Game Theory
• CAP 5510C: Introduction to Bioinformatics
• CAP 5627: Affective Intelligent Agents
• CAP 5640: Grad Introduction to Natural Language Processing
• CAP 5610: Introduction to Machine Learning
• CAP 6619: Advanced Topics in Machine Learning
• CEN 5120: Expert Systems
• EEL 5820: Digital Image Processing
• EEL 5813: Neural Networks-Algorithms & Applications
• STA 6247: Data Analysis II

Business Data Analytics:

Choose 5 from the list below:

• ISM 6136: Business Analytics Applications
• ISM 6205: Database Management OR COP 5725: Principles of Database Management Systems
• ISM 6208: Data Warehousing OR ISM 6404: Business Data Visualization and Reporting OR CAP 5738: Data Visualization
• STA 6247: Data Analysis II
• CAP 6778: Advanced Topics in Data Mining OR COP 6727: Advanced Database Systems OR STA 6636: High Dimension Data Analysis OR CAP 5610: Introduction to Machine Learning OR CAP 5622: Machine Learning Techniques & Application

Biostatistics Data Analytics:

Choose 5 from the list below:

• PHC 6056: Longitudinal Health Data Analysis
• PHC 6059: Survival Data Analysis
• PHC 6064: Applied Statistical Methods for Discrete Data
• PHC 6067: Probabilistic Graphical Models
• PHC 6080: SAS Computing for Health Sciences
• PHC 6084: Intro to Bayesian Inference
• PHC 6099: R Computing (R methods for bio I and bio II)
• PHC 7083: Advanced Bayesian Inference
• PHC 7719: Multivariate Methods in Health Sciences Research
• PHC 6091: Biostatistics 2

Public Policy Analytics:

Within this track, students will  master the use of statistics, computer science, quantitative methods, and big data tools to create more effective public policies. They will be able to meld machine-generated data (e.g. sensors) with citizen-gathered data to predict events, mine social media and the internet for behavior pattens, and create effective and appealing data visualization programs to demonstrate the effects of decisions to politicians.

Required 5 from the list below:

• PAD 6306: Policy Analysis & Planning
• PAD 6053: Political, Social & Economic Context of Public Administration
• PAD 5256: Public Economics & Cost Benefit Analysis
• PUP 6006: Public Policy Analysis & Evaluation
• PAD 6434: Leadership & Decision-making
• STA 6247: Data Analysis II

Click here to view Rubric

IDC 6940: Capstone in Data Science (3 credits)

Course Description

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.

Credit Hours

The 3 credits of the DS Capstone course required for the MS-DS degree is best achieved over a span of two semesters. To facilitate this, you may split it into 1 credit first and 2 later, or vice versa. This allows you to start your capstone project in one term and complete it in the following term. Note that it is best to start the capstone in the term before you complete all your degree requirements. The system will technically allow you to take all 3 credits in one semester. You need to update the credits when you enroll for the course. This course does require permission, please reach out to the program advisor.

Learning Outcomes

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.

Sample Syllabus

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.

Capstone Process

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.

Sample projects can be found at data analysis challenge websites like https://www.kaggle.com and https://dreamchallenges.org/.

Suggested Timeline

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)

Project Guidelines

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.

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Appendix

Project Rubric

Category	Criteria	Meets Criteria	Score 1 – 10
Project Definition	Project Overview	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.
	Problem Statement	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	Metrics used to measure the performance of a model or result are clearly defined. Metrics are justified based on the characteristics of the problem.
Analysis	Data Exploration	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.
	Exploratory Visualization	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	Algorithms and techniques used in the project are thoroughly discussed and properly justified based on the characteristics of the problem.
	Benchmark	Student clearly defines a benchmark result or threshold for comparing performances of solutions obtained.
Methodology	Data Preprocessing	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.
	Implementation	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.
	Refinement	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.
Results	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.

Category	Criteria	Meets Criteria	Score 1 – 10
	Justification	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.
Conclusion	Free-form Visualization	A visualization has been provided that emphasizes an important quality about the project with a thorough discussion. Visual cues are clearly defined.
	Reflection	Student adequately summarizes the end-to-end problem solution and discusses one or two particular aspects of the project they found interesting or difficult.
	Improvement	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.
Overall Quality	Presentation	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.
	Functionality	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)

Faculty Mentors

Note: This section lists mentors associated with the Computational Data Analytics track only. Students in other tracks should contact the Specialization Track Coordinator associated with that track for more information. The track coordinators are Prof. Giri Narasimhan (Computational Data Analytics; giri@cs.fiu.edu), Dr. Karlene Cousins (Business Analytics Track; kcousins@fiu.edu), Prof. Changwon Yoo (Biostatistics Data Analytics; cyoo@fiu.edu), Prof. Shaoming Cheng (Public Policy Analytics; scheng@fiu.edu).

Faculty Mentor	Area(s)
Kemal Akkaya	Cybersecurity; User privacy, ML-based digital forensics; Network traffic analysis; Blockchain
M. Hadi Amini	Smart Cities, Machine Learning for Power Systems, Optimization
Janki Bhimani	Flash-Based Storage Systems, Emerging Memory Technologies, Cloud Computing, Performance Modeling, Resource Management, Capacity Planning
Leonardo Bobadilla	Geospatial, environmental and Ecological Data; Transportation Networks; Sensor Networks; Network Traffic
Bogdan Carbunar	Cybersecurity; Social Networks; Machine Learning
Dong Chen	Distributed & Tiny ML in IoT and Edge Computing devices
Shu-Ching Chen	Multimedia Databases; Machine Learning
Trevor Cickovski	High-performance bioinformatics pipelines, parallelism, and GPU computing
Wenqian Dong	ML, HPC
Mark Finlayson	Natural Language Processing (NLP); Cognitive Science; Digital Humanities
Xudong He	Systems Modeling; Machine Learning
S. S. Iyengar	Digital Forensics
Sumit Kumar Jha	AI and ML
Jason Liu	Simulation; Machine Learning
Amin Kharraz	Cybersecurity; Machine Learning
Christine Lisetti	HCI, Affective computing, Human-centered AI, Virtual reality, Virtual Intelligent Social Agents
Dongsheng Luo	AI and ML
Juan Mancilla-Caceres	AI and ML, Smart Cities
Ananda Mondal	Machine Learning/Deep Learning, Feature Extraction/Selection, Biomedical and Public Health Data
Giri Narasimhan	Computing Systems Performance; Biomedical and Public Health Data; Cultural, Social, Behavioral and Public Opinion Data; Public Policy; Emergency Management; Education; HPC in Data Analytics; Social Networks;
Cuong Nguyen	Artificial Intelligence and Machine Learning
Niki Pissinou	Adversarial Machine Learning; Hybrid Li-FI networks; Blockchains for resource constrained mobile-wireless sensor networks; Cybersecurity deception
Christian Poellabauer	Smart Health; Biomarker Development; Internet-of-Things; Speech Processing; Sensing and Sensor Data Analytics
Agoritsa Polyzou	Educational data mining; Fairness in machine learning, Recommender systems; Data mining
Raju Rangaswami	Systems Performance; Machine Learning
Gregory Reis	Environmental Science; Robotics; Machine Learning
Fahad Saeed	Biomedical & Public Health Data; HPC in Data Analytics; Image & Multimedia Processing
Mo Sha	Networks, ML
Farhad Shirani	Privacy, Security, ML
Ruimin Sun	CPS
Selcuk Uluagac	Cybersecurity; Internet of Things; Machine Learning
Xuyu Wang	Wireless Networks, IoT, AI Security
Yanzhao Wu	Big Data, AI/ML
Wenbin Zhang	Society and AI/ML

Industry Mentor	Area(s)
Bryan Lagae Data Analyst- Florida International University	Cultural, Social, Behavioral and Public Opinion Data; Education and University Data

Core Courses

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.

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.

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

Capstone

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

Elective courses are classified as CDA (applicable to Computational Data Analytics), BA (applicable to Business Analytics), BDA (applicable to Biostatistics Data Analytics), and PPA (application to Public Policy 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. AI, CDA

CAP 5507: Game Theory (3). Game representations, solution concepts, algorithms & complexity, repeated games, learning, auctions, voting application to many disciplines. Familiarity with mathematical proofs would be helpful. AI

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. AI, 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. AI, CDA,

CAP 5622: Machine Learning Techniques & Application (3). Practical introduction to Machine Learning: tools for Supervised/Unsupervised Learning, Reinforcement Learning, Best Practices/Practical Applications, Cloud Deployment of ML models. For non-CS majors. BA

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. AI, 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, BA

CAP 6619: Advanced Topics in Machine Learning (3). Advanced course on machine learning principles and techniques. Students propose, implement, and present a collaborative project with advanced machine learning techniques. Prerequisite: CAP 5610. AI

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

CEN 5120: Expert Systems (3). Introduction to expert systems, knowledge representation techniques and construction of expert systems. A project such as the implementation of an expert system in a high level AI-language is required. Prerequisites: COP 3530 or permission of the instructor. AI

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, BA

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) AI, CDA

EEL 5820: Digital Image Processing (3). Image Fundamentals, Image Transforms, Image Enhancement, Edge Detection, Image Segmentation, Texture Analysis, Image Restoration, and Image Compression. Prerequisite: EEL 3135 and knowledge of any programming language (FORTRAN, Pascal, C). AI, 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 6205 Database Management (3). Review techniques for structuring and managing data in organizations. Discusses data concepts, data modeling, database requirements definition, conceptual, logical, and physical design, and admin. 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 6247 Data Analysis II (3). Analysis of variance, regression analysis. Analysis of covariance, quality control, correlation, empirical distributions. Prerequisites: MAS 3105 and STA 6244. AI, CDA, PPA, 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 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 6059 Survival Data Analysis (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. Prerequisite: PHC 6052, PHC 6091. 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 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 6080 SAS Computing (3). Course covers essential computer-based techniques for the SAS system for data management and statistical analysis relevant to public health. Topics include: programming techniques, macro programming, and SQL with SAS. Prerequisites: PHC 6052, PHC 6091. BDA

PHC 6084 Intro to Bayesian Inference (3). This course will introduce students to probabilistic statistical inference using the Bayesian approach, as well as equip students to perform basic computing within the Bayesian statistical framework. Prerequisite: PHC 6052 and PHC 6091. BDA

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. 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

PHC 6099 R Computing (R methods for bio I and bio II) (3). This course will introduce R statistical software computing and analytics associated with topics discussed in Biostatistics I and II. Prerequisite: PHC 6052 Biostatistics I or equivalent statistics course with permission of instructor. Corequisite: PHC 6091 Biostatistics II. BDA

PHC 7083 Advanced Bayesian Inference (3). This course will cover advanced topics related to Bayesian methods such as prior assumptions, hierarchical modeling and computational MCMC methods with various sampling algorithms. Prerequisite: PHC 6084. BDA

PHC 7719: Multivariate Methods in Health Sciences Research (3) Advanced statistical methods for analysis of multivariate data and related statistical inference in public health and biomedical research. Prerequisites: PHC 6052, PHC 6091. BDA

PAD 6053: Political, Social & Economic Context of Public Administration (3). Examines the context in which public organizations operate, stressing the relationship between such organizations and their multifaceted environment. Emphasis is on examining relevant social and cultural mores and patterns, political values and processes, governmental institutions, economic systems, resource availability, and other environmental factors currently significant to public organizations. Prerequisite: Admission to the majors. PPA

PAD 6306: Policy Analysis & Planning (3). This course presents techniques and tools for the practice of policy analysis in public, nonprofit, and health organizations, with emphasis on constructing policy analysis useful to decisionmakers. Prerequisite: Admission to the majors. PPA

PAD 5256: Public Economics & Cost Benefit Analysis (3). This course provides the quantitative and qualitative tools and case material to solve allocation problems in the public sector. Applied microeconomic theory, welfare economics, and market and government failure are analyzed as are the public alternatives available. Cost-benefit analysis, the ethics of applied practice, and the important skills of communicating with decision makers are included. PPA

PUP 6006: Public Policy Analysis & Evaluation (3). A framework for evaluating public policymaking will be presented. The emphasis will be on criteria and methodologies available for choosing among alternative courses of action. The systems approach, alternative futures, and nth-order consequences of policies will be analyzed. PPA

PAD 6434: Leadership & Decision-making (3). Readings and case studies examine how effective leaders in the public and non-profit sectors make decisions in fluid and challenging environments. Prerequisite: Admission to the majors. PPA

Up-to-date information on tuition and fees is available at https://onestop.fiu.edu/finances/estimate-your-costs/.

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. 

 

Specialization Track Coordinator

• Computational Data Analytics Track
  • Rebeca Arocha <rarocha@fiu.edu>
• Business Analytics Track
  • Dr. Karlene Cousins, kcousins@fiu.edu
• Biostatistics Data Analytics track
  • Dr. Changwon Yoo, cyoo@fiu.edu
• Public Policy Analytics
  • Dr. Shaoming Cheng, scheng@fiu.edu

General Inquiries

For information on the MS in Data Science, please email msds-info@cs.fiu.edu.