Undergraduate Data Science and Analytics Degree Program

This program aims to prepare students with the essential skill sets across disciplines needed for data-driven applications in industry, business and government. The Department of Electrical Engineering & Computer Science (EECS) offers a concentration in Data Science and Engineering (DSE). For information on the concentrations offered by other colleges, click here.

About

Data science is effective in tackling many real-world problems and is being used to make intelligent and informed decisions in many industries such as medicine and healthcare, finance, retail, transportation, manufacturing, media and entertainment. Data science and engineering involves the design and application of tools and methodologies for uncovering patterns in data and making actionable predictions. This program builds a strong background in computational and mathematical foundation of data science, programming, and data structures and algorithm analysis. Students can choose from a rich selection of courses including machine learning, artificial intelligence, and deep learning. They will be prepared not only to use the tools for data science but also to develop new mechanisms and algorithms. Graduates of the program will be well prepared to enter the high demand workforce under exciting roles such as Data Scientist, Data Science Application Developer, Data Science Programmer, Data Analytics Scientist, Machine Learning Scientist, Market Analyst and others.

Data Science and Analytics, DSE concentration, Undergraduate Degree Program Information

• Admission Requirements

• Degree Requirements

• Course Description

• Sample Flight Plan (pdf)

• Undergraduate Mission, Objectives and Student Outcomes

• Student Learning Outcomes Assessment (pdf)

• General Education Curriculum

Data Science Certificate

Admission Requirements

• Submission of official transcripts is required from all applicants.
• Students must satisfy the prerequisites required for each course in the certificate program.
• All five courses in the program must be completed with a grade of "C" or better.

Undergraduate Degree Program in Data Science and Analytics

Course Descriptions

Introductory Statistics (STA 2023) 3 credits

Prerequisite: MAT 1033 or MAC 1105 or MGF 1106 or MAC 2233

An introductory course covering descriptive statistics, probability, binomial and normal distributions, sampling distributions and hypothesis tests, and sampling procedures. Laboratory required. This is a General Education course.

Mathematics of Data Science (MAP 2190) 3 credits

Prerequisite: MAC 1105 with “C” or better

This course will survey mathematical foundations in data science. Topics may include modeling with functions, matrices, solving linear systems, differentiation, integration, multivariate thinking and geometry, regression models, optimization, sensitivity analysis, and graph theory.

Experimental Design and Analysis (CAP 2750) 3 credits

Prerequisite: STA 2023

This course deals with principles of experimental design and data analysis. Topics covered include design of experiments, sampling and analysis of resulting data.

Tools for Data Science (CAP 2751) 3 credits

Prerequisite: None

This course will focus on data manipulation, curation, visualization, exploration, interpretation, and modeling using standard packages and tools employed in the field of data science, as well as best practices for maintaining data and software using version control.

Data Management and Analysis with Excel (QMB 3302) 3 credits

Prerequisite: None

An introductory course covering basic Excel skills for managing information and data, analyzing data, visualizing data through charts and pivot tables, creating scenarios, using functions and automating tasks.

Artificial Intelligence for Social Good (CCJ 3071) 3 credits

Prerequisite: None

In this course students will learn about the social implications of artificial intelligence, data science, and big data, strategies to ensure these systems are accountable to the communities and contexts they are meant to serve, and applied in ways that promote justice and equity.

Data Science Capstone (ISC 4312) 1-3 credits

Prerequisite: Senior standing in the BS in Data Science and Analytics and having completed all core courses

Students in the BS program with Major in Data Science and Analytics will apply theoretical knowledge, methods, and tools to a real-world data science problem. Students can work individually or in teams under the supervision of the course instructor or another faculty member.

Introduction to Computational Mathematics (MAD 2502) 3 credits

Prerequisite: MAC 2281 or MAC 2311

An introduction to mathematical computation by means of algorithmically solving a number of mathematical problems. Introduction to C++. The emphasis will be on the mathematical algorithms involved with problems from analysis, number theory, combinatorics, algebra, linear algebra, numerical analysis and probability.

RI: Introduction to Data Science (CAP 3786) 3 credits

Prerequisite: COP 2220 or MAD 2502

This research-intensive (RI) course surveys the foundational topics in data science: Data acquisition, data exploration and visualization, data analysis with statistics and machine learning, data at scale via working with big data. The course uses statistical software to work through real-world examples that illustrate these concepts. Concurrently, students learn statistical and mathematical foundations that power the data scientific approach to problem solving.

Computational Statistics (STA 3100) 3 credits

Prerequisites: (MAC 2312 or MAC 2282), STA 2023 or higher, and some programming experience

Computer algorithms for evaluation, simulation and visualization, random number generation, sampling from prescribed distributions. Simulations, graphics for data display, computation of probabilities and percentiles, hypothesis testing, simple linear regression and multiple regression.

SAS for Data and Statistical Analyses (STA 3024) 3 credits

Prerequisite: STA 2023 or equivalent

This course introduces the SAS language in a lab-based format. The objective is to develop programming and statistical computing skills to address data management and analysis issues using SAS. The course provides an extensive survey of some of the most common statistical tools and provides decision-making strategies in selecting the appropriate statistical method for   the data at hand.

Probability and Statistics 1 (STA 4442) 3 credits

Prerequisite: MAC 2282 or MAC 2312

An introductory course treating combinatorics, probability spaces, laws of large numbers, and central limit theorem. An introduction to Markov processes, information theory and applications.

Probability and Statistics 2 (STA 4443) 3 credits

Prerequisite: STA 4442

Properties of test statistics, estimation and testing, linear models, contingency tables; topics from non-parametric statistics, design of experiments or methods of inference.

Applied Statistics 1 (STA 4234) 2 credits

Prerequisite: STA 4442; Corequisite: STA 4202L

Point and interval estimation, hypothesis tests, non-parametric procedures, contingency tables. Essential distribution theory. Linear models, including multiple regression and analysis of variance. Emphasis on data analysis, statistical graphics, and diagnostics via personal computing.

Applied Statistics 1 Lab (STA 4202L) 1 credit

Prerequisite: STA 4442 with grade of "C" or better Corequisite: STA 4234

This is a first course in regression analysis. Regression analysis explores relationships among variables by modeling a response. The course focuses on data analysis, statistical graphs and diagnostics via personal computing.

Applied Statistics 2 (STA 4702) 3 credits

Prerequisite: STA 4234

Multivariate statistical methods, including the multivariate normal distribution, component analysis, factor analysis, multivariate analysis of variance and regression, discriminant analysis, and causal modeling. Students will use SAS and/or SPSS statistical software.

Statistical Designs (STA 4222) 3 credits     

Prerequisites: STA 4234, and one of MAC 2282 or 2312

Basic concepts of experimental design: randomized blocks, Latin squares, incomplete blocks, factorial designs, fractional factorials, nested designs. Introduction to design of sample surveys: simple random, stratified, cluster sampling; complex designs; ratio and regression estimation; enumerative versus analytical surveys. Student project required.

Applied Time Series and Forecasting (STA 4853) 3 credits

Prerequisite: STA 4234 or equivalent

Gives a basic introduction to time series and forecasting methods that can be applied to finance, economics, engineering and the natural and social sciences. Topics covered include stationary processes, ARMA models, modeling and forecasting with ARMA processes, spectral analysis and non-stationary and seasonal time series models.

Introduction to Biostatistics (STA 3173) 3 credits

Prerequisite: MAC 2233 with a grade a "C" or better

Introduces basic statistical concepts and procedures that are necessary to conduct statistical analysis for biological researchers. The topics covered are probabilistic foundations, experimental designs and their analyses, summarizing and visualizing data, inferential statistics, including hypothesis tests and regression modeling.

RI: Industrial Problems in Applied Math (MAP 4913) 3 credits

Prerequisites: (MAP 2302 or MAP 3305) and (MAS 2103 or MAC 2313)

This research-intensive course pits students in small groups against real-world problems provided by industrial partners.

Applied Mathematical Modeling (MAP 4103) 3 credits     

Prerequisites: (MAP 2302 or MAP 3305) and (MAS 2103 or MAC 2313)

This course covers the use of differential and difference equations in scientific modeling. Emphasis is on the "modeling" cycle with undergraduate research and inquiry (URI) components.

Topology for Data Science (MTG 4328) 3 credits

Prerequisites: MAS 2103, MAD 2104, and (MAD 2502 or COP 2220)

Introduction to concepts and methods in applied topology and topological data analysis tools, including persistent homology, and their uses in data science: topological spaces, metric spaces, continuity, simplicial complexes, vector spaces, and simplicial homology. Mathematical concepts are grounded by discussions of efficient implementations of computational algorithms and applications.

Graph Theory (MAD 4301) 3 credits

Prerequisites: MAD 2104 and MAS 2103

A first course in theory and applications of graphs including basic properties; coloration; algebraic and geometric aspects; enumeration; algorithms; network flows.

Cryptography and Information Security (CIS 4362) 3 credits

Prerequisites: MAS 2103 and MAD 2502

Classical cryptology, entropy. Stream and block ciphers. Public-key versus symmetric cryptography, one-way and trap-door functions. Primality and factorization, DLP, Diffie-Hellman, RSA and ElGamal cryptosystems. Issues of computer and network security. Secure protocols, identification, authentication, digital signatures, secret sharing schemes.

Introduction to Programming in C (COP 2220) 3 credits

P rerequisite: None

Introduction to programming in C. Variable types, arithmetic statements, input/output statements, loops, conditional statements, functions, arrays and structures. Programming projects in C.

Foundations of Computer Science (COP 3014) 3 credits

Prerequisite: COP 2220 with a “C” or better

Builds programming skills with an emphasis on disciplined program design and coding. Introduction to object-based programming concepts including class design and implementation. Programming in C++.

Data Structures and Algorithm Analysis (COP 3530) 3 credits

Prerequisites: COP 3014 with a "C" or better;    Prerequisite or Corequisite: MAD 2104

The design, implementation and run-time analysis of important data structures and algorithms. The data structures considered include sorted arrays, linked lists, stacks, queues, and trees. An approach based on abstract data types and classes will be emphasized. The use of recursion for algorithm design. Class design and implementation in C++. Programming assignments in the C++ language.

Introduction to Data Science and Analytics (CAP 4773) 3 credits

Prerequisites: COP 3530 and STA 4821 with minimum grades of "C" or permission of instructor

This course deals with the principles of data science and analytics. Topics covered include statistical analysis of data, measurement techniques and tools, machine learning methods, knowledge discovery and representation, classification and prediction models.

Introduction to Deep Learning (CAP 4613) 3 credits

Prerequisite: COP 3530 with minimum grade of "C"

This course teaches students basic concepts of deep learning. The course covers three major topics, including statistical machine learning, neural network structures and deep neural networks. Detailed topics include introduction to machine learning algorithms, perceptron learning, multi-layer neural networks, and deep neural network structures and learning algorithms. The lectures include practical sessions dedicated to the implementation and programming of deep learning frameworks.

Introduction to Artificial Intelligence (CAP 4630) 3 credits

Prerequisite: COP 3530 or ISM 4234

A broad introduction to the core concepts of artificial intelligence, including knowledge representation, search techniques, heuristics and deduction. Programming in Lisp and possibly other software environments.

Introduction to Data Mining and Machine Intelligence (CAP 4770) 3 credits

Prerequisites: STA 4821 and COP 3530

This course deals with the principles of data mining. Topics include machine learning methods, knowledge discovery and representation, classification and prediction models.

Introduction to Computer Systems Performance Evaluation (CEN 4400) 3 credits

Prerequisite: COP 3014, 3014L, and STA 4821

Principles of the quantitative evaluation techniques for computer system hardware and software, emphasizing the establishment and analysis of performance criteria. Deterministic and stochastic methods will be discussed.

Introduction to Database Structures (COP 3540) 3 credits

Prerequisite: COP 3530

An introduction to the design, implementation and use of file managers and relational data base systems. Topics include secondary storage devices, hash and indexed file structures, and the relational data base language SQL. Programming assignments will be done in the C language and in SQL.

Applied Database Systems (COP 4703) 3 credits

Prerequisite: COP 3540

Investigation of state-of-the-art facilities provided by object-relational database systems using Oracle as a vehicle. Java and the Java database interface, JDBC, are considered. Also, server-side web programming with dynamic SQL and CGI, PL/SQL, Java servlets, and JavaServer Pages (JSP) are considered. No prior knowledge of Java or web programming is assumed.

Python Programming (COP 4045) 3 credits

Prerequisite: COP 3530 with minimum grade of "C"

This course is an introduction to the Python programming language with applications to practical problem solving involving data manipulation and analysis. The first part of the course focuses on teaching the basics of the Python language. Topics covered   are data structures (lists, arrays, dictionaries, sets, comprehensions), functions, files and object-oriented language elements. In the second part of the course, students learn to apply advanced language features and methodologies in combination with third-party libraries for scientific computation to develop real-world applications.

Introduction to Internet Computing (COP 3813) 3 credits

Prerequisite: COP 3014

This course teaches students how to design web pages and develop websites at the introductory to intermediate level. The course is project oriented. Students are required to finish several Internet-based projects using the tools introduced in class.

Introduction to Business Analytics and Big Data (ISM 3116) 3 credits

Prerequisite: ISM 3011 or ACG 4401

Provides an understanding of the business intelligence processes and techniques used in transforming data to knowledge and value in organizations. Students also develop skills to analyze data using generally available tools (e.g., Excel).

Business Communication for Data Analysts (GEB 3231) 3 credits   

Prerequisites: Junior standing, admission to College of Business, and ISM 3116

This course introduces students to essential communication skills used by successful data analysts: interpersonal/team membership, concise business and technical writing, confident speaking, effective organizational strategies, critical thinking/analysis, appropriate technical language and formats, and productive job-search approaches within the MIS field. This course builds on analysis of data in ISM 3116 to show how it can be communicated effectively to audiences both within and outside the MIS field.

Data Mining and Predictive Analytics (ISM 4117) 3 credits

Prerequisite: None

Introduces the core concepts of data mining (DM), its techniques, implementation and benefits. Also identifies industry branches that most benefit from DM, such as retail, target marketing, fraud protection, health care and science and web and e- commerce. Detailed case studies and using leading mining tools on real data are presented.

Advanced Business Analytics (ISM 4403) 3 credits

Prerequisite: ISM 3116

An in-depth examination of business analytics methods of visualization, data mining, text mining and web mining using various analytical tools. Applications to smaller firms are investigated in a laboratory setting.

Contemporary Issues of Digital Data Management (ISM 4041) 3 credits

Prerequisite: None

Covers business processes and frameworks for data collection, storage, retrieval and transfer of digital data. Discusses the various ways through which industry and government compile data for purposes such as marketing, customer relationship management, fraud and crime prevention, e-government, etc. Considers also the business, legal, ethical and social context of data gathering and utilization.

Management of Information Assurance and Security (ISM 4323) 3 credits

Prerequisite: None

Emphasizes information security policy development, security management planning, risk assessment and risk management, disaster recovery and business continuity, and personnel issues related to security management.

Database Management Systems (ISM 4212) 3 credits

Prerequisite: ISM 3011 or ACG 4401

Focuses on the development of well-formed databases for the purpose of data management from the initial design of the database to the implementation and query and to the application of database management tools and techniques such as data security for use in business and government organizations.

Social Media and Web Analytics (ISM 4420) 3 credits

Prerequisite: None

Covers concepts and techniques for retrieving, exploring, visualizing and analyzing social network and social media data,  website usage and clickstream data. Students learn to use key metrics to assess goals and return on investment, perform social network analysis to identify important social actors, subgroups and network properties in social media.

Business Analytics for Marketing and Customer Relationship Management (MAR 4615) 3 credits

Prerequisite: This course is open to students in the Bachelor of Business Administration or Bachelor of Science in Data Science and Analytics. MAR 3023 or permission of the instructor.

In this course, students will learn about customer databases, statistical tools for analyzing customer data, implementation of selective tools in data spreadsheets, and application of generated knowledge for marketing, especially customer management, decisions.

Revenue Management and Predictive Analytics in the Hospitality and Tourism (HFT 4881) 3 credits

Prerequisites: None

Exploration of revenue management, big data, and predictive analytics within the hospitality and tourism industry. The course will use a viewpoint of firm value and overall contribution to financial performance. Students will identify direct links between big data and firm performance while utilizing strategic management, prediction, and forecasting. A variety of data sources will be examined. Through analysis, students will learn to manage firms using an analytic culture that turns information into insight.

Spatial Data Analysis (GEO 4167C) 3 credits

Prerequisite: GEO 4022

Designed to help geographers, geologists, earth scientists, and other professionals explore a range of spatial analytical techniques. The emphasis is on the choice and application methods for the analysis of the various types of spatial data that are commonly encountered and analyzed in geographic information systems.

Photogrammetry and Aerial Photograph Interpretation (GIS 4021C) 3 credits

Prerequisites: None

Principles of aerial photography and photogrammetry including the photographic production process, electromagnetic principles, history of aerial photography and aerial platforms, elements of visual image interpretation, and analog and digital (soft copy) photogrammetric methods.

Geospatial Databases (GIS 4118) 3 credits

Prerequisite: GIS 4043C

Geospatial databases provide the functions of storing, managing and querying geospatial data and are essential components of Geographical Information Systems (GIS). This course covers the fundamental principles, techniques and methodologies for designing and implementing a geospatial database and querying and geoprocessing in geospatial databases.

Applications in Geographic Information Systems (GIS 4048C) 3 credits

Prerequisite: GIS 4043C or equivalent

Advanced technical, implementation and application issues in geographic information systems. Geocoding, algorithms for 2- and 3-dimensional representations, and system planning and implementation issues.

Computational Physics (PHZ 3151C) 4 credits

Prerequisites: MAC 2313, PHY 3101C

The course covers selected topics in numerical computation and computer-assisted analysis, with applications to physical systems.

Solar System Astronomy (AST 3110) 3 credits

Prerequisites: AST 2002 and PHY 2053

An intermediate, interdisciplinary course on the nature and dynamics of the solar system through applications of physics, atmospheric science, chemistry and geology. The course expands students' understanding of the different bodies in the solar system, of the fundamental principles of Earth processes to explain/predict processes on other bodies in or outside the solar system and to help them to consider the bodies for future exploration.

Mathematical Methods for Physics (PHZ 4113) 4 credits

Prerequisite: MAP 3305

This course develops applied mathematics for the physical sciences. It introduces integral transform, Green's function and orthogonal function expansion methods for solving differential equations. It also examines selected advanced topics, such as complex variables.

Practical Cell Neuroscience (PCB 4843C) 3 credits

Prerequisites: PCB 3063 with minimum grade of "B-"

This course focuses on understanding neurophysiological signaling at the cellular level. It looks at signaling from the perspective of single ion channels to cellular synaptic transmission. Students learn through both theory and practical laboratory experiments and apply these principles in an experimental proposal that they present and execute, resulting in a final report.

Laboratory Methods in Biotechnology (BSC 4403L) 3 credits 

Prerequisites: MCB 3020, MCB 3020L, BCH 3033 and PCB 3063

Course offers hands-on experience in some of the basic and essential lab skills required in molecular biology and biotechnology that are directly transferable to the workplace. Concepts behind designing and implementing controlled experiments involving manipulation of DNA, RNA and protein are discussed.

Epidemiology of Infectious Diseases (MCB 4276) 3 credits

Prerequisites: None

This course examines the basic principles of epidemiology in the context of infectious diseases. Topics include the distribution and determinants of disease. Case studies from current literature supplement textbook material. The course places a strong emphasis on quantitative aspects of the field, including experimental design and basic statistics.

Concepts in Bioinformatics (BSC 4434C) 3 credits

Prerequisites: PCB 3063; open to students in Biology, Bioengineering, Science/Engineering and Computer Science

The course outlines concepts underlying the mining of the human genome, blending biology, medicine and engineering.

RI: Research Methods in Political Science (POS 3703) 3 credits

Introduction to the scope and methodology of political analysis. Includes introductory examinations of research design, survey research, computer applications, data analysis, and library research. (Course should be completed by the end of second semester of junior year.) This is a research-intensive (RI) course.

Public Opinion and American Politics (POS 4204) 3 credits

Prerequisite: POS 2041 with minimum grade of "C"

Political beliefs, values and attitudes of the American public; mass participation in public affairs; voting behavior; compliance and support for public policies. Linkages between the mass public and government in the United States.

Sociological Analysis: Quantitative Methods (SYA 4400) 3 credits

Design and execution of original research on social class, race, ethnicity, gender, and other issues central to contemporary sociology. Students explore various quantitative techniques using the Statistical Package for the Social Sciences (SPSS) and national survey and census data.

Research Methods in Bioarchaeology (ANT 4192) 3 credits

Prerequisite: ANT 4141, ANT 4514 or permission of instructor

Training in the research methodology of biological anthropology and archaeology. Application to an original research project and the presentation of a written research report.

Information Technology in Public Administration (PAD 3712) 3 credits

Provides a basic introduction to public sector information technology and e-governance. Topics include: computer software and network basics, information infrastructures (their structures, characteristics, applications and policy aspects), implications for government functioning and interactions with the public.

Introduction to the Nonprofit Sector (PAD 4144) 3 credits

This is a multidisciplinary course examining the historical, political, legal, ethical and societal environments in which nonprofit organizations operate. This primarily includes institutions involved with education, social services, health care, and the arts. The course is intended for students who are seeking to enter the nonprofit field and those who have considerable experience working in nonprofits.

Research Methods for Public Management (PAD 4704) 3 credits

The course describes research practices used in the public sector by introducing methodologies, techniques, and decision tools. Areas of study include the research process, sampling procedures, research design, measurement, primary and secondary data, and the collection and analysis of data. In addition, computer applications and presentation of research reports (oral and written) are covered.

Quantitative Inquiry for Public Managers (PAD 4702) 3 credits

Prerequisite: STA 2023

This course introduces students to basic statistical concepts and quantitative methods of inquiry in public management using relevant examples and applications. Successful students should be able to apply statistical concepts and techniques toward effective decision making and evaluation of a wide variety of information.

Criminal Justice Technology (CJE 3692C) 3 credits

Lab course that includes an overview and application of computer hardware and software with criminal justice data for criminal justice purposes. Course also includes discussion of concepts and practice as well as helps prepare students for the criminal justice workplace environment.

Crime Analysis (CJE 4663) 3 credits

An introduction to crime analysis and crime mapping, this course examines types of techniques used to study crime and disorder patterns and problems in law enforcement today. It covers the theory, data collection methods, and statistics used as well as the history of career opportunities for crime analysis.

Computer Crime (CJE 4668) 3 credits

This course provides an overview of computer crime from a criminal justice perspective. It also examines computer crime prevention, computer security, legal and social issues, and modern investigative methodologies.

Teen Technology Misuse (CCJ 4554) 3 credits

Twenty-first century teens have employed communications technology to mistreat, embarrass, harass, control, threaten or abuse others. This includes, but is not limited to, cyber bullying, sexting, the criminal use of Facebook, electronic dating violence, predation and stalking. Students learn of the sociological, criminological, developmental and practical implications of this problem and how it can be addressed.

Methods of Research in Criminal Justice (CCJ 4700) 3 credits

Prerequisite: STA 2023

A study of the purpose of research, the logic of scientific inquiry and research techniques in criminal justice.

Research Methods in Social Work (SOW 4403) 3  credits

Prerequisite: SOW 3302

Introduction to the principles and methods of basic social work research, ethical conduct of research within the context of social work purposes and values. Formulation of problems for study that address the social needs of diverse population groups.

Undergraduate Degree Program in Data Science and Analytics, DSE Concentration

Admission Requirements

All students must meet the minimum admission requirements of the University. Refer to the Admissions section of the University Catalog.

Prerequisite Coursework for Transfer Students

Students transferring to Florida Atlantic University must complete lower-division requirements including the requirements of the Intellectual Foundations Program, College Algebra and Introductory Statistics. Lower-division requirements may be completed through the A.A. degree from any Florida public college, university or community college, or through equivalent coursework at another regionally accredited institution. Before transferring and to ensure timely progress toward the BSDSA degree, students must also complete the prerequisite courses for their major as outlined in the  Transition Guides. 

All courses not approved by the Florida Statewide Course Numbering System that will be used to satisfy requirements will be evaluated individually on the basis of content and will require a catalog course description and a copy of the syllabus for assessment.

Degree Requirements

The minimum number of credits required for the Bachelor of Science with major in Data Science and Analytics is 120 credits: 36 credits in the Intellectual Foundations (IFP) Program, 48 credits of major requirements and 36 credits of electives. Additional requirements:

1. A minimum of 45 upper-division credits;
2. Students must attain a minimum grade of "C" in all major courses to receive credit in the major; and
3. No major course may be taken with a pass/fail grade.

The 48 required credits for the major are listed below.

Common Core

* The Capstone for the B.S. degree with major in Data Science and Analytics (ISC 4941) is a cross college course that can be taken multiple times with a minimum of 3 credits as a requirement for the degree. Students apply their theoretical knowledge, methods and tools acquired during the Data Science and Analytics program to a real-world problem and engage in processing data and applying appropriate analytic methods to the problem. Students implement a solution using appropriate tools and can work individually or in teams under the supervision of the course instructor or another faculty member. This can be accomplished in three ways: an approved Project, Research Experience or Written Thesis.

Concentration Core Requirements

Concencration Core Electives

Choose three courses or four courses so that the total of concentration credits is 21.

Electives

Choose two courses from the Electives Tables so that the total of elective credits is 6.

Arts and Letters Electives

Business Electives

Engineering Electives

Science Electives

Social Work and Criminal Justice Electives

Undergraduate Degree Program in Data Science and Analytics

Undergraduate Mission, Objectives and Student Outcomes

Mission

The mission of this undergraduate program is to provide students with interests in Data Science and Data Analytics a unique and multifaceted educational opportunity within and across each of its areas of concentration. The program will integrate world-class expertise from FAU’s Big Data Platform, FAU’s NSF Big Data Training and Research Laboratory, and FAU’s Center for Cryptology and Information Security (nationally recognized as CAE-R through DHS/NSA), as well as distinguished faculty in departments across five colleges.

The capstone experience in this program will provide high quality, data-driven research opportunities for undergraduate scholarship. This program will also foster interdisciplinary collaboration and community engagement among the participating colleges and industry partners.

Educational Objectives

Within three to five years of graduation, DSA graduates are expected to exhibit the following professional characteristics:

• Career advancement: They will be successful in practicing the profession of computer engineering through their education and training in critical thinking skills, analytical and problem-solving ability, engineering design and development expertise, and communication and teamwork experience.
• Professionalism: They will act with both professional and social responsibility in their career field, including a commitment to protect both occupational and public health and safety, and a commitment to apply ethical standards related to the practice of engineering.
• Lifelong learning: They will continue to develop their knowledge and skills through progress toward or completion of graduate education, and/or other professional development for successful adaptation to technological and cultural changes in society.

Student Outcomes

Based on the Educational Objectives of the BSDAS program, the department has established the following student learning outcomes for the baccalaureate program in Data Science and Analytics.

Graduates will have:

• An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
• An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
• An ability to communicate effectively with a range of audiences
• An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
• An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
• An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
• An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

MS with Major in Data Science and Analytics, DSAL Concentration - Admission Requirements

• Students are expected to have a bachelor’s degree in computer science, engineering, science, information technology, information systems, or a related field. However, those who have pursued a different area of study are encouraged to apply. Students are expected to have completed a college-level programming course, a calculus course (MAC 2233 Method of Calculus or equivalent), and a course in statistics. The admissions committee will evaluate the application holistically to determine applicant suitability using several factors such as academic performance, GPA, background, and experience. The admission committee may assign remedial courses on a case-by-case basis.
• Submit official transcripts from previous institutions attended. The desirable minimum bachelor GPA is 3.0 (of a 4.0 maximum) in the last 60 credits attempted prior to graduation.
• The GRE is not required for this program.
• International students from non-English-speaking countries must be proficient in written and spoken English as evidenced by a TOEFL score of at least 500 (paper-based test), 213 (computer-based test), or 79 (Internet-based test), or at least 6.0 on the IELTS.
• Meet other requirements of the FAU Graduate College.

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MS with Major in Data Science and Analytics - Data Science and Engineering (DSAL) Concentration

Students enrolled in this concentration will take three core courses:

1. CAP 5768  Introduction to Data Science
2. CAP 6673  Data Mining and Machine Learning
3. STA 5195  Biostatistics, or 
   ISM 6404  Introduction to Business Analytics & Big Data, or 
   POS 6934  Special Topics (Quantitative Methods)

Complete four concentration courses with prefix “CAP.”  Can also take CEN 6405 Computer Performance Modeling as one of the four concentration courses.                

Complete three elective courses (9crs) from the list below if Non-Thesis option.  Complete one course (3 crs) from the list below if Thesis option.

Database and Cloud Computing

• CDA 6132  Multiprocessor Architecture                                  
• CEN 5086  Cloud Computing                      
• COP 6726  New Directions in Database Systems                               
• COP 6731  Theory and Implementation of Database Systems                                     
• ISM 6217   Database Management Systems                         

Data Mining and Machine Learning

• CAP 5615   Introduction to Neural Networks                        
• CAP 6546   Data Mining for Bioinformatics                            
• CAP 6618   Machine Learning for Computer Vision                           
• CAP 6619   Deep Learning                             
• CAP 6629   Reinforcement Learning                         
• CAP 6635   Artificial Intelligence                                 
• CAP 6673   Data Mining and Machine Learning                                    
• CAP 6778   Advanced Data Mining and Machine Learning                             
• CAP 6776   Information Retrieval                                
• CAP 6777   Web Mining                                    
• CEN 6405   Computer Performance Modeling                                      
• ISM 6136   Data Mining and Predictive Analytics                                 

Data Security and Privacy

• CIS 6370   Computer Data Security                          
• CTS 6319  Cyber Security: Measurement and Data Analysis                       
• ISM 6328  Management of Information Assurance and Security                                 
• MAD 5474  Introduction to Cryptology and Information Security                                 
• MAD 6478  Cryptanalysis                                
• PHY 6646  Quantum Mechanics/Computing 2                                    

Scientific Applications and Modeling

• GIS 6028C  Photogrammetry & Aerial Photography Interpretation                              
• GIS 6032C  LiDAR Remote Sensing and Applications                       
• GIS 6061C  Web GIS                         
• GIS 6112C  Geospatial Databases                             
• GIS 6127   Hyperspectral Remote Sensing                           
• GIS 6306   Spatial Data Analysis                                 
• PHY 6938  Quantum Information Processing                      
• PHZ 5156  Computational Physics                            
• PHZ 7609  Numerical Relativity                                   

Social Data Science

• ANG 6090  Advanced Anthropological Research 1                            
• ANG 6092  Advanced Anthropological Research 2                            
• ANG 6486  Quantitative Reasoning in Anthropological Research                               
• CAP 6315  Social Networks and Big Data Analytics                          
• COM 6316  Quantitative Communications Research                       
• POS 6934   Quantitative Methods                              
• POS 6736   Research Design in Political Science                                
• SYA 6305    Seminar in Advanced Research Methods                      

Statistics and Data Applications

• BSC 6459   Biomedical Data and Informatics                      
• MTG 6329   Applied Computational Topology                       
• STA 5195    Biostatistics                                   
• STA 6106    Statistical Computing                              
• STA 6177    Survival Analysis                        
• STA 6197    Biostatistics – Longitudinal Data Analysis                                       
• STA 6207    Applied Statistical Methods                                   
• STA 6208    Regression Analysis                                   
• STA 6326    Mathematical Statistics                          
• STA 6857    Applied Time Series Analysis                                 

Business Analytics                         

• CAP 6315   Social Networks and Big Data Analytics                         
• CAP 6780   Big Data Analytics with Hadoop                         
• CAP 6807   Computational Advertising & Real-time Data Analytics                          
• ISM 6136  Data Mining and Predictive Analytics                                  
• ISM 6217  Database Management Systems                         
• ISM 6404  Introduction to Business Analytics and Big Data                          
• ISM 6405  Advanced Business Analytics                                 
• ISM 6555  Social Media and Web Analytics                          
• QMB 6303  Data Management and Analysis with Excel                                     
• QMB 6603  Data Analysis for Manager

MS with Major in Data Science and Analytics, DSAL Concentration - Degree Requirements

The Master of Science with Major in Data Science and Analytics, Data Science and Engineering (DSAL) concentration offers both thesis and non-thesis options. Both options require a minimum of 30 credits. Students must satisfy all University graduate requirements. In addition, the following requirements must be met:

1. At least one-half of the credits must be at the 6000 level or above.
2. Minimum GPA of 3.0 (out of 4.0) or better.
3. All courses in the degree program must be completed with a grade of "C" or better.

Transfer Credits

Any transfer credits toward the requirements for a master's degree in Data Science and Analytics, DSAL concentration must be approved by the department, the College and the University. The transfer credits must correspond to equivalent requirements and performance levels expected for the degree. Normally no more than 6 credits of coursework (that have not been applied to a degree) can be transferred from another institution.

MS with Major in Data Science and Analytics

The Master of Science with Major in Data Science and Analytics (MSDSA) aims to prepare students with the essential skill sets needed to analyze small, fast, big, massive and complex data. Graduates of the program will be well prepared with hands-on experience and data analytics skills to enter the high demand workforce under exciting roles such as Data Engineer, Data Analytics Scientist, Software Engineer, Machine Learning Engineer, Market Analyst and others. The Department of Electrical Engineering and Computer Science (EECS) offers a concentration in Data Science and Engineering (DSAL).

Program Information

• Program Worksheet  - for students who started in Spring 2025 or earlier
• Program Worksheet - for students who started in Summer 2025 or later

Apply Online

Department of Mathetmatical Sciences Track

Provided by the College of Science.

Undergraduate Certificates

Actuarial Science

The certificate in actuarial science enables students to pursue a course of study relevant to actuarial science.

For more information about careers in Actuarial Science or what an Actuary does, please visit BeAnActuary.org.

The required curriculum provides students with necessary mathematical foundations of the field and exposes them to practical applications relevant to their chosen area of specialization.

Each course must be completed with a grade of at least C-.

To complete the certificate program in actuarial science, one has to complete the following required courses:

* As with all degree programs, the authoritative source for the degree requirements is the University Catalog that was in effect for the academic year in which the student entered the University. The information on this page does not supersede the Catalog.

For information about the Certificate Program in Actuarial Science, contact:

Mr. P. Pina, Actuarial Advisor 
Department of Mathematical Sciences 
Science & Engineering Bldg., Room 284 
Boca Raton Campus
Office Phone: 561-297-3340
Email: ppina@fau.edu or broer@fau.edu

Data Science

Data Science is the study of methods to manage, analyze and extract knowledge from data. This 15-credit certificate program has two tracks: Mathematical Sciences (MathSci) and Computer Science and Analytics (CS).

The Data Science certificate draws the 15 credits from Computer Science, Mathematics and Statistics.

Statistics

The certificate program in Statistics enables students to pursue an interdisciplinary course of study in statistics. The required curriculum provides students with necessary statistical foundations of the field and exposes them to practical applications relevant to their chosen area of specialization.

• Certificate Application
• Certificate Checklist

Please submit the application to Dr. Lun Ching-Chang ( Science Building; SE43, Room 222)

Each course must be completed with a grade of at least "C-." The total number of credits required for this certificate is 26.  To obtain the certificate in Statistics, the student must complete the following required/elective courses:

As with all degree programs, the authoritative source for the degree requirements is the University Catalog that was in effect for the academic year in which the student entered the University. The information on this page does not supersede the Catalog. 

For information about the Certificate Program or Minor in Statistics contact:

Lun-Ching Chang, Statistics Advisor
Department of Mathematical Sciences
Science & Engineering Bldg., Room 222
Boca Raton Campus
Office Phone: 561-297-3351
Email: changl@fau.edu

Graduate Certificates

Cyber Security Graduate Certificate

The Cyber Security certificate provides opportunities for graduate students to expand their knowledge and skills to meet the needs of the cyber security field.

Cybercrime-related issues especially impact the State of Florida because a significant part of the state's economic development comes from tourism, international banking and high-tech industries. The number of scientists, engineers, and experts needed with special skills in cybersecurity exceeds the number available. The Cyber Security certificate provides opportunities for graduate students to expand their knowledge and skills to meet the needs of the cybersecurity field. Due to their extensive expertise and facilities, the departments of Computer and Electrical Engineering and Computer Science and Mathematical Sciences have jointly designed the Cyber Security certificate. This 12-credit certificate program has two tracks: Computer Science (CS) and Mathematics (Math).

Mathematics Track

The Cyber Security certificate with a track in Mathematics will be granted to a student who completes four 3-credit courses as follows:

• Three 3-credit courses from the Math Cyber Security course list.
• One 3-credit course from either the Math or the CS Cyber Security course list.

Admission

Open to students who have a bachelor's degree in mathematics or in a related field and a GPA of at least 3.0.

• Students must satisfy the prerequisites for each course in the program.
• All four courses must be completed with a GPA of 3.0 or better.
• All course materials are in English; all international students must demonstrate proficiency in English to enter the program.

Cyber Security Courses by Track

For information about the Graduate Certificate Program in Cybersecurity, contact:

Prof. Edoardo Persichetti , Cybersecurity Advisor
Prof. Veronika Kuchta , Cybersecurity Advisor
Prof. Francesco Sica , Cybersecurity Advisor

For information about the Ph.D., MS, and AMST programs, contact:

Prof. Hongwei Long, Graduate Director 
Department of Mathematical Sciences
Florida Atlantic University
777 Glades Road
Boca Raton, FL 33431
Email: mathgraduate@fau.edu