Spring 2022 Courses
Our courses provide a world-class, practice-based, data-driven education that translates immediately into expertise you can take to the workplace.
Each academic course bears 3 credits, taught over a 15-week semester at a single weekly session of 3 hours (typically 6pm - 9pm).
- Math 661 - Applied Statistics
- HRM 601 - Organization Behavior
- IS 601 - Web Systems Development
- IS 650 - Data Visualization and Interpretation
- IS 665 - Data Analysis for Information Systems
- IS 684 - Business Process Innovation
- IS 688 - Web Mining
- CS 506 - Foundations of Computer Science
- CS 630 - Operating Systems Design
- CS 631 - Data Management Systems Design
- CS 634 - Data Mining
- CS 636 - Data Analytics with R Programming
- CS 643 - Cloud Computing
- CS 656 - Internet and Higher Layer Programming
- CS 675 - Intro to Machine Learning
- CS 677 - Deep Learning
Math 661 - Applied Statistics (Andrew Pole)
Extracting information from data is the application of statistical description, analysis, and inference. Businesses, industry, academia, 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 choke points, …). 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.
HRM 601 - Organizational Behavior (Julie Ancis)
This course is essential to learning how to facilitate and manage collaboration among diverse teams as you investigate and solve real-world problems. This course focuses on organizational structures and functioning. We will learn how personality, attitudes, and values affect workplace behavior and the impact of leadership style on decision-making. Learn how events and conditions, including technological advances, affect the functioning of an organization. Analyze the role of groups in organizations and increase the probability that people will work well in teams. Tackle real business problems through real-world case studies and applied learning opportunities. Develop self-awareness and awareness of others through personal assessments.
IS 601 - Web Systems Development (Ryan Tolboom)
IS 650 - Data Visualization and Interpretation (Aritra Dasgupta)
This graduate level course introduces students to the world of data analytics from an information systems perspective, focusing on the application of various data analysis techniques in business practices. We cover a wide spectrum of topics ranging from fundamental statistics to database, data warehouse, data visualization, and data mining. Being an introductory course, our approach is “shallow and wide”, emphasizing on giving students a complete view of the data analytics profession, covering as many different sub-areas as time allows while not diving too deep into any one specific domain. The goal is to serve as a “guided tour” for students to gain knowledge about the different sub-areas of data analytics and understanding of which area is a best fit for their personal developments. More in-depth materials and discussion for each sub-area will be provided upon students’ requests. Course topics include the rudiments of probability and random variables, data visualization, data warehousing and OLAP analysis, dashboard, scorecard, data mining algorithms, optimization techniques, DSS and knowledge systems.
IS 684 - Business Process Innovation (David Ullman)
This course adopts a balanced approach to business process innovation (BPI) that includes both incremental improvement and re-engineering. It specifically examines the concept of a service-oriented architecture (SOA) and the use of web services as a way to enable scalable and adaptive business processes. Students will learn how to develop process maps using the Business Process Modeling Notation (BPMN) and design process improvements to achieve efficiency, effectiveness, compliance and agility objectives. The focus of the course is on ways in which information technology can be used to manage, transform and improve business processes.
IS 688 - Web Mining (Cody Buntain)
Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage logs. It has direct applications in e-commerce, Web analytics, information retrieval/filtering, personalization, and recommender systems. Employees knowledgeable about Web mining techniques and their applications are highly sought by major Web companies such as Google, Amazon, Yahoo, MSN, and others who need to understand user behavior and utilize discovered patterns from terabytes of user profile data to design more intelligent applications. The primary focus of this course is on Web usage mining and its applications to business intelligence and biomedical domains. We learn techniques from machine learning, data mining, text mining, and databases to extract useful knowledge from the Web and other unstructured/semistructured, hypertextual, distributed information repositories. This data could be used for site management, automatic personalization, recommendation, and user profiling. Topics covered include crawling, indexing, ranking, and filtering algorithms using text and link analysis, applications to search, classification, tracking, monitoring, and Web intelligence. Programming assignments give hands-on experience. A group project highlights class topics.
CS 506 - Foundations of Computer Science (Jing Li)
This course provides the mathematical and analytical foundations of computer science and its applications to various areas in CS. The course covers the material traditionally known as “discrete mathematics”, with special emphasis on CS applications and analysis of algorithms. Discrete Mathematics is viewed as the pillar or language of CS. One needs to be fluent in it to work in many CS fields including data science, machine learning, and software engineering. The course topics include sets and logic, proof techniques, proof by induction, functions and relations, analysis of algorithms, recursion, recurrence equations, divide-and-conquer design technique, counting methods (permutations and combinations), basic discrete probability, and if time permits, introduction to number theory and a brief introduction to graphs and trees.
CS 630 - Operating Systems Design (Jing Li)
Operating systems are the foundation of computer science and computer systems. A deep understanding of OS allows one to develop programs and applications that are efficient and scalable when running on the underlying hardware. This course prepares you with the knowledge of operating systems concepts and designs that appear in computer systems, via discussing the basic operating system abstractions, mechanisms, and their implementations. In particular, topics we will cover include operating system designs, multiprogramming, timesharing, resource scheduling, synchronization of concurrent processes, management of hierarchical storage, embedded and real-time systems, virtualization, and distributed systems.
CS 631 - Data Management Systems Design (Canan Eren)
Acquaintance with fundamental notions of relational database technology. Mathematical properties and usage of database programming languages. Methods of database design and conceptual modeling. Methods of physical storage for database information. Fundamental notions of concurrency control and recovery in database systems.
CS 634 - Data Mining (Pantelis Monogioudis)
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.
This course will teach how to program in R and how to use R for effective data analysis. Students will learn how to install and configure R necessary for an analytics programming environment and gain basic analytic skills via this high-level analytical language. The course covers fundamental knowledge in R programming. Popular R packages for data science will be introduced as working examples. It will familiarize you with the commonly used analytical techniques in data science and develop the way of data science thinking: learn how to preprocess, explore and interpret real data and learn how to model real problems using computational techniques. The format of the course will include lectures by the instructor, computing labs, class discussion, directed reading and student presentation or project.
CS 643 - Cloud Computing (Cristian Borcea)
This course presents a top-down view of cloud computing, from applications and administration to programming and infrastructure. Its main focus is on parallel programming techniques for cloud computing and large scale distributed systems which form the cloud infrastructure. The topics include: overview of cloud computing, cloud systems, parallel processing in the cloud, distributed storage systems, virtualization, secure distributed computing, and multicore programming.
CS 656 - Internet and Higher Layer Programming (Kumar Mani)
This course introduces the protocols and standards of the TCP/IP suite that govern the functioning of the Internet. The material covered in class is a top-down approach on introduction, discussion, and analysis of protocols from the data-link layer to the application layer. Alternative protocols to the TCP/IP suite and new protocols adopted by this suite are discussed. Numerical examples related to network planning and proocol functioning are analyzed.
CS 675 - Intro to Machine Learning (Ioannis Koutis)
This course is an introduction to machine learning and contains both theory and applications. Students will get exposure to a broad range of machine learning methods and hands-on practice on real data. Topics include Bayesian classification, perceptron, neural networks, logistic regression, support vector machines, decision trees, random forests, boosting, dimensionality reduction, unsupervised learning, regression, and learning new feature spaces. There will be several programming assignments, one-course project, one mid-term, and one final exam.
CS 677 - Deep Learning (Ioannis Koutis)
Deep Learning (DL) is a subfield of Machine Learning that has delivered disruptive technologies, created AI algorithms that outperform humans in various tasks, and paves the way for broader advances in Science. DL consists of a set of specialized techniques that exploit the abundant availability of data and computational power to build models that are composed of multiple processing layers and learn representations of data at multiple levels of abstraction. Only a few years back, the development of DL models required significant expertise, but the introduction of open source DL libraries like TensorFlow and PyTorch has opened the area to scientists and professionals with more diverse backgrounds. The course opens with a review of Artificial Neural Networks that guides you through TensorFlow (or PyTorch) and enables you to build novel ANN architectures. Then it presents the evolution of progressively deeper architectures for Convolutional Neural Networks, that addressed various training difficulties and led to very successful image classification models. In this spatial context, you will also learn about Generative Adversarial Networks that are behind the fascinating 'Deep Fake' images and videos. The course then takes you to the emerging applications of Recurrent Neural Networks in temporal data, including Natural Language Processing. You will also learn how Deep Learning combined forces with Reinforcement Learning to create models that can play games. The course will also touch upon selected topics like the ability of deep networks to generalize, techniques for 'pruning' deep networks in order to make them more computationally efficient, and successful applications of DL methods in the Sciences.