Data Science, MS
Dr. Shakil Akhtar, Graduate Program Coordinator
Dr. Shuju Bai, Chair
Mission
The Master of Science in Data Science (MSDS) enables students to pursue advanced careers in Data Science addressing state and national workforce shortages and supporting the growth of the local knowledge-based economy.
Goals
The Master of Science in Data Science (MSDS) program equips students with the knowledge and skills to advance their current careers in or perform a mid-career transition into data science fields, to work independently and collaboratively, and to pursue academic or professional careers in education and research, industry, business, or government.
The program offers two curricular tracks: a Research (Thesis) Track and an Applied (Project) Track. The research track facilitates students to advance careers in data science research, possibly continuing toward a doctoral degree. The applied project track maximizes the acquisition of advanced practical skills for professional placements in industry, business, or government.
Within each track, there are three concentrations from which students may choose: Data Management and Intelligence, Knowledge and Information Systems, and Health Informatics. To earn the Master of Science in Data Science degree, a student must complete 30 credit hours including Data Science core courses, courses within the chosen concentration, and a project or a thesis plus a research techniques course.
Program Outcomes
Graduates of this program will be able to:
- Demonstrate a comprehensive understanding of data science.
- Utilize advanced data science knowledge and skills to solve complex computing problems related to data science specialization.
- Identify and analyze user needs, and integrate data science-based solutions into user environment.
- Possess skills in data science leadership and information management.
General Requirements for Program Admission
To be considered for MSDS admission, applicants must submit an application for admission to Graduate Studies at Clayton State University. Admission to the MSDS program requires an earned baccalaureate degree in computer science, information technology or closely related field from an accredited college or university.
In addition to the general requirements as outlined in this catalog, applicants must have:
- Completed online application to the School of Graduate Studies
- Bachelor’s degree with a minimum 2.5 GPA of undergraduate study from an accredited college or university
- Essay of Purpose and graduate school interest
- Three (3) letters of recommendation
- For applicants with an undergraduate GPA between 2.50 and 2.69, a GRE score of 296 or higher is required.
- International students whose native language is not English are required to submit English Language Proficiency through one of the following options: TOEFL (minimum score of 78 total on the internet-based TOEFL), IELTS (minimum score of 6 total), Duolingo English Test (Minimum score of 100), or successful completion of an approved University System of Georgia (USG) intensive ESL program.
- If an applicant has completed any coursework, degree, or degrees from institutions outside of the United States, he or she must utilize a credential evaluation service. The School of Graduate Studies accepts an official course-by-course evaluation with a GPA that is prepared by either Josef Silney and Associates or World Education Services.
Deadlines
| Admit Term | Final Recommended Deadline |
|---|---|
| Fall | July 15 |
| Spring | November 15 |
| Summer | April 25 |
The final deadlines for Applications and supporting documents for international graduate students are:
| Admit Term | Final Recommended Deadline |
|---|---|
| Fall | May 1 |
| Spring | September 1 |
| Summer | February 1 |
Interested Applicants
Visit the Department of Computer Science and Information Technology webpage. You may also contact the department by email or by phone at: (678) 466-4401.
To apply, visit the Clayton State University School of Graduate Studies webpage.
*This program will be effective Fall 2023.
The program offers two (2) program tracks, Applied (Project) Track and Research (Thesis) Track. For each track, there are three concentrations for students to choose. Students must complete 30 credit hours of MSDS courses with a minimum grade point average of 3.0 to earn the degree. All students must take the four (4) MSDS core courses with a minimum of grade of B. Student may not earn more than two grades of “C” in the program. Students can transfer at most 6 credit hours of non-core courses from other colleges or universities.
Applied (Project) Track
- Four (4) data science core courses: 12 credit hours
- Five (5) concentration courses: 15 credit hours
- CSCI 6599 Special Project: 3 credit hours
Research (Thesis) Track
- Four (4) data science core courses: 12 credit hours
- Four (4) concentration courses: 12 credit hours
- CSCI 6574 Research Techniques: 3 credit hours
- CSCI 6600 Thesis: 3 credit hours
English Proficiency Requirement
Those applicants whose native language is not English or whose language of college instruction was not English are required to submit English Language proficiency scores.
- Test of English as a Foreign Language (TOEFL): 78 total on the internet-based TOEFL (ibT) or 550+ on the paper-based TOEFL
- International English Language Testing System (IELTS): Minimum scores of 6 total
- Duolingo English Test: Minimum score of 100
Applicants whose language of instruction was English should have the Registrar of their home institution submit a letter to Graduate Admissions attesting/certifying that the language of instruction for the baccalaureate degree was English. If this documentation is unavailable, applicants must submit the official acceptable scores on the TOEFL, IELTS or Duolingo.
Program Requirements
*This program will be effective Fall 2023.
The curriculum is generally delivered over three semesters when students follow the planned sequence. All students must take the four MSDS core courses with a minimum grade of B.
| Code | Title | Credit Hours |
|---|---|---|
| Required MSDS Core Courses 1 | 12 | |
| CSCI 5101 | Foundations of Information Systems Security and Ethics | 3 |
| CSCI 5112 | System Analysis & Design | 3 |
| CSCI 5201 | Database Theory and Design | 3 |
| CSCI 5317 | Operating Systems Administration and Security | 3 |
| Concentration Requirements 3 | 12-15 | |
| Choose one concentration from the following: | ||
Data Management and Intelligence | ||
Knowledge and Information Systems | ||
Health Informatics | ||
| Total Credit Hours | 30 | |
DATA MANAGEMENT AND INTELLIGENCE CONCENTRATION (Applied)
| Code | Title | Credit Hours |
|---|---|---|
| MSDS Required Core Courses 1 | 12 | |
| Data Management and Intelligence Concentration (Applied) Requirements | 18 | |
| Special Project | ||
| Choose 5 courses (15 credit hours) from the following: | ||
| Data Management for Analytics | ||
| Data Mining and Data Warehousing | ||
| Foundation of Artificial Intelligence and Deep Learning | ||
| Cloud Computing | ||
| Web Application Development | ||
| Advanced Topics in Information Systems | ||
| Total Credit Hours | 30 | |
DATA MANAGEMENT AND INTELLIGENCE CONCENTRATION (Research)
| Code | Title | Credit Hours |
|---|---|---|
| MSDS Required Core Courses 1 | 12 | |
| Data Management and Intelligence Concentration Requirements 3 | 18 | |
| Research Techniques | ||
| Thesis | ||
| Choose 4 courses (12 credit hours) from the following: | ||
| Data Management for Analytics | ||
| Data Mining and Data Warehousing | ||
| Foundation of Artificial Intelligence and Deep Learning | ||
| Cloud Computing | ||
| Web Application Development | ||
| Advanced Topics in Information Systems | ||
| Total Credit Hours | 30 | |
KNOWLEDGE AND INFORMATION SYSTEMS CONCENTRATION (Applied)
| Code | Title | Credit Hours |
|---|---|---|
| MSDS Required Core Courses 1 | 12 | |
| Knowledge and Information Systems Concentration (Applied) Requirements 3 | 18 | |
| Special Project | ||
| Choose 5 courses (15 credit hours) from the following: | ||
| Information Risk Management | ||
| Foundation of Artificial Intelligence and Deep Learning | ||
| Web Application Development | ||
| Data Science | ||
| Knowledge Engineering | ||
| Advanced Topics in Information Systems | ||
| Total Credit Hours | 30 | |
KNOWLEDGE AND INFORMATION SYSTEMS CONCENTRATION (Research)
| Code | Title | Credit Hours |
|---|---|---|
| MSDS Required Core Courses 1 | 12 | |
| Knowledge and Information Systems Concentration (Research) Requirements 3 | 18 | |
| Research Techniques | ||
| Thesis | ||
| Choose 4 courses (12 credit hours) from the following: | ||
| Information Risk Management | ||
| Foundation of Artificial Intelligence and Deep Learning | ||
| Web Application Development | ||
| Data Science | ||
| Knowledge Engineering | ||
| Advanced Topics in Information Systems | ||
| Total Credit Hours | 30 | |
HEALTH INFORMATICS CONCENTRATION (Applied)
| Code | Title | Credit Hours |
|---|---|---|
| MSDS Required Core Courses 1 | 12 | |
| Health Informatics Concentration (Applied) Requirements 3 | 18 | |
| Special Project | ||
| Choose 5 courses (15 credit hours) from the following: | ||
| Digital Transformation | ||
| Health Care Analytics and Applications | ||
| Introduction to Health Informatics | ||
| Foundations of Clinical Processes and Workflows | ||
| Health Systems Administration | ||
| Information Mgmt.-Health Care | ||
| Advanced Topics in Information Systems | ||
| Total Credit Hours | 30 | |
HEALTH INFORMATICS CONCENTRATION (Research)
| Code | Title | Credit Hours |
|---|---|---|
| MSDS Required Core Courses 1 | 12 | |
| Health Informatics Concentration Requirements 3 | 18 | |
| Research Techniques | ||
| Thesis | ||
| Choose 4 courses (12 credit hours) from the following: | ||
| Digital Transformation | ||
| Health Care Analytics and Applications | ||
| Introduction to Health Informatics | ||
| Foundations of Clinical Processes and Workflows | ||
| Health Systems Administration | ||
| Information Mgmt.-Health Care | ||
| Advanced Topics in Information Systems | ||
| Total Credit Hours | 30 | |
- 1
All students must take the four (4) MSDS core courses totaling 12 credit hours. A minimum grade of B is required for the four core courses.
- 2
Students must select one of two program tracks, either the Applied (Project) Track or the Research (Thesis) Track. Students selecting the Applied (Project) Track must complete CSCI 6599 Special Project (3 credit hours). Students selecting the Research (Thesis) Track must complete CSCI 6574 Research Techniques (3 credit hours) and CSCI 6600 Thesis (3 credit hours).
- 3
Concentration Requirements: There are three concentration options- The Data Management and Intelligence concentration, the Knowledge and Information Systems concentration, and the Health Informatics concentration. Applied (Project) Track students must take 5 courses (15 credit hours) within one of the concentration options.
Research (Thesis) Track students must take 4 courses (12 credit hours) within one of the concentration options.
Computer Science (CSCI) Courses
This course covers the fields of enterprise security and privacy. The course deals with the identification of threats to enterprise information technology (IT) systems, access control and open systems, and system and product evaluation criteria. In addition, it discusses the enterprise security requirements. Risk management and policy considerations are examined with respect to the technical nature of enterprise security as represented by government guidance and regulations to support information confidentiality, integrity and availability. The course also deals with the fundamental hacking approaches through practical exposure via hands-on assignments, discussions, and quizzes. For lab assignments, students are expected to use various tools to complete the deliverable(s).
This course will introduce the concepts and techniques for analyzing and designing business information systems. Topics include the system analysis, the systems development life cycle, system development methodologies, development technology, systems implementation, and systems support. Tools and techniques for systems analysis and systems design are also introduced.
This course presents terminology, basic concepts, and applications of database processing. The course emphasizes database design using various modeling techniques; database implementation using the relational model, normalization, and SQL.
This course covers computer operating systems, such as UNIX and Linux, systems programming,systems administration, and operating systems hardening.
This course will provide students with a good understanding of identifying, assessing, analyzing, measuring, and responding to information risk. Students will be able to make risk mitigation and acceptance decisions given its resource constraints. Students will be able to use risk management tools, regulations, and methodologies for metrics to monitor risk management activities.
Selected advanced topics of current interest in information systems will be presented in this course. Students will review the articles, journals, white papers, and use computerized databases and library resources. This course will be offered as fits the needs and interests of the student and faculty.
Covers the theory and applications of data management to support data analytics, including data models, security, examination, transformation, and exploration. Discusses the fundamental concepts and emerging technologies include relational databases, NoSQL databases, data integration, and data processing for analytics.
This course introduces students to algorithms and skills for data mining and overall architecture of data warehousing. Topics include data cube technology, pattern recognition, advanced classification and clustering analysis, outlier detection, data visualization, data integration, and data warehousing. Data mining and data warehousing applications will also be discussed.
Prerequisites: CSCI 6201
This course is an introduction to artificial intelligence and deep learning. Topics include (1) traditional intelligent system design methodologies, search and problem solving, supervised and reinforced learning, and (2) the technologies, methodologies, and tools for deep learning such as neural networks and optimization algorithms.
Prerequisites: CSCI 5201
This course introduces students to the Cloud concepts and capabilities across the various Cloud service models including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and Business Process as a Service (BPaaS). It covers a broad range of Cloud vendor platforms including AWS, Google App Engine, Microsoft Azure, Eucalyptus, OpenStack and others. The topics include both concepts on parallel and distributed computing platforms and programming skills required for harvesting computational powers.
Prerequisites: CSCI 5317
This course will introduce students to the concepts and fundamental practices necessary to create interactive web-based applications. Application design and development are covered including control mechanisms, models, and views design and development. Students will learn both server size and client site fundamental scripting will be introduced along with customized databases for team projects.
This course provides students with insights on key aspects of Digitalization and Business Transformation. Students will understand the disruptive nature of Digitalization and consequences for Business Strategies and Business Transformation across all industries. Students will receive a short introduction on the development of Information Systems over the last 25 years and today’s most important technologies and technology providers. Furthermore, students will gain an understanding of key technologies like Cloud Computing, Internet of Things, Big Data Analysis, and Artificial Intelligence.
Prerequisites: CSCI 5101
Students will learn how to conduct literature reviews of articles, journals, white papers using Internet, computerized databases and library resources. Students will learn to develop research questions, hypotheses, research topics, research designs and write research papers in standard format.
Continuation of research on Special Project. Satisfactory oral defense of topic is required for graduation.
Continuation of research on thesis. Satisfactory oral defense of topic is required for graduation. Prerequisite(s): CSCI 6574 and Core courses and two courses in the area of emphasis.
Prerequisites: CSCI 6574
This course will present the knowledge, infrastructure, functions, and tools of health informatics. The course provides an overview of the theory, processes, and applications of information systems to healthcare, policy, and management. It also provides a basic understanding of data standards and requirements, critical concepts and practices in mapping and interpreting health information. It explores technology in planning, management, and applications in healthcare. Topics also include core concepts and issues in planning, implementing, and evaluating health information systems.
This course provides an understanding of applications of information systems in healthcare processes and workflows. Students will become familiar with fundamentals of medical terms, coding systems, electronic health records, processes, process analysis and redesign in the healthcare settings. The course also introduces clinical workflows and process evaluation, re-engineering with advanced information management tools and techniques, and case studies.
This course is designed to provide students with an understanding of healthcare data models that could help improve administrative costs, decision making, patient care and patient wellness. Fundamentals of data sciences based upon statistical and biological models will be discussed. Applications to environmental health and other relevant healthcare fields will be considered.
Prerequisites: CSCI 5201
This course will introduce students to data science and skills used in data science. It includes concepts from Statistics, Computer Science and Software Engineering. Students will learn theory and skills of data management, data storage, data processing and analysis, data visualization, and data application. Data science programming languages such as Python and their associated data analysis libraries will be learned through hands-on practices. In addition, students will learn skills of developing data products via programming, research, and communicating results.
Prerequisites: CSCI 5201
This course covers knowledge engineering and its applications. The topics cover designs, develops and integrations of information systems and technologies to construct knowledge. Students will learn about fundamental knowledge representation and reasoning, knowledge modeling, knowledge acquisition, and evolution.
Prerequisites: CSCI 6812
Health Care Management (HCMG)
This course will provide administrative concepts and theories within United States health care systems. The history and evolution of the systems will be discussed and the current state of health care delivery will be analyzed. The political, legal, and financial issues that impact health care will be considered. The course will discuss and assess the different types of health care providers and their roles in the systems.
This course will provide an understanding of the different information systems designed to improve health care delivery and their use in the management of health care organizations. Students will examine the current status of information systems within health care and also explore possible advanced uses of informational systems to monitor patient outcomes, financial stability and marketing information.
Prerequisites: HCMG 5100 (may be taken concurrently)