61 courses with the subject DSS, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DSS 100Excel Competency1
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.
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.
This course is an introduction to Artificial Intelligence (AI) concepts and uses, and to the contemporary Information Technologies (IT) and Systems (IS) that enable them. Students will learn about hardware components, software applications, databases, networks, and Internet technologies. Students will learn how these technologies combine with people and processes to make Business Intelligence, Data Analytics, and Artificial Intelligence systems possible. AI coverage includes its development history, machine learning, neural networks, deep learning, robotics, natural language processing, large language models, generative AI, and other emerging topics. Students will learn to identify how AI/IT/IS affect our lives, to assess personal privacy and organizational security risks, and learn principles that will help them become discriminating, informed, and successful users. Instructors will use AI tools actively during class to guide students in their proper professional and ethical use. Students will examine AI-generated outputs critically, with the goal of equipping them to deal with these technologies in their professional lives. Assignments will include explicit AI policies to showcase responsible use in approaching and analyzing problems.
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.
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.
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.
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.
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.
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.
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.
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.
This course provides a comprehensive overview of supply chain management, focusing on the financial, strategic, and operational decisions that drive modern supply chains. Students will explore key concepts such as transportation, inventory management, and warehouse operations while understanding the impact of digital transformation in optimizing supply chains. Through a series of hands-on demos with industry-standard software tools and guest lectures, students will get exposure to real world practices. By the end of the course, students will have a strong foundation in supply chain strategy, data analytics, and the tools used for performance management and planning, preparing them to lead digital transformations in the supply chain domain.
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.
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.
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.
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.
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.
The Analytics Cup course is a 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. 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
The course is intended to provide the students with an introduction to the quantitative analysis methods utilized for problem-solving and decision making in the health care setting. The content will focus on five areas central to the administration of a healthcare organization: finance, quality, market, operations, and utilization. The course will help future health care leaders understand the data presented as well as think critically beyond the data.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
This course introduces students to building deep neural networks to solve complex problems. From chatbots to self-driving cars, deep learning has been the driving force behind the cutting-edge technology we have grown accustomed to. Research on novel deep learning architectures has expanded the applicability in areas such as home automation and healthcare. Building solutions using deep neural network architecture requires careful planning and execution. This course provides an in-depth understanding of the architecture components and of their use in building the solutions.
This course provides an in-depth introduction to agentic AI, prompt engineering and their applications in language models (LLMs). Students will learn the fundamentals of language models such as ChatGPT, agentic AI, prompt design, data analytics with prompts, and advanced techniques such as few-shot learning and LangChain. Ethical considerations in AI and LLMs will also be addressed. The course includes a hands-on project where students will apply their knowledge to design and implement a prompt engineering solution.
This course provides a general introduction to Artificial Intelligence techniques for decision making and problem solving. The course combines the goal of giving student a broad introduction to the fundamentals of AI with that of giving them actionable knowledge of current tools and techniques useful for building decision-support systems.
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.
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.
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.
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.
