Industry Aligned Concentrations

Our concentration in Applied Statistics is a four-course option that acknowledges both a deeper level of statistical training and understanding of statistics and differentiates the participant from his or her peers. As a student in this concentration, you will focus heavily on probability, time series, and statistical methodology. This curriculum supports the demand positions in statistics and data analysis.    

Required 6 credit hours among the below courses: 

• STAT 51600 – Basic Probability and Applications (3 Credit Hours) 
• STAT 51700 – Statistical Inference (3 Credit Hours) 

Choose 6 credit hours among the below courses: 

• STAT 51400 – Design of Experiments (3 Credit Hours) 
• STAT 52000 – Time Series and Applications (3 Credit Hours) 
• STAT 52500 – Intermediate Statistical Methodology (3 Credit Hours) 
• STAT 52600 – Advanced Statistical Methodology (3 Credit Hours) 

This concentration will provide professionals the knowledge of important aspects of leading, planning, executing, and controlling IT projects. Gain the preparatory level skills to support the success of complex, cross-functional IT related business initiatives. 

The curriculum enhances your knowledge in the areas of economics & procurement management and organizational & change management. Improve your project management and business strategy skills needed to help your organization achieve its strategic goals. Concepts gained with this degree include quality, communication, critical thinking, and implementing, monitoring, & controlling project plans. 

Required 3 credit hours: 

• CNIT 55200 – IT Project Management   

Remaining 9 credit hours can be taken from the below course list: 

• CNIT 55100 – IT Economics  
• CNIT 58000 – Advanced Topics in IT Project Management   
• CNIT 58200 – IT Estimating-Scheduling-Control   
• CNIT 58300 – IT Program and Portfolio Management   
• CNIT 58500 – Organizational and Change Management for IT Projects  
• CNIT 58600 – IT Requirements Management    

This concentration will provide professionals the knowledge of how to determine business requirements that drive IT-related projects. In addition, it will help them identify, analyze and design processes to improve the overall flow of information within their organization.  

The curriculum enhances your knowledge in the areas of structure, policies, and operations of an organization. Learn to work as a liaison between the organization leadership and IT department to help them understand the needs of the organization and how IT can help achieve its goals. Develop skills to identify, document, and implement IT requirements for any business obstacles. 

Required 3 credit hour course: 

• CNIT 53000 – IT Business Analysis (3 Credit Hours) 

Choose 9 credit hours among the below courses:  

• CNIT 53100 – IT Requirements Analysis & Modeling (3 Credit Hours) 
• CNIT 53200 – IT Enterprise Analysis (3 Credit Hours) 
• CNIT 53500 – Advanced Topics in IT Business Analysis (3 credits) 
• CNIT 57000 – IT Data Analytics (3 credits) 
• CNIT 58500 – Organizational and Change Management for IT Projects (3 credits) 

This concentration provides students with vital skills in data science and helps them master the technologies they’ll use in the field. Learn core concepts of geographic information systems (GIS) and spatial data science, including data sources, projections, spatial data processing and analysis methods, data and metadata creation and a conceptual framework for solving spatial problems. 

Students will also learn foundational skills in scripting languages, data types, databases, and data visualization and analysis. By the end of the program, students will be able to collect, analyze, interpret, and combine geospatial data to make informed decisions regarding natural resource management and solve problems in the environmental, agricultural and engineering sectors. 

• ABE 65100 – Environmental Informatics  
• AGRY 54500 – Remote Sensing of Land Resources  
• ASM 54000 – Geographic Information System Application  
• FNR 58700 – Advanced Spatial Ecology and GIS    

This concentration offers professional training for students pursuing careers in the ethics and compliance of emerging technologies, such as AI and big data applications. Students will gain the skills to understand policies and processes in both U.S. and international contexts, comprehend the rationale behind policy proposals and enactments, and learn how these policies are implemented and evaluated for compliance.

Choose three courses among the below courses: 

• POL 50601 – Data, Machine Learning, And Artificial Intelligence For The Public Sector (3 Credit Hours) 
• POL52101 – Applied Public Policy: Institutions, Processes, and Practices (3 credit hours)
• POL52601 – Technology, AI, and Ethics in Public Policy and Public Administration (3 credit hours)
• POL52701 – Local to Global Governance of Data, AI, and Emerging Technology (3 credit hours)
• POL 52801 – Organizational Governance of Technology and AI (3 credit hours)

Choose 1 course among the below courses: 

• POL 50100 – Political Science: Methodology (3 Credit Hours) 
• POL 52101* – Applied Public Policy: Institutions, Processes, Practices
• POL 52901 – Applied Policy And Program Evaluation (3 Credit Hours) 
• POL 55101 – Governance Auditing for AI – Sociotechnical Frameworks and Practices
• SCLA 51000 – Data And Science Storytelling

* This course can only be used as a selective if it was not chosen as one of the initial 3 courses.

This concentration is for learners seeking specialization in smart manufacturing by providing a comprehensive understanding of sustainable design, advanced technology integration, and cybersecurity. The curriculum offers practical insights into Industry 4.0 concepts like IoT, big data, and global supply chains, positioning graduates as leaders in manufacturing innovation.

• ENGT 51000 – Internet of Things and Cybersecurity for Engineering and Technology
• ENGT 55000 – Manufacturing System Design for Sustainability
• MFET 55800 – Smart Manufacturing and Global Supply Chain Management 

This concentration provides professional engineers, technologists, and policymakers with critical data science skills applicable to transportation systems, focusing on smart mobility and urban data analytics. Designed for current professionals in related fields, the program enhances career prospects by imparting cutting-edge knowledge to address challenges like autonomous transport and supply chain resilience, emphasizing the application and communication of data science methods in transportation.

• CCE56401 – Data Science for Smart Cities
• CCE56901 – Smart Logistics
• CCE56601 – Network Models for Connected and Autonomous Vehicles

This concentration blends financial theory with advanced analytics to prepare you for high-impact roles in fintech, banking, and asset management. Gain a competitive edge in the financial sector by mastering data science tools to forecast market trends, assess risk, and optimize investment strategies.

• STAT 54000 – Computational Finance I
• STAT 57900 – Foundations of Statistical Machine Learning
• STAT 58700 – Machine Learning in Finance

This concentration provides students with advanced analytical and technical skills to address spatially informed problems across various disciplines.

The curriculum integrates spatial analytics, remote sensing, and geospatial modeling. This concentration prepares graduates for high-demand roles requiring both data science and geospatial information expertise.  Graduates will be equipped to analyze spatial patterns, inform policy, and drive innovation in fields such as sustainability, public health, transportation, and smart cities.

Choose one course in each area:

• Introduction Course
  • ASM 54000 Geographic Information System Application (3 credits)
  • CCE 50801 Geographic Information Systems (3 credits)
• Advanced Course
  • AAE 52300 Introduction to Remote Sensing (3 credits)
  • AGRY 54500 Remote Sensing of Land Resources (3 credits)
  • EAPS 50701 Geospatial Data Analytics (3 credits)
• Application Course
  • AAE 57500 Introduction to Satellite Navigation and Positioning (3 credits)
  • ABE 65100 Environmental Informatics (3 credits)
  • CCE 54900 Computational Watershed Hydrology (3 credits)
  • CCE 59700 Built Environmental Modeling (3 credits)
  • FNR 59800 Advanced Spatial Ecology and GIS (3 credits)
• Capstone Course
  • ILS 59500 GIS Capstone Experience (3 credits)
    *This course replaces GRAD 58800 Online Master of Science in Data Science Capstone

This concentration allows students to deepen their understanding of AI concepts and specialize in the growing field of Artificial Intelligence.  Designed for current professionals in technical fields, this concentration will explore data-driven AI/ML solutions – effectively formulating complex real-world problems into amenable tasks. Students will leverage their existing programming languages and frameworks and apply them to core machine learning and data mining algorithms.

Choose one course in each area:

• Required Courses
  • ECE 50024 Machine Learning (3 credits)
  • ECE 50836 Introduction to Data Mining (3 Credits)
  • GRAD 50200 Interdisciplinary AI Fundamentals: Bridging Knowledge (1 credit)
  • GRAD 50400 Advanced AI Fundamentals for Technical Professional (2 credits)

This concentration equips students with in-demand expertise at the intersection of AI and cybersecurity, preparing them to address emerging threats, build secure AI systems, and navigate privacy challenges in modern data environments. It provides a competitive edge for careers in data science, cybersecurity, and AI-driven industries by offering practical, advanced skills not covered in other concentrations.

Please note the additional 2-credit pre-requisite for CNIT 57200.

• CNIT 57200* AI Applications in Cybersecurity (3 credits)
  • Pre-requisite: GRAD 50400 Advanced AI Fundamentals for Technical Professional (2 credit hours)
• CNIT 57300 Adversarial Techniques in AI (3 credits)
• CNIT 57400 Information Security and Privacy in the Age of Large Language Models (3 credits)

The Business AI Applications concentration combines the Daniels School of Business’s pragmatic, problem-solving approach with the advanced technical foundation of the MS Data Science program. This concentration equips students to leverage artificial intelligence and data-driven technologies for strategic business impact.

Required Courses:

• MIS 58900 Computational Business Intelligence (2 credits)
• MIS 59000 Web Data & AI Analytics (2 credits)
• MIS 68200 Digital Product Design (2 credits)
• MIS 68300 Technology-Driven Business (2 credits)
• MIS 68700 AI For Business Decisions (2 credits)