Aug 18, 2022  
2021-2022 Undergraduate Academic Catalog and Student Handbook 
    
2021-2022 Undergraduate Academic Catalog and Student Handbook [ARCHIVED CATALOG]

Department of Data Science & Business Analytics


Data Science and Business Analytics

Department Chair: Dr. Shahram Taj, Professor

Assistant Chair: Dr. Rei Sanchez-Arias, Assistant Professor

 
Degree Programs Certificate Programs
Data Science
Business Analytics
 

 

 


Overview

Department of Data Science and Business Analytics offers two undergraduate degree programs, and three entry-level certificates. The undergraduate degree programs are Bachelor of Science in Data Science and Bachelor of Science in Business Analytics. The undergraduate entry-level certificate programs are Coding for Data Analytics, Entrepreneurship, and Health Systems.

There are five concentrations available to students in B.S. in Business Analytics and B.S. in Data Science:

  1. Logistics & Supply Chain Management

  2. Intelligent Mobility

  3. Quantitative Economics and Econometrics

  4. Big Data Analytics

  5. Health Systems Engineering

Bachelor of Science, Data Science

See Program Description  for full curriculum and details.

Florida Common Prerequisites: Data Science

Students who started as freshmen at Florida Poly (native students) must complete general education requirements and the following courses to enter the degree program as a junior:

  • COP 2271C - Introduction to Computation and Programming Credits: 3

  • COP 3530 - Data Structures & Algorithms Credits: 3

  • MAC 2311 - Analytic Geometry and Calculus 1 Credits: 4

  • MAC 2312 - Analytic Geometry and Calculus 2 Credits: 4

  • MAD 2104 - Discrete Mathematics Credits: 3

  • PHY 2048 - Physics 1 Credits: 3

  • PHY 2048L - Physics 1 Laboratory Credits: 1

  • QMB 3200 - Advanced Quantitative Methods Credits: 3

  • STA 2023 - Statistics 1 Credits: 3

 

Transfer students must meet general education requirements and satisfy the following Florida State Common Prerequisites to enter the degree program as a junior:

COP 2271C (1)

& COP 3530

& MAC 2311

  • or MAC X281

& MAC 2312

  • or MAC X282

& MAD 2107

& PHY 2048/2048L

  • or PHY X048C

  • or PHY X043/X048L

  • or PHY X053/X053

  • or PHY X049C

  • or PHY X044/X049L

  • or PHY X054/X054L

& QMB 3200

& STA 2023

  • or EGS X025

  • or QMB X100

  • or STA X024

  • or STA X032

  • or STA X037

  • or STA X122

(1)  Intro programming in C, C++, JAVA, or equivalent language.

Academic Learning Compact: Data Science

Florida Polytechnic University’s Academic Learning Compact describes what students, who follow the major’s study plan, will know and be able to do. These are listed as core student learning outcomes.

Student Learning Outcomes:

The Outcomes Involve These Skills:

Upon completion of the Data Science Degree, students will be able to:

Content

Critical Thinking

Communication

Apply current data science concepts, techniques, and practices to solve complex problems.

X

 

 

Analyze a given data science problem and formulate a solution in terms of the datasets needed, the techniques required or the technologies to be utilized.

 

X

 

Communicate effectively insights, analysis, conclusions, or solutions to a diverse audience.

 

 

X

Bachelor of Science, Business Analytics

See Program Description  for full curriculum and details.

Florida Common Prerequisites: Business Analytics

Transfer students must meet general education requirements and satisfy the following Florida State Common Prerequisites to enter the degree program as a junior:

ACG X021

  • or ACG X022

  • or ACG X001 & ACG X011

& ACG X071

  • or ACG X301

  • or ACG X072

& BUL 2241

  • or BUL X131

  • or CGS X092

  • or GEB X350

  • or MAN X440

  • or PHI X600

& CGS 1100

  • or CGS X100C

  • or CGS X000

  • or CGS X530   

  • or CGS X531

  • or CGS X570

  • or ISM X000

& ECO X013

& ECO X023

& EGS 3625

& MAC X311

  • or MAC X281

& STA X023

  • or EGS X025

  • or ESI X230

  • or QMB X100

  • or STA X024

  • or STA X032

  • or STA X037

  • or STA X122

Academic Learning Compact: Business Analytics

Florida Polytechnic University’s Academic Learning Compact describes what students, who follow the major’s study plan, will know and be able to do. These are listed as core student learning outcomes.

Student Learning Outcomes:

The Outcomes Involve These Skills:

Upon completion of the Business Analytics Degree, students will:

Content

Critical Thinking

Communication

Apply current business analytics concepts, techniques, and practices to solve business problems.

X

 

 

Analyze a given business problem using appropriate analytics techniques to generate insights and solutions.

 

X

 

Communicate effectively insights, analysis, conclusions, and solutions to a diverse audience.

 

 

X

Certificate, Coding for Data Analytics

See Program Description  for details.

Certificate, Entrepreneurship

See Program Description  for details.

Certificate, Health Systems Engineering

See Program Description  for details.

Course Offerings

Courses

  •  

    ACG 2020 - Accounting for Managers


    Credits: 3

    Prerequisites: None
    Course Description: This course is designed to enable the student to understand and apply the fundamental concepts and procedures of both financial and managerial accounting. Topics include basic accounting terminology, financial statement analysis and interpretation, internal control, cost behavior, cost-volume-profit analysis, budgeting, and the use of accounting data in making informed, ethical decisions..
  •  

    ACG 2021 - Principles of Financial Accounting


    Credits: 3

    Prerequisites: None
    Course Description: This course is an introduction to the principles of financial accounting and focuses on wealth and income measurement and the preparation and interpretation of financial statements. The recording and reporting of financial activity, balance sheets, income statements, changes in equity, cash flows, underlying assets, liabilities, and equities are also presented.
  •  

    ACG 2071 - Principles of Managerial Accounting


    Credits: 3

    Prerequisites: ACG 2021 - Principles of Financial Accounting  
    Course Description: This course covers tools used by management for cost reporting and control including statements, analytical tools, and reports.
  •  

    AVM 3012 - Air Transportation and Operations


    Credits: 3

    Prerequisites: MAN 2591 Introduction to Operations and Supply Chain Management   OR MAN 3504 - Introduction to Operations and Supply Chain Management  
    Course Description: This course covers air transportation including major, regional, cargo, and general carriers.
  •  

    BUL 2241 - Law, Public Policy, Negotiation and Business


    Credits: 3

    Prerequisites:  ENC 1101 - English Composition 1: Expository and Argumentative Writing  
    Course Description: The Structure of the legal system; administrative law and government regulation of business; and the legal environment of business including constitutional law, torts, contracts, and product liability are presented.
  •  

    CAP 3774 - Data Warehousing


    Credits: 3

    Prerequisites: COP 3710 - Database 1  
    Course Description: Fundamentals of building and populating a data mart to support the planning, designing and building of business intelligence applications and data analytics are covered in this course.
  •  

    CAP 4786 - Topics in Big Data Analytics


    Credits: 3

    Prerequisites: COP 3710 - Database 1  and MAS 3114 - Computational Linear Algebra  
    Course Description: This course provides the fundamental knowledge to capture and analyze all sorts of large-scale data from a variety of fields, such as people behavior, sensors, biological signals, finance, and more. Platforms for data storage system and distributed processing of large data sets, Hadoop HDFS and MapReduce, Spark, and others, and different ways of handling analytics algorithms on different platforms will be introduced.
  •  

    CAP 4793 - Advanced Data Science


    Credits: 3

    Prerequisites: CAP 4770 - Data Mining & Text Mining  and COP 3710 - Database 1   
    Course Description: This course introduces advanced concepts, methodologies and techniques in relation to data science including novel learning approaches, deep learning, reinforcement learning, novel data mining methodologies and emerging modes of data acquisition and aggregation. The course will develop students’ understanding of data mining concepts and their ability to carry out advanced data science projects.
  •  

    CAP 5735 - Data Visualization and Reproducible Research


    Credits: 3

    Prerequisites: None
    Course Description: A project-centered introduction to the visual display of quantitative information for both knowledge discovery and the communication of results. Fundamentals of reproducible research with attention to best practices and modern frameworks for data science project collaborations. 
  •  

    CAP 5765 - Computational Data Analysis


    Credits: 3

    Prerequisites: STA 2023 - Statistics 1  
    Course Description: This course explores advanced topics in statistical data analysis and computability. It prepares students to perform big data analysis and organization using machine learning and data mining techniques and algorithms. Topics include: multivariate statistical methods, computational statistics, classification, clustering, prediction, regression analysis, and principal components analysis.
  •  

    CAP 5770 - Data Mining & Text Mining


    Credits: 3

    Course Description: This advanced course addresses the knowledge discovery process and the use of data mining concepts and tools as part of that process. In depth analysis of processes for automatically extracting valid, useful, and previously unknown information from data sources and using the information to make decisions is also covered.
  •  

    CAP 5771 - Data Mining & Text Mining


    Credits: 3

    Prerequisites: None
    Course Description: This advanced course addresses the knowledge discovery process and the use of data mining concepts and tools as part of that process. In depth analysis of processes for automatically extracting valid, useful, and previously unknown information from data sources and using the information to make decisions is also covered.
  •  

    CAP 5775 - Data Warehousing


    Credits: 3

    Prerequisites: None
    Course Description: Advanced techniques for building and populating a data mart to support the planning, designing and building of business intelligence applications and data analytics are covered in this course, along with data governance, stewardship, and security.
  •  

    CGS 1100 - Computer Information Technology and Applications


    Credits: 2

    Prerequisites: None
    Co-requisite or Prerequisite: None
    Co-requisite: None

    Course Description: Using information technology to improve business process performance as well as the creation of value for organizations.  Spreadsheets, relational database management and design principles, information systems, and other software applications that are typically used in the workplace are presented.  The impact of AI, machine learning, data science, and cloud technologies in business solutions is examined. 
  •  

    CGS 5096 - Advanced Legal, Ethical and Management Issues in Technology


    Credits: 3

    Prerequisites: None
    Course Description: This course covers legal, ethical and regulatory issues present in the domain of applied technology. Students will learn to apply SWOT, PEST, and SEEC methods to analyze situations based on social, political, environmental, cultural and economic criteria. This course is designed to emphasize moral and ethical training for students to be well prepared for the business environment.
  •  

    CGS 5367 - Advanced Applied Information


    Credits: 3

    Prerequisites: Knowledge of database fundamentals, Statistics and modeling, computer programming.
    Course Description: This course covers historical and modern approaches of knowledge management (KM), knowledge discovery (KD), data analytics (DA), and information retrieval (IR). This is an advanced level course and is focused on direct application of the principles and methods of information science theories and model building. Students are exposed to a variety of commercial data analytic tools for visualization and analysis techniques.
  •  

    CIS 3301 - Business Intelligence


    Credits: 3

    Prerequisites: COP 3710 - Database 1  and STA 3036   
    Course Description: This course discusses the application of decision support systems in the organizational environment. Designing and implementing decision support systems with a variety of development systems as well as language, concepts, structures and processes involved in the management of information systems including fundamentals of computer-based technology and the use of business-based software for support of managerial decisions are also covered. Emphasis will be placed on enterprise resources systems such as SAP and SAS.
  •  

    CTS 4452 - Business Intelligence 2


    Credits: 3

    Prerequisites: CIS 3301 - Business Intelligence  
    Course Description:

    This is an advanced course in business intelligence covering applications such as SAP, HFM, and SAS. It is designed to lead to a certification level in one of the major ERP systems.

  •  

    CTS 4453 - Business Intelligence 3


    Credits: 3

    Prerequisites: CTS 4452 - Business Intelligence 2  
    Course Description: The course explores Web Services Architecture and methods for implementing communication of systems and software over distributed networks. Topics include: Client-side programming, distributed transactions, remote procedure calls, component objects, server side programming and network load balancing.
  •  

    ECO 2013 - Principles of Macroeconomics


    Credits: 3

    Prerequisites: None
    Course Description: This course presents the nature of economic aggregates such as investment, employment, and price levels. The interrelationship of business and government policies; applicability of economic theory to the problems of business forecasting; cyclical fluctuations and long-term economic trends are also examined. This course meets communication/writing-intensive requirements (W).
  •  

    ECO 2023 - Principles of Microeconomics


    Credits: 3

    Prerequisites: None
    Course Description: Theories of production, determination of prices and distribution of income in regulated and unregulated industries are discussed. Attention is also given to industrial relations, monopolies and comparative economic systems. This course meets communication/writing-intensive requirements (W).
  •  

    ECP 4031 - Benefit Cost Analysis


    Credits: 3

    Prerequisites: ECO 2023 - Principles of Microeconomics  
    Course Description: This course discusses the benefit-cost analysis of business and public projects, programs, and regulations. Students will be provided opportunities to conduct a benefit-cost analysis and determine if a public benefit-cost analysis is accurate.
  •  

    ECP 4044 - Economic Analysis for Technologists


    Credits: 3

    Prerequisites: ECO 2023 - Principles of Microeconomics  and MAC 2311 - Analytic Geometry and Calculus 1  and (STA 2023 - Statistics 1  or equivalent)  
    Course Description: The course applies the tools of economic analysis to develop a systematic approach to critical thinking about problems in science and technology management, particularly under conditions of incomplete or imperfect information. Topics include: time value of money; risk and uncertainty; demand approximation and forecasting; information acquisition, use, and value; real option value; optimal production and pricing under uncertainty; peak load pricing and optimal capacity; decisions in strategic environments, and market structure. When appropriate, emphasis will be placed on applications in the areas of science, engineering and technology.
  •  

    ECP 5007 - Economic Analysis for Technologists


    Credits: 3

    Prerequisites: ECO 2023 ECO 2023 - Principles of Microeconomics  and MAC 2311 MAC 2311 - Analytic Geometry and Calculus 1 , Introduction to Probability and Statistics, or equivalent courses, or permission of program director.
    Course Description: The course applies the tools of economic analysis to develop a systematic approach to critical thinking about problems in science and technology management, particularly under conditions of incomplete or imperfect information. Topics include: time value of money; risk and uncertainty; demand approximation and forecasting; information acquisition, use, and value; real option value; optimal production and pricing under uncertainty; peak load pricing and optimal capacity; decisions in strategic environments, firm structure.
  •  

    EGN 3448 - Operations Research


    Credits: 3

    Prerequisites: MAC 2311 - Analytic Geometry and Calculus 1  and (STA 2023 - Statistics 1   or STA 3032 Probability and Statistics  )
    Course Description: Basic approaches for modeling and solving operation efficiency challenges, and predicting and demonstrating cost-savings or other value-added gains.
  •  

    EGN 3466 - Discrete Event Simulation


    Credits: 3

    Co-requisite or Prerequisite: (STA 2023  or STA 3032 ) and COP 2271  
    Course Description: Discrete Event Simulation models a large complex system in order to study and analyze its dynamic behavior over time.  Simulation of complex discrete-event systems with applications in industrial and service industries.  Course topics include modeling and programming, simulations in one or more high-level computer packages such as simul8, input distribution modeling, generating random numbers, and statistical analysis of simulation output data.  The course will contain a team simulation project.
  •  

    EGS 3625 - Engineering & Technology Project Management


    Credits: 3

    Prerequisites: None
    Course Description: This course discusses planning, controlling, and evaluating technology and engineering projects. Topics include modeling, project organization, risk analysis, technical forecasting, time and cost estimation and accommodation, and resource allocation and leveling. Verbal and written technical and managerial reports are also required.
  •  

    EGS 5930 - Special Topics


    Credits: 1-3

    Prerequisites: Graduate Standing
    Course Description: This course will expose students to the current research topics in engineering. Lectures will be based on: literature review methods, scientific writing techniques and structure, industrial and academic guest lecturers, themed research paper surveys, and student presentations. The college’s concentrations will be especially emphasized in the chosen topics.
  •  

    ENT 2112 - Entrepreneurial Opportunity Analysis


    Credits: 3

    Prerequisites: None
    Course Description: In this course, students assess the personal attributes, as well as the skills base, professional talent, and educational and work experiences within an organization that are necessary to create successful business ideas. Students examine the external environment to identify trends and needs in the marketplace for potential business opportunities. Each individual has the opportunity to screen potential business ideas by assessing whether or not these compliment the individual and his/her organization based on an evaluation of its strengths and skills base, as well as the student’s personal, professional, and financial goals. Students develop initial market feasibility analyses to test their concepts through basic market research.
  •  

    ENT 3001 - Foundations of Entrepreneurship 1


    Credits: 3

    Prerequisites: EGN 1007 - Concepts and Methods for Engineering and Computer Science  
    Course Description: This course is the first in a four-course series designed to prepare the student to understand the most important aspects of turning an idea, technology or passion into a business.  This course focuses on the entrepreneur’s relationship to the customer.  This includes identifying customers, defining and refining a value proposition, and defining channels through which customers can buy the product or service. 
  •  

    ENT 3002 - Foundations of Entrepreneurship 2


    Credits: 3

    Prerequisites: ENT 3001 - Foundations of Entrepreneurship 1  
    Course Description: This course is the second in a four-course series designed to prepare the student to understand the most important aspects of turning an idea, technology or passion into a business.  This course focuses on the developing a business.  This includes defining a business model, determining how to monetize the product, developing the initial product and planning to scale the business. 
  •  

    ENT 4117 - Business Fundamentals for Entrepreneurs


    Credits: 3

    Prerequisites: None
    Course Description: Business Fundamentals for Entrepreneurs is an advanced level course. In this course you engage in a deep dive in to the planning of a business. In the first half of the course we will go over basic startup skills like understanding corporate structures and financial literacy. In the second half of the course we will go through how to finance your business and the legalities behind your business. By the end of this course you should come out with an advanced understand of the skills needed to start a business.
  •  

    ENT 4947 - Startup Execution


    Credits: 3

    Prerequisites: None
    Course Description: Startup Execution is an advanced concepts course. This course will require students to develop a project using the principles of Lean Startup, SCRUM, design thinking, and agile thinking. This course reinforces the skillset learned the previous three entrepreneurship courses and is designed to be a practicum of these skills. This course will help students master the Agile and Lean way of thinking to translate into whatever projects they do either in existing companies or startups.
  •  

    ENT 5016 - Entrepreneurship and New Venture Creation


    Credits: 3

    Prerequisites: None
    Course Description: This course teaches students how to launch a competitive technology startup from idea generation to building Minimum Viable Product (MVP) to raising seed capital. It fosters a student team’s ability to launch entrepreneurial venture. Over the course of a semester, students will work in teams, and with Silicon Valley and local partners to launch a company and pitch to venture capitalists with a prototype product or MVP. All skills needed to become a successful entrepreneur will be covered, including team-building, product development, product-market fit, customer validation, financial modeling, technology viability assessment, business plan development, venture capital, full life cycle strategy, venture launch etc. This is a hands-on, deep-dive course which requires true dedication and time commitment.
  •  

    ENT 5930 - Innovation and Emerging Technologies


    Credits: 3

    Prerequisites: None
    Course Description: This course will expose students to the current research topics in emerging technology and innovation. Lectures will be based on: literature review methods, feasibility studies, scientific writing techniques and structure, industrial and academic guest lecturers, themed research paper surveys, and student presentations. Special topics are based on the concentrations currently offered at both College of Engineering and College of Innovation and Technology. Some of these topics are 3D printing, crowd sourced gaming, mobile health systems, Cloud Computing, nanotechnology, renewable energy technologies etc.
  •  

    ENV 4610 - Sustainable Logistics


    Credits: 3

    Prerequisites: None
    Course Description: This course introduces students to current and future trends in logistics technology, policy, and sustainability. Topics include resource sustainability, environmental impacts of existing and emerging technologies and local environmental conditions and the global climate.
  •  

    ESI 3005 - Introduction to Networks and a Connected World


    Credits: 3

    Prerequisites: COP 2271C - Introduction to Computation and Programming  and

    Computer Engineering/Electrical Engineering students STA 3032 - Probability and Statistics    

    Computer Science students STA 2023 - Statistics 1   
    Course Description: Networks are deeply integrated in all aspects of our lives such as social networks, network of communication devices, the internet of things, transportation system and logistics or the network of brain cells. In this course, networks will be viewed from a graph theory perspective including directed and undirected graphs, paths, cycles, loops and trees. The course will focus on the spatial and the temporal nature of the network elements across different modes. Path flow estimation, route choice as well as link cost functions and the equilibrium principle will be discussed. The course will emphasize modeling of intelligent mobility networks by working on a class project.

  •  

    ESI 4011 - Data Analytics for Smart City & Transportation


    Prerequisites: ESI 3005 - Introduction to Networks and a Connected World  or CNT 3004C - Introduction to Computer Networks  
    Course Description: This course focuses on design strategies, simulation techniques, and data analytics to strengthen the knowledge of existing cities, and understand the needs and requirements of future cities through a data driven analysis. Smart cities utilize information and communication technologies to enhance the quality and performance of transportation, utility and energy services from cost and consumption perspectives. The course explains how smart cities operate in a controlled and monitored network environments and discusses techniques to work with data generated by transportation and communication networks, crowd-sensing systems and other relevant technologies.
  •  

    ESI 4513 - Intelligent Mobility


    Credits: 3

    Prerequisites: COP 2271C - Introduction to Computation and Programming    
    Course Description: Intelligent Mobility involves the application of advanced technologies to connect people, places, and goods. This course provides students with necessary understanding of smart and intelligent technologies that facilitate research, design, adoption and evaluation of advanced automation and connected vehicles. The emerging capabilities of automation technologies and their early deployment along with the various techniques of enterprise data management will also be discussed.
  •  

    ESI 5315 - Optimization and Simulation


    Credits: 3

    Prerequisites: None
    Course Description: This course familiarizes the student with frequently used models in Operations Research. Such models include decision analysis; optimization techniques, and Discrete-Event Simulation. Course is supplemented with real world examples and cases.
  •  

    FIN 2000 - Introduction to Business Finance


    Credits: 3

    Prerequisites: None.
    Co-requisite or Prerequisite: None.
    Co-requisite: None.

    Course Description: This course is an introduction to the principles of business finance. Emphasis is placed on understanding basic finance concepts. The major topics of study include the concept of money, the monetary system, capital markets, time value of money, savings & investment, interest rates, fiscal policies, short-term and long-term financing, and stocks and bonds.
  •  

    FIN 2001 - Introduction to Business Finance


    Credits: 3

    Prerequisites: ACG 2020 - Accounting for Managers  or ACG 2021 - Principles of Financial Accounting  
    Course Description: This course is an introduction to the principles of business finance. Emphasis is placed on understanding basic finance concepts. The major topics of study include the concept of money, the monetary system, capital markets, time value of money, savings & investment, interest rates, fiscal policies, short-term and long-term financing, and stocks and bonds.
  •  

    GEB 3373 - International & Comparative Dimensions of Business


    Credits: 3

    Prerequisites: None
    Course Description: Enterprises, markets, institutions, firm competitiveness, industry globalization, international business transactions, and entry strategies are discussed from a cross-cultural and international perspective.
  •  

    HIM 2340 - Development and Administration of Health Information Systems


    Credits: 3

    Prerequisites: None
    Course Description: This course focuses on using health information systems to support managerial decision-making. Implementation methods are discussed for the integration of clinical, personnel, and financial data collection, administration and dissemination. This course is taught from an organizational perspective and is designed to develop managerial decision making skills.
  •  

    HIM 3626 - Empirical Methods in Health Informatics


    Credits: 3

    Prerequisites: MAD 2104 - Discrete Mathematics  and STA 3036 - Probability and Statistics for Business, Data Science, and Economics   
    Course Description: This course focuses on research paradigms and methods. Various research approaches are presented, with emphasis on research design, methods, data collection and analysis techniques. Significant exploration of health statistics is also covered.
  •  

    HIM 4016 - Policy Issues in Health Informatics


    Credits: 3

    Prerequisites: None
    Course Description: This course covers regulatory, political, cultural and ethical issues as applied to national, agency, organizational and individual healthcare services and alternative delivery methods.
  •  

    HIM 4064 - Survey of the US Health Care System


    Credits: 3

    Prerequisites: None
    Course Description: This course covers historical and current foundations in the US health care system. The discussions focus on the most current emerging issues.
  •  

    HIM 4484 - Advanced Topics 1: Consumer and Population Health Informatics


    Credits: 3

    Prerequisites: HIM 4064 - Survey of the US Health Care System  
    Course Description: A comprehensive examination of healthcare needs, access, and use factors. In-depth analysis of supply and distribution of health professionals and facilities; and critical review of current issues pertinent to health care services with focus on care costs, quality assessment and financial models of care in both private health insurance systems and governmental programs.
  •  

    HIM 4508 - Assessment of Outcomes for Clinical and Medical Care Delivery


    Credits: 3

    Prerequisites: HIM 3626 - Empirical Methods in Health Informatics  and HIM 2340 - Development and Administration of Health Information Systems  
    Course Description: The course discusses comprehensive methods for determining quality of care and economic impacts of health care models. The course examines outcomes and value added from the view of patients, providers and third party payors. Focus is on determining standards and consideration for setting organizational policy.
  •  

    HIM 4644 - Implementation of EHR/EMR and Clinical Support Methods


    Credits: 3

    Prerequisites: COP 3710 - Database 1  
    Course Description: This course is an in-depth study of the clinical information system processes, models and alternatives. Discussions focus on the most current emerging trends in electronic health records, including social, ethical, economic and cultural impacts of choices.
  •  

    IDC 4942 - Data Analytics Capstone I


    Credits: 3

    Prerequisites: Senior-level Status
    Course Description: This course is part one of the Senior Capstone sequence for data science and business analytics. This advanced course covers critical thinking and problem solving techniques applied to data analytics projects. The goal of this course is to carry out an industry-relevant project in applied data science and business analytics that synthesizes concepts from data acquisition, analytics, visualization, data management, and modeling.
  •  

    IDC 4943 - Data Analytics Capstone II


    Credits: 3

    Prerequisites: IDC 4942 - Data Analytics Capstone I  
    Course Description: This course is part two of the Senior Capstone sequence for data science and business analytics. This advanced course covers critical thinking and problem solving techniques applied to data analytics projects. The goal of this course is to carry out an industry-relevant project in applied data science and business analytics that synthesizes concepts from data acquisition, analytics, visualization, data management, modeling, and application development and deployment. Students will complete intensive research and produce significant written documentation of the project.
  •  

    IDS 2144 - Legal, Ethical, and Management Issues in Technology


    Credits: 3

    Prerequisites: None
    Course Description: This is an intermediate level course intended to prepare students for legal and ethical issues they will encounter in their professional careers and student internships. The course focuses on management oriented technology issues in the legal and business environment, professionalism, and the impact of technology on society. The course also covers service-based learning.
  •  

    MAN 1590 - Introduction to Logistics and Supply Chain Management


    Credits: 3

    Prerequisites: None
    Co-requisite or Prerequisite: None.
    Co-requisite: None.

    Course Description: This course is an introduction to the processes and functions of logistics, materials, and supply chain management and focuses on creating a competitive advantage.
  •  

    MAN 3132 - Logistics and Supply Chain Management Communications


    Credits: 3

    Prerequisites: MAN 1590 - Introduction to Logistics and Supply Chain Management
    Co-requisite or Prerequisite: None.
    Co-requisite: None.

    Course Description: Communication with media and government as well as media management is presented.
  •  

    MAN 3520 - Six Sigma


    Credits: 3

    Prerequisites: STA 2023 - Statistics 1  
    Course Description: Strategies, techniques, and tools for process improvement resulting in continuous efforts to achieve stable and predictable results are covered in this class. Application of Six Sigma including managing processes, process improvement and control, and toolset application.
  •  

    MAN 3570 - Purchasing and Materials Management


    Credits: 3

    Prerequisites: None
    Course Description: Procurement, contracting cycle, methods of purchasing, source selection, receipt, inspection, and quality assurance are covered in this course. Inventory, physical distribution, surplus, salvage, and disposal are also discussed.
  •  

    MAN 3610 - Global Logistics Management


    Credits: 3

    Prerequisites: None
    Course Description: This course compares global versus national transportation management. Global transportation management, decision making, operations, logistics, supply chain, and traffic management are discussed.
  •  

    MAN 3613 - Supply Chain Risk Management


    Credits: 3

    Prerequisites: MAN 2591 Introduction to Operations and Supply Chain Management  OR MAN 3504 - Introduction to Operations and Supply Chain Management  
    Course Description: This course provides an overview of Supply chain security and risk management (SCSRM). Supply chain security in relationship to homeland security is discussed. Topics include security organizations, legislation, first response and recovery, as well as security related to maritime, container cargo, land transportation, food chain, pharmaceutical, utilities, and cyber security.
  •  

    MAN 4522 - Planning and Control Systems for Supply Chain Management


    Credits: 3

    Prerequisites: MAN 2591 - Introduction to Operations and Supply Chain Management  OR MAN 3504 - Introduction to Operations and Supply Chain Management  
    Co-requisite or Prerequisite: EGN 3448 - Operations Research  
    Course Description: Enterprise, material requirement, and manufacturing resource planning as well as computer integrated manufacturing are covered in this course.
  •  

    MAN 4545C - Logistics and Supply Chain Management Computer Software


    Credits: 3

    Prerequisites: MAN 2591 Introduction to Operations and Supply Chain Management  OR MAN 3504 - Introduction to Operations and Supply Chain Management  
    Course Description: Route planning, enterprise real estate, as well as supply chain, workforce and all-channel commerce software are discussed in this class.
  •  

    MAN 4558 - Lean Operations Management


    Credits: 3

    Prerequisites: MAN 2591 Introduction to Operations and Supply Chain Management  OR MAN 3504 - Introduction to Operations and Supply Chain Management  
    Course Description: This course discusses relationships with suppliers and customers, quality management, process improvement, and cost analysis. This course will introduce students to lean principles and practice in production and transactional business procedures. The course will provide the student with an introduction to lean operations describing how evaluations and assessments of operations systems are performed. Lean operation tools and techniques will be described and in some cases demonstrated in simulation exercises. Issues relating to employee involvement, improvement teams, training and culture will be presented. Planning for lean process implementation and the necessity of sustain improvements will be discussed. Examples of applications in manufacturing and business processes will be presented.
  •  

    MAN 4593 - National Transportation Management


    Credits: 3

    Prerequisites: COP 2271C - Introduction to Computation and Programming   
    Course Description: This course provides a comprehensive overview of transportation management and policy and includes the perspective of recent technological advancements in connected and autonomous vehicles. Carrier selection and management, purchasing, order processing, facility operation and design, distribution, operations, transportation costing and negotiation are also discussed.
  •  

    MAN 4594 - Reverse Logistics


    Credits: 3

    Prerequisites: MAN 2591 Introduction to Operations and Supply Chain Management  OR MAN 3504 - Introduction to Operations and Supply Chain Management  and EGN 3448 Operations Research  
    Course Description: In this course forward-moving logistics is compared to reverse-moving logistics. Both goods and information are discussed. Topics include federal and state regulations, waste management, recycled materials, technology, financial controls, stakeholders, and performance measurement.
  •  

    MAN 4633 - Strategic Management


    Credits: 3

    Prerequisites: MAN 2591 Introduction to Operations and Supply Chain Management  OR MAN 3504 - Introduction to Operations and Supply Chain Management  
    Course Description: This course integrates concepts and knowledge from a broad range of core business and analytical skills to explore contemporary factors such as social, technological, environmental, political, and economic conditions influencing the business environment. The course emphasizes strategic thinking in crafting and executing strategy. This course requires significant group-based work through use of case studies.
  •  

    MAN 5245 - Organizational Behavior & Leadership


    Credits: 3

    Prerequisites: Graduate Standing
    Course Description: An investigation of ethical problems in business practice. Topics include personal morality in profit-oriented enterprise; codes of ethics; obligations to employees and other stakeholders; truth in advertising; whistle-blowing and company loyalty; regulation, self and government; the logic and future of capitalism. Emphasis on business law and legal impacts on ethical decision making.
  •  

    MAN 5528 - Principles of Logistics/Transportation Systems


    Credits: 3

    Prerequisites: None
    Course Description: This course will be a project and case study based course that will discuss the management perspective of distribution, transportation, inventory, global logistics, sustainable logistics, supply chain finance, data analysis, logistics IT and RFID systems.
  •  

    MAN 5596 - Global Supply Chain Management


    Credits: 3

    Prerequisites: None
    Course Description: This course will be a project and case study based course that will focus on management and improvement of supply chain processes and performance. This course will cover the topics of: global supply chain drivers, global supply chain distribution centers, inventory, packaging, transportation, trade agreements, sustainability, cost and innovation.
  •  

    MAN 5598 - Logistics Management


    Credits: 3

    Prerequisites: None
    Course Description: This course will be a project and case study based course that will discuss the management perspective of distribution, transportation, inventory, global logistics, sustainable logistics, supply chain finance, data analysis, logistics IT and RFID systems.
  •  

    QMB 3200 - Advanced Quantitative Methods


    Credits: 3

    Prerequisites:   and COP 2271 Introduction to Computation and Programming   

     
    Course Description: Advanced concepts in statistical analysis. Linear models and experimental design, multiple regression analysis, analysis of variance with multiple classification, analysis of covariance, repeated measures analysis of variance, multiple comparison techniques, and diagnostic procedures and transformations are discussed in this course.

  •  

    QMB 5565 - Quantitative Empirical Research Methods


    Credits: 3

    Prerequisites: None
    Course Description: This course will begin with a concentrated review of probability, distributions of random variables, and hypothesis testing, and move on to provide a foundation in applied multivariate statistical methods. The course will focus not just on estimating models, but also on specifying, evaluating, and refining models to support a problem driven research agenda.
  •  

    STA 3241 - Statistical Learning


    Credits: 3

    Prerequisites: STA 3036 - Probability and Statistics for Business, Data Science, and Economics  or (MAS 3114 - Computational Linear Algebra  and STA 2023 - Statistics 1  ) 

     
    Course Description: This is an introductory-level course in supervised learning. Topics include classification and regression, cross-validation and bootstrap, model selection, dimension reduction, tree-based methods, random forests and boosting, support-vector machines, principal components, and cluster analysis. Students will have hands-on experience in model building, machine learning, and implementation.

  •  

    TRA 4174 - Hazardous Materials Management & Transportation


    Credits: 3

    Prerequisites: MAN 2591 Introduction to Operations and Supply Chain Management  OR MAN 3504 - Introduction to Operations and Supply Chain Management  
    Course Description: This course presents the packaging, transport, storage and delivery of hazardous materials. The Environmental, Health and Safety (EHS) impact is also considered.