Course Descriptions

This course provides fundamental computer literacy skills for manufacturing students in a MS Windows environment. Productivity software applications such as Word, Excel, OneNote, Teams, and PowerPoint are utilized with an emphasis on manipulating data for personal and professional communication. These applications will be directed towards computer mathematics, engineering economy calculations, and preliminary research techniques.

This course introduces the concepts of manufacturing including perspectives on overview, designing, managing, and improving manufacturing operations and systems.  An overview of how manufacturing processes have advanced from pre-industrial times through the present is discussed covering the agricultural revolution, industrial revolution (Industry 1.0-5.0), and information revolution.  Other topics of discussion include manufacturing logistics (from materials handling inside the factory via warehousing to supply chain management), global awareness, and ethics in manufacturing.

This course uses the various tools and techniques for processing metallic materials as a vehicle for developing a solid foundation for the hands-on manufacturing activities and industrial safety fundamentals used throughout the Advanced Manufacturing curriculum. Concepts introduced include precision measurement and layout, material properties and handling, and the use of various hand tools and machinery used in metal fabrication. Completion of OSHA-approved safety training and consistent demonstration of safe and responsible operation of lab equipment is a required part of this coursework.

This course is an introduction to analog and digital electronics as it relates to advanced manufacturing through hands-on activities centered around building and logically troubleshooting circuits and devices. The concepts and theories will be covered in an industrial and or an advanced manufacturing setting. Use of instrumentation will be stressed with the application of problem-solving techniques.

The Computer-Aided Design (CAD) course introduces three dimensional solid modeling design skills for manufacturing – skills critical in modern manufacturing environments. Using industry-leading software packages, content stresses foundational skills in 2D modeling, 3D solid object modeling, blueprint reading, and computer-aided drafting. The student will learn to create objects and assemblies in the digital realm; read and accurately create useful technical drawings that enable accurate communication of design concepts and requirements; and learn about various roles that CAD software and related software packages play in the manufacturing lifecycle.

This phase of the student’s experiential program challenges the student to identify, investigate and analyze a particular topic (or problem area) in advanced manufacturing (ADMA). A key objective is to apply skills, methods, and knowledge obtained in prior courses with independent thinking and scientific-based research. At the end of Project I, the student (or student team) must complete and submit a written proposal and complete an oral presentation which describes how the project will be fully executed in the follow-on Project II course . The project is undertaken with the close mentorship of a faculty member and may involve a community/partner.

This course is the application of sophisticated computer simulation software for analysis of manufacturing operations, procedures and processes. The course includes an overview of server-based and cloud-computing applications to permit secure data sharing and collaborations in company partnerships. Team and individual projects with utilizing manufacturing simulation and data management applications will be applied and presented.

This course is based on, and not limited to, applied product design and rapid prototyping techniques. An introduction to the application of the cradle-to-grave engineering model will be used to design or redesign industrial solutions. The use of hand tools, 3D printers and equipment will be applied to quickly produce mockups of the developed solution and its presentation.

This course is an overview of the types of non-metallic materials, selection, destructive testing, processing and application of non-metallic materials including and not limited to natural, laminated, plastic, compounds and fluids provided through industrial based solutions. Lab activities, demonstrations and visitations may be utilized to reinforce concepts.

This Digitally Enhanced Manufacturing course offers a comprehensive overview of the design, development, and application of Extended Reality (XR), Digital Twins, and the Internet of Things (IoT) in advanced manufacturing. XR seamlessly integrates virtual representations with the tangible realities of factory operations. Digital Twins are virtual copies of physical buildings, machines, and products, while real-time data from physical systems is recorded and streamed by IoT devices. The course emphasizes how these technologies collaboratively address unique manufacturing challenges as they provide explicit directions, harness real-time data, enhance visualizations, and simulate various manufacturing-related scenarios. The course will explore the evolution of these technologies from historical applications, present-day impact, and developing roles in advanced manufacturing. Practical applications are reinforced through lab activities, with potential on-site experiences to witness these innovations firsthand.

The Industrial Networking and Cybersecurity course is an overview of the development of industrial networks and the methods available to secure the networks. The areas of study in industrial networking include an introduction to computer networks, physical layer cabling with twisted pair and fiber optics and wireless networking and their related hardware’s. The areas of study in industrial cybersecurity include industrial control systems, insecure be inheritance, anatomy of ICS attacks, industrial control system risk assessments, the Purdue Model, the Defense-in-depth model, physical ICS security, ICS network security, ICS computer security, ICS application security, ICS device security and ICS cybersecurity program development.

This solid modeling course utilizes parametric technology, rapid prototyping, and technical sketching, along with the application of product processes and the examination of the components/variables of good design.  CAD solid modeling software will be utilized to create part models and assemblies in the course as well.  Products are designed and analyzed for manufacturability, performance, and potential for profitability for a company. Oral presentations, patent searches and prototype development will be assigned and completed.

Additive manufacturing, an extension of 3D printing processes, allows digital concepts and designs to be realized in the physical world through a variety of processes – each with their own design considerations, advantages, and potential disadvantages. The mechanical processes of 3D printer operations will be investigated, as well as the correct operation, troubleshooting, and maintenance of multiple machines used in additive manufacturing. 3D printer formats used during the course can include Fused Filament Fabrication, Multi-Jet Fabrication, Stereolithography, Powder Bed Fusion, and other emerging technologies in the additive manufacturing realm. Design for 3D printing, software packages for 3D print setup and operations, and cost considerations for additive manufacturing at scale will also be studied.

The Subtractive Manufacturing course is a deep dive into the programming and usage of machining centers and turning centers. The areas of study will include the exploration of CNC machines (functionality and usage of 3-axis & 5-axis miling machines and dual-spindle lathes), CNC programming, various tool types and tool holders, collets, automatic tool changers (ATCs) & turrets, spindles, chucks, fixtures & jigs, chip auger & conveyor systems, and air blast & coolant systems. An introduction to performing tool offsets and probing, as well as setup of coordinate systems will also be covered.

This course is an overview of the broad spectrum of processing approaches involved in “top down”, “bottom up”, and hybrid nanofabrication. The majority of the course details a step by step description of the equipment, facilities processes and process flow used in today’s device and structure fabrication. The student will be introduced to processing and manufacturing concerns such as safety, process control, contamination, yield, and processing interaction. The student will design process flows for micro- and nano-scale systems. The student will learn the similarities and differences in “top down” and “bottom up” equipment and process flows by undertaking hands-on processing. This hands-on overview exposure covers basic nanofabrication processes including deposition, etching, and pattern transfer.

An internship allows the student to put theory into practice. The student applies classroom experiences to the workplace at an off-site placement, where ideas are tested and competencies and skills are developed. Throughout the internship, the student works regularly with a faculty supervisor, the Office of Experiential Programs, and a site supervisor who guides the learning process. The student integrates the collective observations, analyses, and reflections of the experiential team into an internship portfolio that showcases the accomplishments of the experience. The unique portfolio is constructed throughout the internship, and represents the evolutionary and dynamic nature of the learning process.

This course is the conversion of CAD resources into NC machine code for the production of metallic and non-metallic products while integrated with industrial robots. Robotics will be introduced with hands-on programming of industrial robots and include tasks such as pick-and-place, welding, palletizing, assembly, finishing and robot integration into advanced manufacturing facilities.

This course introduces economic analysis from an engineering/manufacturing and personal finance perspective involving cost concepts (i.e., total revenue, profit, etc.) as well as the time value of money concept with equivalence involves present, future, and uniform series cash flows.  The student will learn how to handle various economic scenarios involving the evaluation of both single and mutually exclusive alternative projects/products utilizing present worth, future worth, annual worth, internal rate of return, payback methods, and cost-benefit analysis.  In addition, the student will learn how to interpret job offer letters (i.e., salaries and benefit packages) such that they are prepared to understand their own financial situation/status as they start their career pathways in advanced manufacturing after graduation.

This course is designed for the student who demonstrates an interest in an area of study not offered or who wishes to pursue a discipline in greater depth than possible through existing courses. An independent study counts as an elective and may not be used for accelerated or remedial credit. A learning contract between the student and instructor defines the responsibilities of the parties and specifies the learning objectives and standards for successful completion of the project. A calendar of meeting times and deadlines shall be a part of that contract.

This course furthers the investigation into CNC programming with the usage of several CAD/CAM software platforms. In addition, advanced manufacturing topics such as Geometric Dimensioning and Tolerancing (GD&T), process planning, and Group Technology (GT) are explored. The student takes a deep dive into learning additional programming for multiple industrial robot platforms and explore related robotics & automation topics such as robot speed of movement & precision, selection of end effectors / mechanical grippers, robotics control
systems, and industrial logic.

This course in an introduction to advanced manufacturing techniques including setup, operation, maintenance, troubleshooting and use of current and advanced material processing machines and devices.  A systems engineering approach will be used to analyze complex systems for functional requirement decomposition as well as boundary/interface identification and description. This course will also build to a student-driven proposal for a cohort project in the Advanced Manufacturing II class which demonstrates and reinforces the skills and knowledge acquired from multiple courses and projects throughout the ADMA curriculum.

This course is the application of a combination of digital and analog logic technologies that will lay down a framework from which programmable logic controllers are programmed. The concepts of inputs, outputs, relay logic and ladder logic will be addressed. Industrial robots and automated devices will be introduced, on-line as well as pendent programming to include tasks such as pick and place, finish application and device integration.

This course is the approach of using computers to control the entire production process utilizing closed-loop control processes, based on real-time input from scenarios. The student will totally complete the digitization of manufacturing scenarios into advanced manufacturing scenarios in this course by including the application of CAD/CAM techniques.

This course is the application of sophisticated computer simulation software for a complete analysis of manufacturing operations and processes for a cradle to grave evaluation. Ground up individual and team projects utilizing simulation software, active data collection and storage to refine the manufacturing process that is controlled while providing and implementing efficiencies.

This course is the application of the completed advanced manufacturing suite of resources, which will be applied to solve several different manufacturing issues/projects provided by manufacturing experts. Building upon experiences and skills acquired in prior coursework and projects, class cohorts will use a course project format to pursue a series of manufacturing challenges which demonstrate and showcase a variety of manufacturing techniques, cohort interests, and learning objectives. Results will then be analyzed and presented in a professional academic format suitable for a capstone undergraduate experience.

This course is a follow-up execution of the advanced manufacturing (ADMA) project proposed in the Project I course (ADMA 298). The ideal project has a clear purpose that builds directly upon the learning and research findings that occurred previously within ADMA 298. The final project should demonstrate application of the skills, methods, and knowledge of
the ADMA discipline to solve a problem or answer a question representative of the type to be encountered in the student’s profession. A final written report and an oral presentation are
required of all students (or student teams) at the conclusion of Project II. As with Project I, this is undertaken with the close mentorship of a faculty member and may involve a community partner.

The Data Analytics Certificate Program provides you with the skills and techniques necessary to advance your organization’s ability to perform data driven decision making. The culmination of this program, a Capstone project, offers the opportunity to assess your organization’s data maturity and to develop a plan to transform your organization using data.

Data Sciences Comprehensive Exam

The use of analytics is a common practice in modern business settings. This course introduces the basic concept and practice of analytics and its role in modern businesses. The emphasis is on the tools and techniques of analytics with case studies and examples. Topics include: data querying and reporting; data access and management; data cleansing; statistical programming; data mining introduction; relational databases; and, statistical analysis of databases. The student is introduced to Business Intelligence (BI) and statistical methodology (i.e. clustering, decision tree, etc.) using popular analytics packages such as SAS, Google Analytics, Business Objects, Aginity, and others.

The development and implementation of models to predict outcomes based on input data is becoming an essential skill in modern enterprises. The objective of this course is to teach this skill. The course covers the principles of qualitative as well as quantitative models that can be used for predicting outcome based on input data. The predictions may be definitive, based on the assumptions or estimates based on probabilities. The student explores how to prepare input data, build predictive models, and assess the models by examining the output produced. Topics include: exploratory data analysis, linear regression, multiple linear regression, regression diagnostics, logistics regression, analysis of variance (ANOVA), time series and forecasting, statistical methods for process improvement, classifiers, and non-linear models. General concepts behind how software packages roll up and how they screen data and produce risk scores on topics such as in-patient probability of readmissions.

This course focuses on risk management models and tools and the measurement of risk using statistical and stochastic methods, hedging, and diversification. Examples of this are insurance risk, financial risk, and operational risk. Topics covered include estimating rare events, extreme value analysis, time series estimation of external events, axioms of risk measures, hedging using financial options, credit risk modeling, and various insurance risk models.

The first course in analytics covers the core concepts and applications of analytics. The student is introduced to the main concepts and tools of analytics including descriptive, predictive, and prescriptive analytics. During the course, the student uses a variety of statistical and quantitative methods, computational tools, and predictive models to make data-driven decisions. By the end of the course, the student will apply the concepts to real work projects where, by asking some questions about an issue or situation, use analytical tools to respond to it, and present it to technical and layperson audiences.

This course reviews the fundamental mathematics required to be successful in the analytics program. It is designed to strengthen the mathematical abilities while addressing the requirements for coding/scripting. It presents the mathematical topics as coding/scripting problems. This is intended to further strengthen the ability to develop the subroutines/codes/scripts that are also necessary in an analytics career.

This course covers the basic principles of statistical modeling and inference.  The course focuses on developing and fitting several types of regression models, multilevel models, and everything in between.  Topics included in the class cover, prior predictive simulation, sampling from the posterior, interaction terms, covariance, information criteria, and Markov Chain Monte Carlo estimation.

This course introduces the principles and practices of exploratory data analysis as the foundation for effective analytics. The student will develop practical skills in data retrieval, preparation, transformation, visualization, and quality assessment while working with structured, complex, and big data. The course emphasizes exploratory techniques for discovering patterns, identifying relationships, reducing data complexity, and generating hypotheses that inform subsequent analyses. Through hands-on assignments and applied projects, the student will gain experience using analytical tools to transform raw data into meaningful insights.

This course takes an applied perspective and provides the statistical tools and analytic thinking techniques needed to: formulate a clear hypothesis, determine the most efficient method to obtain required data, determine and apply the proper statistical techniques to the resulting data, and effectively convey the results to both experts and laypersons. The course begins with a review of the descriptive analytics concepts (i.e., sampling, and statistical inferences) introduced in ANLY 500 as well as general conventions regarding experimentation and research. It then progresses to predictive and prescriptive analytics techniques such as regression and forecasting that can be used to predict future events. Later sessions focus on issues related to lack of experimental control (e.g., quasi-experimental design and analysis). The course culminates with a research project in which the student applies the concepts learned to their own research interests.

The visualization and communication of data is a core competency of analytics. This course takes advantage of the rapidly evolving tools and methods used to visualize and communicate data. Key design principles are used to reinforce skills in visual and graphical representation.

This course focuses on risk management models and tools and the measurement of risk using statistical and stochastic methods, hedging, and diversification. Examples of this are insurance risk, financial risk, and operational risk. Topics covered include estimating rare events, extreme value analysis, time series estimation of external events, axioms of risk measures, hedging using financial options, credit risk modeling, and various insurance risk models.

Web technologies based on text and Natural Language Processing (NLP) are becoming the backbone of analytic solutions for understanding language as text language processing has come to play a central role in the multilingual information society. This course provides a highly accessible introduction to the field of text analytics focusing on processing text, information extraction with named entity recognition, text summarization and generation, text classification, chatbots, and deep learning toolkits. The course is a practical guide on how to create end to end systems for production in business, research, and other applied fields using *R* and Python programming languages.

Decision-making in business today requires the use of all resources, particularly information. Analytics supports decision-making quantitatively by applying information received from multiple sources. This course provides the foundation for quantitative decision-making using a rational, coherent approach and includes decision-making principles and how they are applied to business challenges today.

This course introduces the student to machine learning. It provides the student with the cognitive, mathematical and analytical foundation required for machine learning. It also provides the student with a broad overview of machine learning, including topics from data mining, pattern recognition and supervised and unsupervised learning. This course prepares the student for the complex, higher-level topics in Principles and Applications of Deep Learning.

This course considers complex, high-level topics in machine learning. It builds on the foundation provided by Applications of Machine Learning to develop algorithms for supervised and unsupervised machine learning, to study and develop artificial neural networks, to study, develop and evaluate systems for pattern recognition and to consider trade-offs in models, for example, balancing complexity (e.g. volume, variety and velocity of big data) and performance. The focus of this course will be on more advanced techniques such as deep learning and advanced AI.

This course is an introduction to computational language models with a focus on semantic representations. Using methods such as topic models and sentiment analysis, the goal of the course is to show how insights can be derived from text focusing on semantic information. Topics range from basic visualizations of text data to text-based recommendation systems. The topics in this course have real-world applications to gaining insight into human attitudes and behaviors across domains.

This course provides the student with the required knowledge and skills to handle and analyze data using a variety of programming languages as well as a variety of programming tools and methods. Depending on current industry standards, the student will be provided with the opportunity to develop knowledge and skills in programming environments such as R, Octave, and Python. In addition, the student is introduced to current industry standard data analysis packages and tools such as those in Matlab, SAS or SPSS.

This course covers key analytical techniques used in the analysis and forecasting of time series data. Specific topics include the role of forecasting in organizations, exponential smoothing methods, stationary and non-stationary time series, autocorrelation and partial autocorrelation functions, univariate autoregressive integrated moving average (ARIMA) models, seasonal models, Box-Jenkins methodology, regression models with ARIMA errors, transfer function modeling, intervention analysis, and multivariate time series analysis techniques such as Vector Autoregression (VAR), Cointegration and Vector Error Correction Model (VECM).

Attaining quality data on attitudes, behaviors, preferences, and opinions can necessitate the development of experiments, surveys, interviews, focus groups, and other observational methodologies. This course will cover how data analysts in marketing, consumer behavior, healthcare, and other domains can create and implement these research designs to gather primary data for analysis and modeling.

This course explores a topic or collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of analytics.

This program cultivates and supports research partnerships between the student, faculty and other researchers. It provides the student with the opportunity to work on cutting-edge research. Research projects can be at any appropriate and approved level; introductory, participatory or expert. Each project requires an approved proposal, periodic status reports and a final written report with a presentation prepared by the student in collaboration with the research supervisor.

Digital marketing has revolutionized how organizations connect with their target audiences. Coupled with the power of analytics, businesses now possess tools to measure, refine, and optimize their marketing strategies. This course bridges the gap between theoretical marketing principles and data-driven decision-making. The student will explore key digital marketing channels, the latest trends, and the analytics methods essential for crafting impactful campaigns in today’s fast-evolving digital landscape.

This course introduces the fundamental tool in prescriptive analytics. Optimization is the process of selecting values of decision variables that minimize or maximize some quantity of interest. Optimization models have been used extensively in operations and supply chains, finance, marketing, and other disciplines to help managers allocate resources more effectively and make lower cost or more profitable decisions.

This course provides the student with exposure to the theoretical background for advanced analytical topics and methods. Topics include unstructured data/information and big data. For example, the theoretical background required for the integration of data mining and text analytics or text mining are explored. Additional topics could include the implementation and use of data lakes and ontology evaluation.

This course will cover ethical concepts in data analytics and explore ongoing ethical dilemmas in the field including ethical issues related to AI. Specifically, this course will cover the legal, moral, and social implications of the analytic process from data collection to data analysis to data storage to communication and decision-making from insights. The objective of the course is to prepare the student to address the ethical challenges in the evolving field of data science.

This course allows the student to pursue an area of interest that is within the broad scope of analytics. A faculty member will supervise this study.

This course provides a more in depth presentation of the theory behind linear statistical models, segmentation models, and production level modeling. Further emphasis is placed on practical application of these methods when applied to massive data sources and appropriate and accurate reporting of results.

This course will introduce the student to advanced research designs relevant to research in data science. The course will show the student how to determine the data required to answer different types of research questions and implement data collection plans. The course will explore experimental and quasi-experimental designs as well as data mining and dataset creation.

This course provides an in-depth exploration of multivariate data analysis techniques for datasets with multiple dependent variables. It covers traditional methods such as Multivariate Analysis of Variance and Principal Component Analysis, while expanding into advanced topics including machine learning, deep learning, convolutional neural networks (CNNs), graph neural networks (GNNs), large language models, multimodal deep learning, and time series analysis. R packages and Python libraries (e.g., scikit-learn, TensorFlow, PyTorch) will be used extensively for implementation.

This course introduces the power and pitfalls of handling user information in an ethical manner. The student is offered a historical and current perspective and will gain an understanding of their role in assuring the ethical use of data.

This seminar is designed to prepare the student for their comprehensive exams and dissertation work. This seminar will introduce the student to program expectations and milestones. Through the seminar, the student will explore potential dissertation research topics and gain an understanding of the current data science landscape.