Fall 2021 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).


​​Download the Fall 2021 Course Sheet

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. 

 

IS 601 - Web Systems Development (Ryan Tolboom)

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.

 

IS 657 - Spatio-Temporal Urban Analytics (Mark Cartwright)

This is a project-based course in which we will focus on visualization of high-dimensional data collected about urban populations. Students will have an option of working on an individual project or a group project where they will focus on visual communication of data-driven insights. To get the most out of this course, it is necessary that students have taken the IS 650 course and are familiar with basic data visualization techniques.

Students will get to choose from a number of project options and the project goal can be tailored based on a match between their preferences and the course objectives. They will work closely with the instructor and his team of PhD students and the schedule (of meetings/deliverables) can be mutually agreed upon before the start of the course.

Students will be evaluated based on the progress they make towards meeting the project deliverables. The focus will not solely be on a final product but on the lessons they learn while committing and rectifying mistakes along the way.

The project options include, but are not limited to, the following topics: data discovery, where the goal is to semantically integrate openly available spatio-temporal data (e.g., NYC open data) for driving decision-making about various facets, like health, education, economy, etc., of an urban population; educational data mining, where openly available college and university datasets (across states and multiple years) will be combined with students’ economic and demographic data for identifying potential bias and lack fairness in admissions’ criteria; studying information consumption, where social media data (e.g., Twitter) combined with sentiment analysis can be used to analyze the nature of content that propagates through social networks in times of a global pandemic/”infodemic” and reflect on the need for each person be their own moderator for preserving their mental health.

All of the aforementioned projects comprise an element of research. They require students to exercise their critical thinking skills and will have ample opportunity for them to come up with innovative approaches/solutions, and in turn, contribute directly to the research projects.

 

IS 665 - Data Analysis for Information Systems (Lin Lin)

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.

 

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.

 

CS 636 - Data Analytics with R Programming (Yao Shen)

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 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 protocol functioning are analyzed.

 

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.