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ACADEMIC PROGRAMS

 

Interdisciplinary Programs

Florida Atlantic University’s interdisciplinary programs capitalize on the advancements and knowledge of various academic disciplines to offer students unique programs of study. By combining related and sometimes unrelated disciplines across FAU colleges, these programs prepare students for multifaceted careers.

The programs listed below are offered across multiple colleges. The University also offers interdisciplinary programs shared by various departments within a college. Those programs are listed within the specific college sections.

Undergraduate Programs
FAU Max Planck Honors Program

Undergraduate Certificates
Applied Mental Health Services
Data Science

Graduate Programs
Master of Science with Major in Data Science and Analytics
Master of Science with Major in Information Technology and Management

Graduate Certificates
Big Data Analytics
Cyber Security
Geographic Information Systems
Transportation, Logistics and Supply Chain Management

Undergraduate Programs

The FAU Max Planck Honors Program (MPHP)

Eligible College of Science majors in Biology, Psychology, and Neuroscience and Behavior may apply to participate in this Jupiter-specific honors program for undergraduates. For students pursuing the MPHP, 3 to 6 of the elective credits in their individual program must be applied toward the requirements of the MPHP. These include successful completion of a Capstone experience (1 to 3 credits) and three different MPHP Enrichment courses (1 credit each) from those listed below. A minimum grade of "B" must be achieved in graded courses ("S" in non-graded courses) among these exclusive MPHP course options for the credits to count toward the requirements of the MPHP. Visit the MPHP website to apply.

FAU Max Planck Honors Program Required Coursework
 
Core Course (required for all participants)
Honors Introduction to Neuroscience Research PSB 4003 1
 
Enrichment Course Electives (at a minimum, two different courses are required)
Honors Scientific Communication BSC 4934 1
Honors Advanced Cell Imaging for Neuroscientists PCB 4933C 1
Honors Advanced Genetics PCB 4935 1
Honors Advanced Physiology PCB 4937C 1
Honors Advanced Scientific Grant Writing PCB 4956 1
Honors Advanced Techniques in Neuroscience PSB 4112C 1
Honors Directed Independent Research PSB 4916 0-3
Honors Symposium Presentation PSB 4922 1
Honors Special Topics in Neuroscience PSB 4931 1
Max Planck Honors Seminar PSB 4932 1
Honors Journal Club in Neuroscience PSB 4951 1
 
Capstone Options (at least 3 credits in one of the following courses is required)
FAU Max Planck Honors Capstone PSB 4902 1-3
Honors Mentored Research PSB 4910 1-3
FAU Max Planck Honors Thesis PSB 4970 1-3

Undergraduate Certificates

Applied Mental Health Services Certificate (Changes effective spring 2020.)
The undergraduate certificate in Applied Mental Health Services, offered jointly by the Department of Psychology and by the Department of Counselor Education in the College of Education, provides a curricular experience for students who wish to pursue careers in clinical psychology, mental health counseling and allied human services that enhances the student's chosen major. This program is also specialized training for students who wish to pursue graduate degrees in these critical-need careers.

Students who have completed 60 credits with a GPA of 3.0 or better may apply for the certificate program. The program requires a minimum of 17 15 credits by completing the psychology and counselor education courses below. Students must attain a 3.0 GPA or better to qualify for the certificate. A grade of "C-" or better (unless otherwise noted in the course description) is required in all psychology courses taken as part of the requirements for the Applied Mental Health Services certificate. Students receiving a bachelor's degree in the Department of Psychology will meet the requirements for certification by completing the courses listed below, as well as their prerequisites. Students from other departments should meet with an advisor to determine eligibility and requirements for this certificate program. Students who qualify will receive a certificate of completion and a notation on their transcript.

Required Courses (15 credits)
Abnormal Psychology CLP 4144 3
Clinical Psychology CLP 4343 3
Neuropsychology PSB 4240 3
Psychology and the Law SOP 4751 3
Career and Lifespan Development SDS 3340 3
Interpersonal Communication Skills SDS 4410 3
Elective Courses (2 credits, minimum)
Special Topics (in Counseling) *, ** MHS 5930 3
University Student Mentoring and Peer Coaching * SDS 3483 2
Psychology and the Law SOP 4751 3

* Course offered in the Department of Counselor Education in the College of Education.
** Prerequisite: Permission of instructor.

Data Science Certificate
Data Science is the study of methods to manage, analyze and extract knowledge from data. Industry and government need an educated workforce with the necessary expertise to make use of the enormous volumes of data available to them. Due to their extensive expertise and facilities, the departments of Mathematical Sciences and Computer and Electrical Engineering and Computer Science have jointly designed the Data Science certificate. This 15-credit certificate program has two tracks: Mathematical Sciences (MathSci) and Computer Science (CS). The Data Science certificate draws the 15 credits from Computer Science, Mathematics and Statistics.

Admission
The program is open to students who satisfy the prerequisite courses required for each course in the certificate curriculum. Both tracks - MathSci and CS - require two core courses and three elective courses for a total of 15 credits. All five courses must be completed with a grade of "C" or better.

Core Courses - 6 credits
RI: Introduction to Data Science CAP 3786 3 or
Introduction to Data Science and Analytics CAP 4773 3
Probability and Statistics for Engineers STA 4032 3 or
Probability and Statistics 1 STA 4442 3 or
Stochastic Models for Computer Science STA 4821 3
Elective Courses by Track - 9 credits  
MathSci Track - Select two from the following courses and one more from this list or the list of CS elective courses. *Recommended electives.
RI: Computational Statistics STA 4102 3
Statistical Designs STA 4222 3
RI: Statistical Learning * STA 4241 3
Applied Statistics 1 STA 4234 2
Applied Statistics 1 Lab STA 4202L 1
Applied Statistics 2 * STA 4702 3
Applied Time Series and Forecasting STA 4853 3
CS Track - Select two from the following courses and one more from this list or the list of MathSci elective courses.
Introduction to Deep Learning CAP 4613 3
Introduction to Artificial Intelligence CAP 4630 3
Introduction to Data Mining and Machine Intelligence CAP 4770 3
Introduction to Computer Systems Performance Evaluation CEN 4400 3
Introduction to Database Structures COP 3540 3
Applied Database Systems COP 4703 3

Graduate Programs

Master of Science with Major in Data Science and Analytics
(New program effective fall 2019.)

The Master of Science with Major in Data Science and Analytics (MSDSA) is a multi-college interdisciplinary program jointly administered by the Department of Mathematical Sciences in the Charles E. Schmidt College of Science, the Department of Computer & Electrical Engineering and Computer Science in the College of Engineering and Computer Science, the Department of Information Technology and Operations Management in the College of Business and the Department of Political Science in the Dorothy F. Schmidt College of Arts and Letters. The program aims to prepare students with essential skill sets needed to analyze small, fast, big, massive and complex data. To allow for maximum flexibility in career aspirations, students may select from four concentrations:

Admission Requirements (Changes effective summer 2020.)
To be admitted to the MSDSA program, applicants must:

1. Have obtained a bachelor's degree from an accredited institution and possess a minimal background consisting of MAC 2233 (Methods of Calculus) or equivalent, and STA 2023 (Introductory Statistics) or equivalent and computer programming (COP 2220 or MAD 2502) or equivalent. Students applying to the Data Science and Engineering concentration must have completed a college-level introductory programming course with a minimum grade of "C." Knowledge of Python and statistical packages such as R, as well as coursework in linear algebra are recommended for all concentrations;

2. Have an undergraduate GPA of 3.0 or higher in the last 60 credits of undergraduate coursework;

3. Submit two letters of recommendation for all concentrations, except the Data Science and Engineering concentration;

4. Have attained scores of at least 151 (verbal) and 151 (quantitative) on the Graduate Record Examination (GRE). GRE scores more than five years old are not acceptable normally. The Data Science and Engineering concentration requires the submission of the GRE score (verbal and quantitative sections), but no minimum values are required;

5. Be proficient in written and spoken English. International students from non-English-speaking countries must present a score of at least 500 (paper-based test) or 213 (computer-based test) or 79 (internet-based test) on the Test of English as a Foreign Language (TOEFL) or a score of at least 6.0 on the International English Language Testing System (IELTS); and

6. Meet other requirements of the FAU Graduate College.

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Curriculum Requirements (Changes effective summer 2020.)
The MSDSA program offers both thesis and non-thesis options. Both options require a minimum of 30 credits. Students are required to take one common core course, two additional core courses, four concentration courses and three elective courses for the total of 30 credits. The exact courses taken are to be determined by the students and their advisory committee. The thesis option requires only one elective course and 6 thesis credits. Students selecting the thesis option must complete and defend a written thesis successfully.


Data Science via Scientific Inquiry Concentration

Common Core Courses
Introduction to Data Science CAP 5768 3
Biostatistics STA 5195 3
Take one two additional core courses
Data Mining and Machine Learning CAP 6673 3
Biomedical Data and Informatics BSC 6459 3 or
Introduction to Business Analytics and Big Data ISM 6404 3 or
Special Topics (Quantitative Methods) POS 6934 3
Take four concentration courses
Computer Data Security CIS 6370 3
Cyber Security: Measurement and Data Analysis CTS 6319 3
Introduction to Cryptology and Information Security MAD 5474 3
Graph Theory MAD 6307 3
Cryptanalysis MAD 6478 3
Applied Computational Topology MTG 6329 3
Biostatistics STA 5195 3
Statistical Computing STA 6106 3
Survival Analysis STA 6177 3
Regression Analysis STA 6236 3
Mathematical Statistics STA 6326 3
Applied Time Series Analysis STA 6857 3
Take three elective courses from the Electives Table. Thesis option requires only one elective course and 6 thesis credits.


Data Science and Engineering Concentration

Common Core Courses
Introduction to Data Science CAP 5768 3
Data Mining and Machine Learning CAP 6673 3
Take one two additional core courses
Biomedical Data and Informatics BSC 6459 3 or
Biostatistics STA 5195 3 or
Introduction to Business Analytics and Big Data ISM 6404 3 or
Special Topics (Quantitative Methods) POS 6934 3
Take four concentration courses
Introduction to Neural Networks CAP 5615 3
Social Networks and Big Data Analytics CAP 6315 3
Data Mining for Bioinformatics CAP 6546 3
Machine Learning for Computer Vision CAP 6618 3
Deep Learning CAP 6619 3
Information Retrieval CAP 6776 3
Web Mining CAP 6777 3
Advanced Data Mining and Machine Learning CAP 6778 3
Big Data Analytics with Hadoop CAP 6780 3
Computational Advertising and Real-Time Analytics CAP 6807 3
Computer Performance Modeling CEN 6405 3
Take three elective courses from the Electives Table. Thesis option requires only one elective course and 6 thesis credits.


Data Analytics in Business Concentration

Common Core Courses
Introduction to Data Science CAP 5768 3
Introduction to Business Analytics and Big Data ISM 6404 3
Take one two additional core courses
Biostatistics STA 5195 3 or
Biomedical Data and Informatics BSC 6459 3 or
Data Mining and Machine Learning CAP 6673 3 or
Special Topics (Quantitative Methods) POS 6934 3
Take four concentration courses
Quantitative Communication Research COM 6316 3
Data Mining and Predictive Analytics ISM 6136 3
Database Management Systems ISM 6217 3
Advanced Business Analytics ISM 6405 3
Social Media and Web Analytics ISM 6555 3
Data Management and Analysis with Excel QMB 6303 3
Data Analysis for Managers QMB 6603 3
Take three elective courses from the Electives Table. Thesis option requires only one elective course and 6 thesis credits.


Data Science in Society Concentration

Common Core Courses
Introduction to Data Science CAP 5768 3
Special Topics (Quantitative Methods) POS 6934 3
Take one two additional core courses
Biostatistics STA 5195 3 or
Biomedical Data and Informatics BSC 6459 3 or
Data Mining and Machine Learning CAP 6673 3 or
Introduction to Business Analytics and Big Data ISM 6404 3
Take four concentration courses
Advanced Anthropological Research 2 ANG 6092 3
Quantitative Reasoning in Anthropological Research ANG 6486 3
Social Networks and Big Data Analytics CAP 6315 3
Quantitative Communication Research COM 6316 3
Social Media and Web Analytics ISM 6555 3
Seminar in Political Behavior POS 6208 3
Research Design in Political Science POS 6736 3
Seminar in Advanced Research Methods SYA 6305 3
Take three elective courses from the Electives Table. Thesis option requires only one elective course and 6 thesis credits.

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Electives Table
Business Analytics
Data Mining and Predictive Analytics ISM 6136 3
Database Management Systems ISM 6217 3
Introduction to Business Analytics and Big Data ISM 6404 3
Advanced Business Analytics ISM 6405 3
Social Media and Web Analytics ISM 6555 3
Data Management and Analysis with Excel QMB 6303 3
Data Analysis for Managers QMB 6603 3
Database and Cloud Computing
Multiprocessor Architecture CDA 6132 3
Cloud Computing CEN 5086 3
New Directions in Database Systems COP 6726 3
Theory and Implementation of Database Systems COP 6731 3
Database Management Systems ISM 6217 3
Data Mining and Machine Learning
Introduction to Neural Networks CAP 5615 3
Social Networks and Big Data Analytics CAP 6315 3
Data Mining for Bioinformatics CAP 6546 3
Machine Learning for Computer Vision CAP 6618 3
Deep Learning CAP 6619 3
Data Mining and Machine Learning CAP 6673 3
Information Retrieval CAP 6776 3
Web Mining CAP 6777 3
Advanced Data Mining and Machine Learning CAP 6778 3
Big Data Analytics with Hadoop CAP 6780 3
Computational Advertising and Real-Time Analytics CAP 6807 3
Computer Performance Modeling CEN 6405 3
Data Mining and Predictive Analytics ISM 6136 3
Data Security and Privacy
Computer Data Security CIS 6370 3
Cyber Security: Measurement and Data Analysis CTS 6319 3
Management of Information Assurance and Security ISM 6328 3
Introduction to Cryptology and Information Security MAD 5474 3
Cryptanalysis MAD 6478 3
Quantum Mechanics 2 PHY 6646 3
Scientific Applications and Modeling
Photogrammetry and Aerial Photography Interpretation GIS 6028C 3
LiDAR Remote Sensing and Applications GIS 6032C 3
Web GIS GIS 6061C 3
Geospatial Databases GIS 6112C 3
Hyperspectral Remote Sensing GIS 6127 3
Spatial Data Analysis GIS 6306 3
Special Topics (Quantum Information Processing) PHY 6938 3
Computational Physics PHZ 5156 3
Numerical Relativity PHZ 7609 3
Social Data Science
Advanced Anthropological Research 1 ANG 6090 3
Advanced Anthropological Research 2 ANG 6092 3
Quantitative Reasoning in Anthropological Research ANG 6486 3
Social Networks and Big Data Analytics CAP 6315 3
Quantitative Communication Research COM 6316 3
Special Topics (Quantitative Methods) POS 6934 3
Research Design in Political Science POS 6736 3
Seminar in Advanced Research Methods SYA 6305 3
Statistics and Data Applications
Biomedical Data and Informatics BSC 6459 3
Biostatistics STA 5195 3
Statistical Computing STA 6106 3
Survival Analysis STA 6177 3
Biostatistics - Longitudinal Data Analysis STA 6197 3
Applied Statistical Methods STA 6207 3
Regression Analysis STA 6236 3
Mathematical Statistics STA 6326 3
Applied Time Series Analysis STA 6857 3
Applied Computational Topology MTG 6329 3

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Master of Science with Major in Information Technology and Management

The Master of Science with Major in Information Technology and Management (MSITM) is jointly offered by the Department of Computer & Electrical Engineering and Computer Science (CEECS) in the College of Engineering and Computer Science and the Department of Information Technology and Operations Management (ITOM) in the College of Business. Designed for highly motivated individuals with computing and/or managerial backgrounds, the program aims to prepare students for a management career in the area of information technology in organizations. To allow for maximum flexibility in career aspirations, students can select from four concentrations:  Advanced Information Technology, emphasizing the technical aspect of organizational IT systems; Information Technology Management, focusing on the management issues of IT in organizations; Computer Science Data Analytics; and Business Analytics.

Admission Requirements
To be admitted to the MSITM program applicants must have:

1. An undergraduate degree in Computer Science, Information Engineering Technology or an IT-related field of study. Applicants with another undergraduate degree and documented work experience of two or more years in an IT function will be evaluated as well;

2. An undergraduate GPA of 3.0 or higher;

3. GRE or GMAT scores more than five years old are normally not acceptable. The GRE and the GMAT requirement is waived for any student who has a baccalaureate degree from either FAU's Department of Computer & Electrical Engineering and Computer Science (CEECS) or FAU's Department of Information Technology and Operations Management (ITOM) with a GPA of at least 3.25 (out of a possible 4.0) in the last 60 credits attempted prior to graduation;

4. International students from non-English-speaking countries must be proficient in written and spoken English as evidenced by a score of at least 500 (paper-based test) or 213 (computer-based test) or 79 (Internet-based test) on the Test of English as a Foreign Language (TOEFL) or a score of at least 6.0 on the International English Language Testing System (IELTS); and

5. Meet other requirements of the FAU Graduate College.

Degree Requirements
Students in the Advanced Information Technology and Computer Science Data Analytics concentrations are required to complete 30 graduate-level credits, or 10, 3-credit courses (5000 level or higher), with a 3.0 GPA or better to graduate. Students in the Information Technology Management and Business Analytics concentrations are required to complete 33 graduate-level credits, or 11, 3-credit courses (5000 level or higher), with a 3.0 GPA or better to graduate.

Students in the Advanced Information Technology and Computer Science Data Analytics concentrations will be awarded the degree by the College of Engineering and Computer Science, while those in the Information Technology Management and Business Analytics concentrations will have their degrees awarded by the College of Business. For more information about the Master of Science in Information Technology and Management degree program, call the Department of Computer & Electrical Engineering and Computer Science at 561-297-3482, or email ceecs@fau.edu.

Advanced Information Technology Concentration (Changes effective fall 2020.)
Students are required to take the following three four courses. from the following list:

Software Engineering CEN 5035
Object-Oriented Software Design COP 5339
Data Mining and Machine Learning CAP 6673 or
Theory and Implementation of the Database Systems COP 6731
Management of Information Systems and Technology ISM 6026

In addition, students need to take five four electives from the following CEECS courses. Additional CEECS courses may be used as electives with prior approval of the CEECS advisor:

Data Mining and Machine Learning (if not counted in the required courses group) CAP 6673
Advanced Data Mining and Machine Learning CAP 6778
Software Maintenance and Evolution CEN 6027
Software Testing CEN 6076
Computer Data Security CIS 6370
Mobile Computing CNT 6517
Topics in Computer Science COT 5930
Topics in Computer Science COT 6930
Computer Performance Modeling CEN 6405
Video Communication CNT 6885
Software Architecture and Patterns CEN 6085
Information Retrieval CAP 6776
Natural Language Processing CAP 6640
Introduction to Data Science CAP 5768
Cloud Computing CEN 5086
Theory and Implementation of Database Systems (if not counted in the required courses group) COP 6731
Cyber Security: Measurement and Data Analysis CTS 6319
Computational Advertising and Real-Time Data Analytics CAP 6807
Social Network and Big Data Analytics CAP 6315
Foundations of Vision CAP 6411
Sensor Networks and Smart Systems CNT 5109
Mobile Application Development CAP 5675
Advanced Internet Systems CAP 6819

The last two electives must be chosen from the following ITOM courses:

Information Technology Project and Change
Management
ISM 6316
Management of Information Assurance and Security ISM 6328
Enterprise Information Technology Service
Management
ISM 6368
Web-Based Business Development ISM 6508
Information Technology Sourcing Management ISM 6509
Advanced Business Analytics ISM 6405
Data Mining and Predictive Analytics ISM 6136
Social Media and Web Analytics ISM 6555
Mobile Apps for Business ISM 6058
Data Management and Analysis with Excel QMB 6303

Information Technology Management Concentration (Changes effective spring 2020.)
Students are required to take the following seven courses offered by the College of Business:

Management of Information Systems and
Technology
ISM 6026
Information Technology Project and Change
Management
ISM 6316
Management of Information Assurance and Security ISM 6328
Enterprise Information Technology Service
Management
ISM 6368
Web-Based Business Development
ISM 6508
Information Technology Sourcing Management ISM 6509
Graduate Business Communication Applications GEB 6215

Students must take one elective from the following ITOM courses:

Advanced Business Analytics ISM 6405
Data Mining and Predictive Analytics ISM 6136
Social Media and Web Analytics ISM 6555
Mobile Apps for Business ISM 6058
Data Management and Analysis with Excel QMB 6303

In addition, students must take three electives from the following courses offered by the College of Engineering and Computer Science. Additional CEECS courses may be used as electives with prior approval of the CEECS advisor:

Data Mining and Machine Learning CAP 6673
Software Maintenance and Evolution CEN 6027
Software Testing CEN 6076
Computer Data Security CIS 6370
Mobile Computing CNT 6517
Object-Oriented Software Design COP 5339
Theory and Implementation of Database Systems COP 6731
Topics in Computer Science COT 5930
Information Retrieval CAP 6776
Natural Language Processing CAP 6640
Introduction to Data Science CAP 5768
Cloud Computing CEN 5086
Cyber Security: Measurement and Data Analysis CTS 6319
Software Engineering CEN 5035
Computational Advertising and Real-Time Data Analytics CAP 6807
Social Network and Big Data Analytics CAP 6315
Introduction to Neural Networks CAP 5615
Foundations of Vision CAP 6411
Software Architecture and Patterns CEN 6085
Sensor Networks and Smart Systems CNT 5109

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Computer Science Data Analytics Concentration (Changes effective fall 2020.)
Students are required to take the following three courses offered by from the Computer and Electrical Engineering & Computer Science (CEECS) department. following list:

Introduction to Data Science CAP 5768
Software Engineering CEN 5035
Theory and Implementation of the Database Systems COP 6731
Object-Oriented Software Design COP 5339
Data Mining and Machine Learning CAP 6673 or
Introduction to Neural Networks CAP 5615

In addition, students must take four CEECS electives, at least two of which are from the CEECS Data Analytics group: from the following CEECS courses:

CEECS Data Analytics electives are listed below. Additional CEECS courses may be used with prior approval of the CEECS advisor.
Data Mining and Machine Learning (if not counted in the required courses group) CAP 6673
Introduction to Neural Networks (if not counted in the required courses group) CAP 5615
Social Network and Big Data Analytics CAP 6315
Deep Learning CAP 6619
Natural Language Processing CAP 6640
Data Mining for Bioinformatics CAP 6546
Information Retrieval CAP 6776
Web Mining CAP 6777
Advanced Data Mining and Machine Learning CAP 6778
Big Data Analytics with Hadoop CAP 6780
Computer Performance Modeling CEN 6405
Computational Advertising and Real-Time Data Analytics CAP 6807
Introduction to Data Science CAP 5768
Other CEECS electives are listed below. Additional CEECS courses may be used with prior approval of the CEECS advisor.
Cloud Computing CEN 5086
Computer Data Security CIS 6370
Sensor Networks and Smart Systems CNT 5109
Mobile Application Development COP 5675
Advanced Internet Systems COP 6819

The last three electives must be chosen from the following ITOM courses:

Data Mining and Predictive Analytics ISM 6136
Database Management Systems ISM 6217
Introduction to Business Analytics and Big Data ISM 6404
Advanced Business Analytics ISM 6405
Social Media and Web Analytics ISM 6555
Data Management and Analysis with Excel QMB 6303
Data Analysis for Managers QMB 6603

Note: Students in this concentration meet the requirements for the Big Data Analytics certificate. Follow up with the CEECS advisor to apply for the certificate.

Business Analytics Concentration (Changes effective spring 2020.)
Students are required to take the following seven courses offered by the College of Business:

Management of Information Systems and Technology ISM 6026
Information Technology Project and Change Management ISM 6316
Introduction to Business Analytics and Big Data ISM 6404
Data Mining and Predictive Analytics ISM 6136
Advanced Business Analytics ISM 6405
Social Media and Web Analytics ISM 6555
Graduate Business Communication Applications GEB 6215

Students must take one elective from the following ITOM courses:

Data Management and Analysis with Excel QMB 6303
Information Technology Sourcing Management ISM 6509
Web-Based Business Development ISM 6508
Mobile Apps for Business ISM 6058
Management of Information Assurance and Security ISM 6328
Enterprise Information Technology Service Management ISM 6368

In addition, students must take three electives from the following courses offered by the College of Engineering and Computer Science:

Data Mining and Machine Learning CAP 6673
Information Retrieval CAP 6776
Natural Language Processing CAP 6640
Computational Advertising and Real-Time Data Analytics CAP 6807
Social Network and Big Data Analytics CAP 6315
Introduction to Neural Networks CAP 5615
Deep Learning CAP 6619
Data Mining for Bioinformatics CAP 6546
Web Mining CAP 6777
Advanced Data Mining and Machine Learning CAP 6778
Big Data Analytics with Hadoop CAP 6780
Computer Performance Modeling CEN 6405
Introduction to Data Science CAP 5768

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Graduate Certificates

Big Data Analytics Certificate
The digital age is here to stay. Organizations now own and have access to unfathomable amounts of data. New technologies and efforts are needed to move on to the next phase of the digital revolution - the data revolution. To provide students with the knowledge necessary in this age of Big Data, the Department of Computer and Electrical Engineering and Computer Science (CEECS) and the Department of Information Technology and Operations Management (ITOM) have jointly designed the Big Data Analytics graduate certificate. This 12-credit certificate allows graduate students to expand their knowledge and skills in the concepts, technologies, and tools of business intelligence, data analytics and business analytics and be recognized for their achievement. The certificate program has two tracks: Computer Science (CS) and Business (BU).

Tracks
CS Track: The Big Data Analytics certificate with a track in Computer Science will be granted to a student who completes three 3-credit courses from the CS Data Analytics course list and one 3-credit course from the ITOM Business Analytics course list.

BU Track: The Big Data Analytics certificate with a track in Business will be granted to a student who completes three 3-credit courses from the ITOM Business Analytics course list and one 3-credit course from the CS Data Analytics course list.

Admission
CS Track: Open to students who have a B.S. degree in Computer Science or in a related field of Science or Engineering 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.

BU Track: Open to students who have a bachelor's degree in Business 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.

Big Data Analytics Courses by Track (Change effective fall 2020.)

CS Data Analytic Courses
(Select three from this list and one from the list of ITOM courses.)
Introduction to Neural Networks CAP 5615 3
Introduction to Data Science CAP 5768 3
Social Networks and Big Data Analytics CAP 6315 3
Data Mining for Bioinformatics CAP 6546 3
Deep Learning CAP 6619 3
Data Mining and Machine Learning
CAP 6673 3
Information Retrieval CAP 6776 3
Web Mining CAP 6777 3
Advanced Data Mining and Machine Learning CAP 6778 3
Big Data Analytics with Hadoop CAP 6780 3
Computer Performance Modeling CEN 6405 3
Deep Learning CAP 6619 3
Computational Advertising and Real-Time Data Analytics CAP 6807 3
ITOM Business Analytics Courses
(Select three from this list and one from the list of CS courses.)
Data Mining and Predictive Analytics ISM 6136 3
Database Management Systems ISM 6217 3
Introduction to Business Analytics
and Big Data
ISM 6404 3
Advanced Business Analytics ISM 6405 3
Social Media and Web Analytics ISM 6555 3
Data Management and Analysis with Excel QMB 6303 3
Data Analysis for Managers QMB 6603 3

Cyber Security Certificate

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 cyber security 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 cyber security 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).

Tracks
CS Track: The Cyber Security certificate with a track in Computer Science will be granted to a student who completes four 3-credit courses as follows: three 3-credit courses from the CS Cyber Security course list and one 3-credit course from either the CS or the Math Cyber Security course list.

Math 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 and one 3-credit course from either the Math or the CS Cyber Security course list.

Admission
CS Track: Open to students who have a B.S. degree in Computer Science or in a related field of Science or Engineering 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.

Math Track: 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

CS Cyber Security Courses (Select three from this list and one more from this list or the list of Math courses)
Practical Aspects of Modern Cryptography CIS 5371 3
Computer Data Security CIS 6370  
Distributed Systems Security CIS 6375 3
Secret Sharing Protocols COT 6427 3
Cyber Security: Measurement and Data
Analysis
CTS 6319 3
Math Cyber Security Courses (Select three from this list and one more from this list or the list of CS courses.)
Introduction to Cryptology and Information
Security
MAD 5474 3
Cryptanalysis MAD 6478 3
Coding Theory MAD 6607 3
Number Theory and Cryptography MAS 6217 3

Geographic Information Systems Certificate
The Geographic Information Systems (GIS) certificate for graduate students is offered jointly by the Department of Geosciences and the School of Urban and Regional Planning in the College for Design and Social Inquiry. Graduate students who complete the program below with a grade of "B" or better in each course are entitled to receive the certificate. Students should consult with the director of the GIS Center or their graduate advisor about registration for this program. Students shall use the courses below to complete the certificate.
Required Courses (9 credits)
Principles of Geographic Information Systems* GIS 5051C 3
OR    
Introduction to GIS in Planning URP 6270 3
AND    
Applications in Geographic Information Systems GIS 5100C 3
Spatial Data Analysis GIS 6306 3
Choose two of the following courses (6 credits)
Programming in Geographic Information Systems GIS 5103C 3
Web GIS GIS 6061C 3
Geospatial Databases GIS 6112C 3
Environmental Analysis in Planning URP 6425 3
Managing GIS Projects URP 6272 3

* If the undergraduate version of this course was already counted for the undergraduate GIS certificate, this graduate version cannot be counted toward the graduate GIS certificate.

Transportation, Logistics and Supply Chain Management Certificate

To provide students with the knowledge necessary in this age of connected supply chains, the Department of Information Technology and Operations Management (ITOM) in the College of Business and the Department of Civil, Environmental and Geomatics Engineering (CEGE) in the College of Engineering and Computer Science offer a jointly designed certificate in Transportation, Logistics and Supply Chain Management. This 12-credit certificate permits graduate students to expand their knowledge on the technical skills of transportation engineering and the analytical business decision-making skills of supply chain management.

Admission
This certificate program is open to students who have a bachelor's degree in business or engineering or in a related field and a GPA of at least 3.0. Students must satisfy the prerequisites for each course in the program.

Curriculum (Change effective spring 2020.)
All four required 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.

Required Courses by Department
ITOM Department (select two from the list, one of which must be MAN 6596)
Operations Management MAN 6501 3
Project Management MAN 6581 3
Supply Chain Management MAN 6596 3
CEGE Department (select two from the list)
Transportation System Analysis TTE 6501
5501
3
Transportation and Supply Chain Systems TTE 6507 3
Maritime Freight Operations TTE 6508 3

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