Decision & System Sciences

DSS 100 Excel Competency (1 credit)

Mastering Excel is a critical for students as they enter the workforce. In Excel Competency, students will learn basic, intermediate and advanced Excel skills including financial, accounting, statistical, and decision making. The course will explore the use of excel in all fields of the business school. Students will be provided with instruction and short videos for reinforcement and review.

Attributes: Undergraduate

DSS 150 Data Visualization (3 credits)

The human mind can handle significant amounts of information, but is not able to process the large masses of data required for business decision-making. There is a vast number of data processing and visualization technologies, tools, and techniques available to business users, but it is important to first understand how human consumers of information receive and interpret it. This class uses an interdisciplinary approach to examine methods for data presentation which are more meaningful to users. Students will learn a variety of concepts related to information gathering, processing, and presentation, and have some practice with a data visualization tool. Course activities draw from various disciplines including information systems, computer science, cognitive psychology, economics, graphic design, and research methods to examine and evaluate information. Students will present and analyze data sets in graphical form and explain their findings via written, oral, and visual presentations.

Attributes: First-Year Seminar, Undergraduate

DSS 200 Intro to Information Systems (3 credits)

Information systems play a critical operational, tactical and strategic role in global businesses. Technology has both a direct and indirect impact on how firms do business, where they do business, and on the products and services they market. In this course, the dynamic and ongoing impact of technology on business operations is examined at the industry, corporate, and individual levels. Topics examined include the effect of technology on business processes, services, and products, the supply chain, customer relationship management, decision- making, knowledge management, communications, outsourcing, information security, and the ethical use of technology.

Attributes: Undergraduate

DSS 210 Business Statistics (3 credits)

This course covers probability concepts as well as descriptive and inferential statistics. The emphasis is on practical skills for a business environment. Topics include probability distributions, estimation, one-sample and two-sample hypothesis testing, inferences about population variances, and chi-square test of independence. Students will also become familiar with spreadsheet applications related to statistics and with statistical software.

Prerequisites: DSS 100 (may be taken concurrently)

Attributes: Undergraduate

DSS 220 Business Analytics (3 credits)

Every organization, must manage a variety of processes. In this course the student will development an understanding of how to evaluate a business process. Additionally, the art of modeling, the process of structuring and analyzing problems so as to develop a rational course of action, will be discussed. The course integrates advanced topics in business statistics-linear and multiple regression and forecasting, production and operations management-linear programming and simulation, and project management. Excel software is used for problem solving.

Prerequisites: DSS 210

Attributes: Undergraduate

DSS 251 Internship (3 credits)

This course is reserved for students completing internships for credit. This course may not count as a major elective for BIA, ML/AI or SCM. It may not count as a minor elective for BIA, ML/AI or SCM. Students may count this course as a general elective and must be supervised by a DSS faculty member.

Attributes: Undergraduate

DSS 315 BIA Concepts & Practices (3 credits)

This course is an introduction to various scientific viewpoints on the decision-making process. Viewpoints covered include cognitive psychology of human problem-solving, judgment and choice, theories of rational judgment and decision, and the mathematical theory of games, and these topics will be focused in the field of Business Intelligence and Analytics, with systems theory as an overarching theme. Latest academic research and industry practice will be presented by guest speakers to motivate the topic an enhance learning.

Prerequisites: DSS 200

Attributes: Undergraduate

DSS 321 Project Management (3 credits)

This course introduces students to project management - an important skill for every student to successfully identify, plan, execute, monitor and close-out projects. Topics covered include introduction to project management, project selection and prioritization, project chartering, organizational capability, leading and managing project teams, stakeholder analysis and communication planning, scheduling projects, resourcing projects, budgeting projects, risk planning, quality planning, project supply chain management, determining project progress and results, and finishing projects and realizing benefits. Throughout the course, students will gain valuable project management experience by working in small groups.

Attributes: Undergraduate

DSS 325 Open Source Program Lang (3 credits)

As data volume grows across industry and government, techniques to manage and use this data are critical. In this course, we learn the use of open-source programming languages, such as Python, that make it possible to deal with the demands placed on us by big data. The course covers topics including variables, input and output, compound data types, conditionals and branching, functions, recursion, data dictionaries, exception handling, and object-oriented programming. The course stresses good programming style and practical applications.

Prerequisites: DSS 220

Attributes: Undergraduate

DSS 330 Database Management (3 credits)

Databases help organizations store what they know. Everything from information about business partners to supply chain management data to customer/consumer behavior is stored in a database of some type. It is no exaggeration to say all investment in computer technologies over the past few decades has been made in order to enable the collection, storage, analysis, synthesis, and communication of data, and it is all facilitated by database systems. As such, databases are the foundational technologies for enabling business intelligence and analytics services and activities. Students in this course will be exposed to the theoretical underpinnings of database systems, their component technologies, enabling processes, and to current and emerging applications. Students will obtain basic hands-on experience with an end-user database application (MS Access), an open-sourced enterprise-level system (MySQL), and an understanding of the capabilities of all enterprise-level relational database management systems. The course is required of all students pursuing a BI&A major or minor.

Prerequisites: DSS 200 or CSC 115 or CSC 120 or MHI 301

Attributes: Undergraduate

DSS 335 Found of Supply Chain Mgmt (3 credits)

This course introduces a comprehensive and fundamental understanding of supply chain management (SCM) for undergraduate students. It contains analytical concepts, case studies, and recent examples in academia and industry. It covers the major issues and models practitioners concerning the related fields: inventory management, SCM network design and planning, supply chain integration and strategy, distribution strategies, procurement and outsourcing, flexibility and Toyota Production System (TPS), risk management, Sustainable supply chains, recent IT in SCM (e.g., AI, blockchain, Internet of Things, robotics), etc. Most chapters initiate with an emerging or mature case in the field. After all the learning and discussion in the chapter, one would be expected to offer the case a practical and constructive solution with grounded theories or models. Modeling and programming are not among the course objectives, while some classic models will be introduced for basic optimization understanding purposes.

Attributes: Undergraduate

DSS 350 SCM Dynamics (3 credits)

This course is tailored for undergraduate students majoring in Supply Chain Management, Business Intelligence and Analytics, and Computer Science. It provides a comprehensive foundation in simulation-based optimization and the practical application of Pyomo, empowering students to tackle complex challenges in real-world supply chain scenarios. It integrates stochastic control and static optimization techniques and explores Reinforcement Learning and meta-heuristics to optimize profits, estimate lost sales, and use advanced demand distributions. It also provides an explanation of how you can optimize a multi-echelon supply chain. Most of the chapters end with case studies that explain how these methods can be applied in real-world settings. Modeling and programming are required as part of the course objectives, while demo codes will be offered to students to start with their own projects in class and their future careers.

Prerequisites: DSS 220

Attributes: Undergraduate

DSS 360 CPIM Certification (3 credits)

This course includes content needed to pass the exam for part I of the Certified in Planning and Inventory exam offered by the Association for Supply Chain Management. Agility is critical to thriving supply chains. CPIM certification shows employers than an individual knows how to effectively manage disruptions, demand variations and supply chain risk. Topics include SC fundamentals. Operating environments, financial fundamentals, demand management, voice of the customer (VoC), product and process design, capacity management, planning, inventory, purchasing cycle and distribution.

Prerequisites: DSS 335

Attributes: Undergraduate

DSS 365 CSCP Certification (3 credits)

This course includes content needed to pass the exam for Certified Supply Chain Professional (CSCP) offered by the Association for Supply Chain Management. Topics include SC design and strategy, procurement and delivery of goods, supply chain partner relationships, reverse logistics; measure, analyze and improve supply chains; compliance with standards, and risk management.

Prerequisites: DSS 335

Attributes: Undergraduate

DSS 370 Insurance Data & Analytics (3 credits)

A revolution is well underway in statistics: "Data & Analytics", "Big Data", and "Data Science" are now embraced as the new table stakes in data analysis. Given the quantitative nature of risk, the risk management professional is well-positioned to partner with other disciplines to advance the potential of these concepts to benefit the insurance industry. In order to be a participant in the conversation, however, the risk management professional should have knowledge of the language, practices, tools and techniques of the technology supporting this revolution.

Prerequisites: DSS 210 and RMI 200

Attributes: Undergraduate

DSS 415 Data Wrangling & Visualization (3 credits)

Data Wrangling is the process of transforming and/or mapping data from its "raw" initial collected form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics and visualization. In this course, you will learn how to import, clean, structure, and effectively display data. Underlying data, in many business applications, comes from multiple sources and may have missing values and inconsistencies that need to be rectified. Data visualization is an interdisciplinary field that deals with graphically representing that data. It is a particularly efficient way of communicating when the data is numerous in size (rows and/or columns) and also in multiple formats (quantitative, qualitative, geographical, etc.). Data cleansing and wrangling will then allow the creation of realistic, insightful, and comprehensible data visualizations, while avoiding misleading techniques. Through discussion, individual research, and hands-on use of cutting-edge tools (including: Alteryx, Excel, and Tableau), we will develop knowledge and skills that will be immediately applicable in any analytics field. Hands-on projects are used throughout the course to allow students to see immediate results of the tools and techniques learned.

Prerequisites: DSS 220

Attributes: Undergraduate

DSS 416 Data Wrangling: Ethics Int. (3 credits)

Data Wrangling is the process of transforming and/or mapping data from its “raw” initial collected form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics and visualization. In this course, you will learn how to import, clean, structure, and effectively display data. Underlying data, in many business applications, comes from multiple sources and may have missing values and inconsistencies that need to be rectified. Data visualization is an interdisciplinary field that deals with graphically representing that data. It is a particularly efficient way of communicating when the data is numerous in size (rows and/or columns) and also in multiple formats (quantitative, qualitative, geographical, etc.). Data cleansing and wrangling will then allow the creation of realistic, insightful, and comprehensible data visualizations, while avoiding misleading techniques. Through discussion, individual research, and hands-on use of cutting-edge tools (including: Alteryx, Excel, and Tableau), we will develop knowledge and skills that will be immediately applicable in any analytics field. Hands-on projects are used throughout the course to allow students to see immediate results of the tools and techniques learned. Moreover, the potential for benefit(loss), can be translated into decision-making, risk assessment and strategic planning. It can provide managers with tools for measuring the project viability. We will examine ethical precepts and theories within the context of global community development.

Prerequisites: DSS 220

Attributes: Ethics Intensive, Faith Justice Course, Undergraduate

DSS 420 Introduction to Data Mining (3 credits)

The "business intelligence" wave has quickly spread throughout the business sector. This wave begins with canned reports, through query & reporting, data warehouse/marts, online analytical processing (OLAP), then to data mining. This course discusses how data mining techniques are used to transform large quantities of data into information to support tactical and strategic business decisions. While the student will be introduced to data mining techniques, the focus of the course is learning when and how to apply data cleaning, appropriate methodology, and more importantly read and process output meaningfully in business applications and explain the output clearly and concisely without analytics jargon. The aim of this course is to provide the student with the foundation to data mine and understanding of the data mining process. It includes an introduction to some advanced statistical decision-making tools, including several multivariate data mining techniques, factor/principal component analysis, cluster analysis, ANOVA, multivariate regression, and logistic regression.

Prerequisites: DSS 220

Attributes: Undergraduate

DSS 425 Analytics Cup (3 credits)

The Analytics Cup course is an annual competition in which teams will solve a real-world problem situation utilizing their Business Intelligence (BI) and/or Business Analytics (BA) skills. During the course, all the students will learn about new BI and BA techniques and software, such as Trade Promotion Optimization (TPO), text analytics, and optimization. Each team will dig deeper into the application of one or more these software packages to solve their real-world problem situation. The competition culminates where each team presents their solution to a panel of judges who select the SJU Analytics Cup Champions.

Prerequisites: DSS 420

Restrictions: Enrollment is limited to students with a major, minor, or concentration in Business Intellig. Analytics.

Attributes: Undergraduate

DSS 430 Alternative Risk Financing (3 credits)

The course focuses on the theory and practice of evaluating the value impact of risk financing options. The course covers simulating risk distributions, evaluating retention and transfer strategies, evaluating risk financing options (after-tax, NPV), off-shore financing, role of reinsurance, forecasting risk loss, capital market functions, forming captive insurance companies. The course's projects rely heavily on Excel as a tool to evaluate and model risk financing options - using both simulated and real-world data. Group projects also utilize Access to create relational databases of risk data for analysis. This course is aligned with the risk management industry designation exam, ARM 56. This course is also approved under The Institutes Collegiate Studies for CPCU program. DSS 330 is recommended for this course, but is not a required prerequisite.

Prerequisites: DSS 220 and RMI 301

Attributes: Undergraduate

DSS 435 Advanced Business Analytics (3 credits)

This course extends several of the foundation Business Analytics topics from DSS 220 to address more complex problem solving situations. Techniques to be covered are optimization models (linear programming, integer programming, non-linear programming and others), simulation models, optimization/simulation models, and decision analysis. These techniques will all be presented in the context of real world problems. To improve the students' ability to develop such models, fundamental problem solving skills of modeling and process analysis will be developed.

Prerequisites: DSS 220

Attributes: Undergraduate

DSS 440 Six Sigma Apps & Foundations (3 credits)

This course presents an introduction to Six Sigma and its vocabulary, coverage of business statistics focusing on hypothesis testing, multiple regression, experimental design, analysis of variance, statistical process control, analytic hierarchy process, discrete event simulation, and other tools of Six Sigma. This course roughly covers the material covered on the yellow belt/green belt certification examination.

Prerequisites: DSS 220

Attributes: Undergraduate

DSS 445 Statistical Programming Lang (3 credits)

The goal of this course will be to use R's command line interface (CLI) to build familiarity with the basic R toolkit for statistical analysis and graphics. Specifically, students will learn good programming practices to manage and manipulate data, become familiar with some of R's most commonly used statistical procedures, and apply knowledge of data mining techniques (Multivariate Statistics, Regression, ANOVA, Cluster Analysis, Logistic Regression) for complex data sets using R.

Prerequisites: DSS 420 or MAT 423 or ECN 410

Attributes: Undergraduate

DSS 447 Resilient Supply Chains (3 credits)

Supply chains have historically been optimized with respect to costs and other specific attributes, including the provisioning of materials, manufacturing processes, and distribution logistics. This highly optimized network of exchanges is therefore sensitive to sudden or extreme changes in demand, such as those experienced during the COVID-19 pandemic. This course introduces students to bleeding-edge techniques for making supply chains more resilient. Specific topics include methods for the identification of critical dependencies and for the evaluation, verification and restoration of properties of the supply chain.

Prerequisites: DSS 200 and DSS 220

Attributes: Undergraduate

DSS 451 Machine Learning for Bus I (3 credits)

This course will introduce Artificial Intelligence (AI) and Machine Learning (ML) applications and methods in Business. The course will begin by exploring terminology, basic concepts and definitions in AI/ML and move on to understanding what AI can and cannot realistically do. A variety of ML methods will then be introduced. The Python Programming language will be used to analyze data using these methods (starting with a mini-bootcamp to review programming concepts). Frequent use of real-world business case studies will be made in order to help connect these concepts to business applications.

Prerequisites: (DSS 325 or CSC 115 or CSC 133) and (DSS 420 or MAT 424)

Attributes: Undergraduate

DSS 455 Machine Learning for Bus II (3 credits)

This course will build upon the methods learned in DSS 451 and will also introduce some of the most popular Machine Learning Algorithms currently. This will include Neural Networks and Deep Learning, which are one of the fastest growing and widely used ML algorithms in the industry. The Python Programming language will be used to analyze data using these methods. Frequent use of real-world business case studies will be made in order to help connect these concepts to business applications.

Prerequisites: DSS 451

Attributes: Undergraduate

DSS 465 Supply Chain Analytics (3 credits)

This course introduces the undergraduate-level quantitative theory and tools to remedy the supply chain management (SCM) crisis after the COVID-19 pandemic. Students from SCM major or related major in their junior or senior year are encouraged to take this interdisciplinary course covering techniques and knowledge from Microeconomics, Statistics, Operations Management, and Data Analytics (a brief review of the required knowledge in this field is scheduled before the introduction of modeling). Students will be exposed to analytical concepts and techniques, case studies, and recent examples in academia and industry. It covers the major issues and models practitioners concerning the related fields: inventory management, logistics management, SCM network design and planning, distribution strategies, qualitative and quantitative forecasting, data analytics, recent IT in SCM (e.g., AI, blockchain, Internet of Things, robotics), etc. Basic modeling and programming can be expected on the course. However, the teaching approaches, contents, difficulty levels, and final deliverables should seamlessly fit the expectations, backgrounds, and prior knowledge of students in each section.

Prerequisites: DSS 220

Attributes: Undergraduate

DSS 470 DSS Special Topics I (3 credits)

Content of this course varies to allow for ongoing changes to business intelligence and related fields. The instructor will provide the course description for a given semester.

Attributes: Undergraduate

DSS 471 DSS Special Topics II (3 credits)

Content of this course varies to allow for ongoing changes to business intelligence and related fields. The instructor will provide the course description for a given semester.

Attributes: Undergraduate

DSS 493 Independent Study I (3 credits)

Students will study a topic in decision and system sciences with a faculty mentor.

Attributes: Undergraduate

DSS 494 Independent Study II (3 credits)

Students will study a topic in decision and system sciences with a faculty mentor.

Attributes: Undergraduate

DSS 509 Curricular Practical Training (1 credit)

Curricular Practical Training (CPT) is defined by US Citizenship and Immigration Services as employment which is an integral part of an established curriculum, including alternative work/study, internship, cooperative education, or any other type of required internship or practicum that is offered by sponsoring employers through cooperative agreements with the institution.

DSS 600 Found for Bus Intel & Analyts (3 credits)

This course provides an overview of operations for the student new to business. It is broken into three major component parts. The first is the introduction of operations. We examine the relationship between strategic and tactical decisions and the overall impact on the company in both manufacturing and service operations. The second part is focused on the management of processes and providing the necessary tools to understand the flow of information and materials in a business setting, including forecasting and describing arrival and service processes. The third part examines the supply chain through presentation of the supply chain strategies and sustainability.

Restrictions: Enrollment limited to students in the MSBI program. Enrollment is limited to Graduate level students.

DSS 605 Emerging Tech for Business (3 credits)

Businesses must be innovative to stay competitive in the marketplace. Technology allows businesses to innovate, improve their processes, create and update products and services, and transform and create new business models. Business leaders, decision-makers, and employees must continuously look for emerging technologies and understand and incorporate them early enough to stay ahead of competitors. This course will introduce students to several emerging technologies and concepts of innovation. The focus will be emerging technologies' business applications, impact, risks, opportunities, etc. In addition to business impact, the course will discuss the environmental and societal impacts of using emerging technologies. Students will use different learning mediums and methods, including books, online materials, active in-class discussions and discussion boards, writing papers, and presentations.

Restrictions: Enrollment is limited to Graduate level students.

DSS 610 Business Analytics (3 credits)

The aim of this course is to provide the student with an understanding of several analytics techniques and to provide some insight into how these tools may be used to analyze complex business problems and arrive at a rational solution. The techniques to be studied are data visualization, forecasting, linear programming, decision analysis and simulation. Cases of increasing complexity will be used to emphasize problem description, definition, and formulation. The computer will be used extensively throughout the course, primarily by using available programs to perform the calculations after the problem has been correctly formulated. Emphasis will be placed on the interpretation and implementation of results. In addition, we will examine the current/future of analytics. Students must complete the ALEKS online Statistics Proficiency module before enrolling in DSS 610.

Prerequisites: HSB Foundation with a score of DS510

Restrictions: Enrollment is limited to Graduate level students.

DSS 615 Python Programming (3 credits)

Python is an open source programming language that focuses on readability, coherence and software quality. It boosts developer productivity beyond compiled or statically typed languages and is portable to all major computing platforms. This course is designed as an introduction to python programming and the characteristics that make it unique. Student will learn the use of the python interpreter, how to run programs, python object types, python numeric types, dynamic typing, string fundamentals, lists and dictionaries, and tuples and files.

Prerequisites: DSS 610

Restrictions: Enrollment is limited to Graduate level students.

DSS 620 Con & Pract of DSS Modeling (3 credits)

Building on the background of previous courses, this course will extend the use of spreadsheet modeling and programming capabilities to explore decision models for planning and operations using statistical, mathematical, and simulation tools.

Prerequisites: DSS 610

Restrictions: Enrollment is limited to Graduate level students.

DSS 625 Fund of Database Mgmt Systems (3 credits)

This course covers the introductory database management concepts such as data normalization, table relationships, and SQL. In addition to a basic theoretical presentation of the database design concepts, students will be required to design and develop a database application using a modern fourth generation language system. This course teaches students the foundations of database management systems and relational data model. Another basic component of this course is the use of SQL – Structured Query Language. Students will also learn how to create databases, modify databases, and develop queries using SQL.

DSS 630 Database Mgmt Theory & Pract (3 credits)

Business Intelligence rests on the foundation of data storage and retrieval. In this course, students will be presented with the theory of operational database design and implementation. The concepts of normalization, database queries and database application development will be introduced using contemporary tools and software such as SQL for program development.

Prerequisites: DSS 610

Restrictions: Enrollment is limited to Graduate level students.

DSS 640 Managing Data Intelligence (3 credits)

The objective of this course is to introduce the students to business analytics technologies with a major emphasis on advanced data management technologies such as data warehousing and distributed systems. Further, the course also focuses on illustrating various analytics techniques and their applications. In addition, the course also provides students an illustration of how organizations employ data intelligence to make decisions or to gain a competitive edge.

Prerequisites: DSS 610 and DSS 630

Restrictions: Enrollment is limited to Graduate level students.

DSS 650 Process Simulation & Analysis (3 credits)

Using contemporary software tools, students will learn to break down the steps of business process analysis and design. They will first build process maps, and then use queueing theoretic concepts to statistically characterize arrival and service times. They will build simulation models in multiple software applications, and complete hypothesis tests to determine the significance of differences in scenarios.

Prerequisites: DSS 610

Restrictions: Enrollment is limited to Graduate level students.

DSS 655 Optimization Modeling (3 credits)

This course provides the student with a deeper understanding of several optimization methods, such as linear programming, integer linear programming, multiple objective, and nonlinear programming. and provide some insight into how these tools may be used to analyze complex business problems and arrive at a rational solution.

Prerequisites: DSS 610

Restrictions: Enrollment is limited to Graduate level students.

DSS 660 Introduction to Data Mining (3 credits)

This course in the Business Intelligence Program will extend the concepts of data mining to an exploration of a contemporary Data Mining tool set on a large live data set. In this course, students will be encouraged to find the patterns in the data and to prepare reports and presentations describing the implications of their findings.

Prerequisites: DSS 610 or MHI 563

Restrictions: Enrollment is limited to Graduate level students.

DSS 665 R Statistical Language (3 credits)

The goal of this course will be to use R's command line interface (CLI) to build familiarity with the basic R toolkit for statistical analysis and graphics. Specifically, students will learn good programming practices to manage and manipulate data, become familiar with some of R's most commonly used statistical procedures, and apply knowledge of data mining techniques (Multivariate Statistics, Regression, ANOVA, Cluster Analysis, Logistic Regression) for complex data sets using R.

Prerequisites: DSS 610 and DSS 660

Restrictions: Enrollment is limited to Graduate level students.

DSS 670 Data Visual & Perf Analyt (3 credits)

This course introduces the concept of creating meaningful performance measures, identifying key performance indicators, graphic design, and best practices in data visualization through short hands-on projects. Students will work to understand best practices for visual design of performance dashboards to communicate, rather than dazzle, understand current software and uses, and leverage modern tools to discover stories within the data. Emphasis will be placed on learning how to present critical information that provides insightful and actionable results. By the end of the course, students will also be prepared to take the Tableau certification exam and the Qlik Sense certification exam.

Restrictions: Enrollment is limited to Graduate level students.

DSS 675 Decision Analysis/Game Theory (3 credits)

This course introduces decision making techniques for systems operating under uncertainty and a set of analytical tools used to study the strategic interactions of individuals and institutions. The course covers probability and Bayesian inference, basic concepts of decision theory, decision tree, static and dynamic games (under complete and incomplete information). Applications include cooperation, price setting under imperfect competition, trust and reputation building, bargaining, auctions, signaling, and matching markets.

Prerequisites: DSS 610

Restrictions: Enrollment is limited to Graduate level students.

DSS 676 Data Wrangling & Adv Visualtn (3 credits)

Data Wrangling is the process of transforming and/or mapping data from its "raw" initial collected form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics and visualization. In this course, you will learn how to import, clean, structure, and effectively display data. Underlying data, in many business applications, comes from multiple sources and may have missing values and inconsistencies that need to be rectified. Data visualization is an interdisciplinary field that deals with graphically representing that data. It is a particularly efficient way of communicating when the data is numerous in size (rows and/or columns) and in multiple formats (quantitative, qualitative, geographical, etc.). Data cleansing and wrangling will then allow the creation of realistic, insightful, and comprehensible data visualizations, while avoiding misleading techniques. Through discussion, individual research, and hands-on use of cutting-edge tools (Alteryx, Excel, and Tableau), we will develop knowledge and skills that will be immediately applicable in any analytics field. This course will heavily utilize Alteryx and focus on building on the Data Visualization knowledge learned in DSS 585. Hands-on projects will be leveraged throughout the course to allow students to see immediate results of the tools and techniques learned. Note: Alteryx is only available for Windows and uses a substantial memory. All students must have access to a Windows based computer.

Prerequisites: DSS 670

DSS 680 Predictive Analytics (3 credits)

This course extends the data mining process to the predictive modeling, model assessment, scoring, and implementation stages. In this course, professional data mining software and small and large data sets will be used to effectively analyze and communicate statistical patterns in underlying business data for strategic management decision making.

Prerequisites: DSS 610 and DSS 660

Restrictions: Enrollment is limited to Graduate level students.

DSS 690 Special Topics Course (3 credits)

Content of this course varies to allow for ongoing changes to business intelligence and related fields. The instructor will provide the course description for a given semester.

Restrictions: Enrollment is limited to Graduate level students.

DSS 693 Independent Study I (3 credits)

Students will study a topic in decision and system sciences with a faculty mentor.

DSS 694 Special Topics (1-3 credits)

Topics will vary according to the semester in which the class is offered.

DSS 710 Six Sigma Apps & Found (3 credits)

This course prepares the student for the Six Sigma Green Belt certification examination. Topics include the Six Sigma dashboard and related models (DMAIC, DMADV, DFSS: QFD, DFMEA, and PFMEA), selecting and managing projects, organizational goals, lean concepts, process management and capability, and team dynamics and performance.

Prerequisites: DSS 610

Restrictions: Enrollment is limited to Graduate level students.

DSS 720 Supply Chain Analytics (3 credits)

Management of supply chains is critical to the success and profitability of all businesses, whether manufacturing or service companies. This course examines supply chains and the business analytic tools which are most effective in developing supply chain efficiencies and supply chain value. Topics include supply chain strategy, network and system design, operations management, sourcing, logistics, forecasting, inventory management, relationship management and sustainable supply chain management.

Prerequisites: DSS 610

Restrictions: Enrollment is limited to Graduate level students.

DSS 730 Digital Analytics (3 credits)

This course explores the methods used to measure, analyze, and present the performance of websites, mobile applications, social platforms, as well as complementary platforms such as video, email, and podcasts. We use common tools like Google Analytics and Tag Manager to measure and promote the websites you build during course. Emphasis is on the application of these methods to support investment decisions and the continuous improvement of digital properties in practice.

Restrictions: Enrollment is limited to Graduate level students.

DSS 740 Analytics w/ Machine Learning (3 credits)

Machine learning is a branch of computer science and related artificial intelligence methodologies that can "learn" how to perform useful tasks from prior data. This course teaches students different machine learning techniques such as statistical pattern recognition, supervised and unsupervised learning, regularization, clustering, decision trees, neural networks, genetic algorithms, and Naïve Bayes and illustrates how to implement learning algorithms using machine learning software packages. Students will learn to apply these techniques to analyze data collected from systems and processes of interest, with the purpose of uncovering dependencies, and identifying patterns and behaviors of interest.

Prerequisites: DSS 610 and DSS 615

DSS 750 Fundamentals of Cyber Security (3 credits)

This course introduces students to the interdisciplinary field of cybersecurity by discussing the evolution of information security into cybersecurity, cybersecurity theory, and the relationship of cybersecurity to nations, businesses, society, and people. Students will be exposed to multiple cybersecurity technologies, processes, and procedures, learn how to analyze the threats, vulnerabilities and risks present in these environments, and develop appropriate strategies to mitigate potential cybersecurity problems.

Prerequisites: DSS 610

Restrictions: Enrollment is limited to Graduate level students.

DSS 760 CPS Framework (3 credits)

This course introduces students to the CPS Framework, which was developed by the National Institute of Standards and Technology (NIST) in an effort to facilitate a shared understanding of cyber-physical systems, their foundational concepts and their unique dimensions. Cyber-physical systems are smart systems that include interacting networks of physical and computational components. They are widely recognized as having great potential to enable innovative applications and impact multiple economic sectors in the worldwide economy. Through the use of a shared vocabulary, the CPS Framework facilitates a thorough analysis of complex systems and processes, the uncovering of dependencies, weaknesses, risks, and the identification of corrective actions, both within the cyber domain and outside of it.

Prerequisites: DSS 610

DSS 770 Special Topics (3 credits)

Content of this course varies to allow for ongoing changes to business intelligence and related fields. The instructor will provide the course description for a given semester.

Restrictions: Enrollment is limited to Graduate level students.

DSS 790 Adv Topics: Cyber Analytics (3 credits)

Content of this course varies to allow for ongoing changes to cyber analytics and related fields. The instructor will provide the course description for a given semester.

Prerequisites: DSS 610

MHI 205 Digital Health (3 credits)

This course provides an overview of the evolving role of technology in the delivery of healthcare services at both the organizational and individual levels. Topics examined include the adoption of telehealth, increased virtualization of care settings, and role of the federal government in advancing technology use. Students gain exposure to common terms and build a vocabulary around computing services, privacy regulations, and the importance of information services in a healthcare setting.

Attributes: Undergraduate

MHI 301 Health Info Management Systems (3 credits)

A critical skill for health professionals is to be able to gather, organize, analyze and safely store important health information. This course provides an overview of healthcare information management and applications and technologies within healthcare organizations like the electronic health record (EHR).

Attributes: Undergraduate

MHI 550 Research Methods (3 credits)

Explores the history of health research, basic principles and types of research in order that health professionals will be able to critically evaluate research in their respective fields. This course is a combination of lecture, discussion and experiential learning designed to instill a critical understanding of the research process for application to clinical practice.

Restrictions: Enrollment is limited to Graduate level students.

MHI 560 Health Informatics (3 credits)

A survey of the current use of information technology in the clinical and management practice for the healthcare delivery enterprise. Students will become familiar with the basic terminology, strategies, and utilization of IT as a key component in the delivery of patient care in a simulated environment.

Restrictions: Enrollment is limited to Graduate level students.

MHI 561 Digital and Connected Health (3 credits)

A review of how patient centric technologies play a role in health and wellness. Students will become familiar with emerging trends in remote patient monitoring, telehealth, mobile applications (apps) and other novel technologies.

Restrictions: Enrollment is limited to Graduate level students.

MHI 562 Database for Health Care (3 credits)

This course provides hands on use of database management tools and structured query language (SQL). Specific applications will be explored with an emphasis placed on the practice of organizing, identifying, and uniting disparate sources of health care data.

Prerequisites: MHI 560 or HAD 560

Restrictions: Enrollment is limited to Graduate level students.

MHI 563 Data Analysis for Health Care (3 credits)

Health care systems increasingly create and capture data necessitating a focus on data analysis for quality improvement. This course builds on data organization skills with an emphasis on analyzing process, outcomes, and relations captured in the health record and across other health related data elements. Students will use data visualization tools paired with quantitative data driven techniques which aid in addressing challenges associated with the Triple Aim in healthcare.

Prerequisites: (MHI 560 or HAD 560) and (MHI 562 (may be taken concurrently) or HAD 562 or DSS 625 (may be taken concurrently) or DSS 630 (may be taken concurrently))

Restrictions: Enrollment is limited to Graduate level students.

MHI 564 Privacy&Security: Health Care (3 credits)

Regulatory and ethical condensations require healthcare practitioners to protect patient information. This course presents both the regulatory framework, technical requirements, and administrative responsibilities to adhere to established laws governing the collection and use of protected health information (PHI).

Restrictions: Enrollment is limited to Graduate level students.

MHI 565 Health Data Standards (3 credits)

Health information requires an understanding of various data standards to allow for the structure and exchange of health data. This course explores the approach and need for standards in the areas of eXtensible Markup Language (XML), laboratory information systems, radiology information systems, and electronic health records. There is a strong focus on the development and implementation of widely recognized clinical documentation formats using HL7 and FHIR based standards.

Prerequisites: MHI 560 or HAD 560

Restrictions: Enrollment is limited to Graduate level students.

MHI 670 Special Topics in MHI (3 credits)

Content varies for ongoing developments in the field of health informatics. The instructor will provide the course description.

Prerequisites: MHI 560 or DSS 610

Restrictions: Enrollment is limited to Graduate level students.

MHI 700 Health Informatics Capstone (3 credits)

The capstone course is the final class for students with an interest in the field of health informatics. Students will utilize skills and competencies gained across the curriculum to design strategies and approaches which help to leverage technology to deliver healthcare. Students will evaluate systems and work in coordinated groups based on the persona of senior healthcare executives

Prerequisites: (MHI 560 or HAD 560) and (MHI 561 or HAD 561) and (MHI 550 or HSV 550) and (MHI 562 or DSS 625 or DSS 630 or CSC 621 or MHI 564 or HAD 564)