Summer 2022 Courses

Each academic course bears 3 credits, taught over a 15-week semester at a single weekly session of 3 hours (typically 6 pm - 9 pm)

• MATH 661 - Applied Statistics Andrew Pole

• IS 601 - Web Systems Development Ryan Tolboom

• IS 678 - IT Service Management David Ullman

• CS 634 - Data Mining Pantelis Monogioudis

• CS 644 - Intro to Big Data David Bader

• CS670 -  Artificial Intelligence Pantelis Monogioudis

Extracting information from data is the application of statistical description, analysis, and inference. Businesses, industry, academia, and governments face these questions every day, looking for opportunities and seeking solutions for emergent problems: How much bandwidth can that streaming compression technology free up? Will we gain customers if we improve the audio quality? How should we examine performance data from four different designs of a prosthetic limb? What is the reliability of different component manufacturing processes? How do we allocate funding to maximize relief impact? In this course, you will learn basic and advanced mathematical and statistical tools and techniques for extracting information from your data, big or small, and to make quantitatively justified statements about general populations of concern (customers, patients, supply chain chokepoints, …). You will also use real-world data from many settings in the current leading data analytic environments R and Python to learn how to address these business, research, and social needs. 

This course trains in the development of web-based systems using Python and the Django web framework. You will learn to program with Python and Javascript, currently two of the three most in-demand programming languages, and learn to develop a web based system through an intensive, hands-on project that requires application of real-world problem-solving skills. You will also learn to utilize the Django web framework, one of the ten most popular web frameworks and currently in use by Instagram and Pinterest. Model-View-Template design, the Django templating language, and object relational mapping for database access will be covered in this course. At the conclusion of the course, you should be able to create a bespoke web interface for a standard internal business application. 

Prerequisites: IS 663 or CS 673 This course introduces the Information Technology Infrastructure Library (ITIL) fundamentals of the service management life cycle-service strategy, service design, service transition, service operation, and continual service improvement. ITIL provides a comprehensive, consistent, and coherent framework of best practices for IT Service Management (ITSM), which promotes a quality approach for achieving business effectiveness and efficiency in the use of information systems. This course presents the basic terminology and an overview of the functions and processes for each of the life cycle phases as they apply to IT Management. Although ITIL is originally presented as an approach for designing IT processes, we can expand this view and apply it to the design of other business services. Possible semester-long contexts are the processes of an educational services provider or health care services provider.

The process of extracting signals from massive amounts of noisy data is a multi-step endeavor that involves, data analysis, visualization, learning algorithms, model analysis and verification. In this course you will learn not only what methods to use at each one of these stages but also, how to design and architect end-to-end pipelines that deliver on your technical and, ultimately, business objectives. Using real-world use cases from Zillow, Uber and other technology companies, you will use Python and cutting-edge open source machine learning libraries and tools to mine massive, publicly available repositories of structured (tabular) as well as unstructured (e.g. natural language) data. The skills acquired in this course are essential for aspiring data analysts or scientists across all data-driven industries. 

Data science is a rapidly emerging discipline at the intersection of machine learning, algorithms, and statistics. Solving real-world problems in domains such as financial services, cybersecurity, and health informatics requires the generation, storage, management, and analytics, on massive data sets. This course will take you on a journey of developing high performance data analytics for massive data sets using accelerated computer architectures, state-of-the-art software frameworks, and real-world case studies. In-depth topics will focus on mathematical modeling, scalable algorithms, and high-performance computing, for fast and practical solutions. Together we will explore real-world data modeling and prediction from the Data Science Design Manual and learn How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. 

This course introduces concepts, approaches and techniques of artificial intelligence, and focuses on materials that are fundamental and have a broad scope of applications.  Topics include Problem Solving, Intelligent Agents, Logical Agents, Propositional Logic Knowledge Representation and Reasoning, First-Order Logic Knowledge Representation and Reasoning, Uncertain Knowledge Representation and Reasoning, Quantifying Uncertainty, Probabilistic Reasoning, Learning, Statistical Learning Theory, Bayesian Learning, Decision Tree learning, Neural Networks, Deep Learning, Reinforcement Learning, Genetic Algorithms, Perception, Pictorial Knowledge Representation, Search, Action, and Robotics.