An intermediate statistics class designed to build the mathematical foundation for  students dealing with Big Data phenomena. Topics include discussions of probability, data sampling, data summarization, sampling distributions, statistical inference, statistical pattern analysis, hypothesis testing, regression, and nonparametric inference over multidimensional data collections. Students will engage in Big Data projects using various publicly available data sets and leveraging modern Data Science tools, techniques, and cyberinfrastructure. This is a 3-credit hour course.

This course covers core concepts for heterogeneous data management, including relational databases, NoSQL databases, and other data storage systems. Advanced database topics are covered related to relational modeling, normalization, and optimization. The data management lifecycle for various data types and storage systems is investigated by participants, allowing them to learning to balance the data characteristics and the analytical needs when constructing and exploiting database solutions. Additionally, predictive modeling and machine learning topics are linked into this course to provide thematic linkages to data science. This is a 3-credit hour  course.

This course builds upon previous database concepts to focus on interrogation of vast structured and unstructured data stores. Various software ecosystems are explored for data mining and information extraction from heterogeneous data sources. Learning activities focus on scripting interactions with data stores for acquisition of data, manipulation, and refinement, and retrospective data modeling. Additional learning activities focus on the modeling, management, and exploitation of streaming data.  Various Big Data software ecosystems are explored. This is  a 3-credit hour course.

This course provides an overview of state-of-the-art topics in Big Data Security, looking at data collection (smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting structured data from unstructured data, systems issues (exploiting multicore, security). Securing sensitive data, personal data and behavioral data while ensuring a respect for privacy will be a focus point in the course. This is a 3-credit hour class.

Covers the Fundamental concepts of current visualization concepts and technologies. Unlike many data visualization courses, this one focuses on principles of visualization design and the grammar of graphics. These principles are then implemented in popular contemporary visualization technologies. Students will develop an advanced knowledge of the appropriate selection, modeling, and evaluation of data visualizations. This is a 3-credit hour course.

Introduces the ethics related to Big Data in industry, business, academia, and research settings. Students will learn the social, ethical, legal and policy issues that underpin the big data phenomenon. Discussions and case studies will help guard against the repetition of known mistakes and inadequate preparation. The course content will follow the guidelines to be developed by the Council for Big Data, Ethics, and Society.   This is a 1-credit hour course.

Using a case-study approach, students will engage in discussions on a variety of big data topics relevant to their emphasis area and the realm of Big Data. This course will help students generate ideas and prepare them for the Big Data Capstone. Course work will be performed in small teams, mentored by faculty and/or industry advisors. Teams will research, cultivate, curate, and leverage large data sets. Students will gain hands-on experience applying relevant data science and analytical technology and techniques to gain insight and information from these real-world data sets. This is a 3-credit hour course.

This course provides an opportunity for participants to tackle a real-world data science project, delivered as a problem-based exercise. Participants will perform the full data science lifecycle methodology on a relevant challenge problem as final learning activity that draws upon all the foundational data science concepts and technologies, as well as specialized technologies and concepts relative to a particular concentration area.  This is a 3-credit hour course.

The course provides a complete overview of the domain knowledge, technical skills and applications of genomics commonly used in industry. Students will review biological and technological foundations for genomics, and explore BLAST, HMM and other biological database search methods. Biological programming tools will be introduced to facilitate assembly and annotation in genomics. This sets students up to use the GATK library for variant analysis, explore comparative genomics, expression analysis and methyl-seq analysis in the latter part of the course.

This course is intended to review theoretical, conceptual, and analytic issues associated with network perspectives on communicating and organizing. The course applies the science of social network analysis and computational linguistics as computing strategies for making sense of electronic communications for, by and between people. The curriculum builds across a wide array of disciplines in order to take an in-depth look at theories, methods, and tools to examine the structure and dynamics of networks.

In Process of Development

An intermediate data wrangling and analysis class designed to provide students with an in-depth overview of collecting and analyzing Twitter data. Computational topics include composing, sending, and receiving Hypertext Transfer Protocol (HTTP) messages. Data wrangling topics include parsing json files, navigating recursively nested structures, and processing textual data. Analysis methods include machine learning, network analysis, topic modeling, time series, etc.

This course introduces students to cluster and cloud computing big data ecosystems. Topics include a survey of cloud computing platforms, architectures, and use-cases. Students will examine scaling data science techniques and algorithms using a variety of cluster and cloud paradigms, such as those built atop Hadoop (Map-Reduce) concepts, and others.

This course will provide in-depth treatment of the evolution of high performance, parallel computing architectures and how these architectures and computational ecosystems support data science. We will cover topics such as: parallel algorithms for numerical processing, parallel data search, and other parallel computing algorithms which facilitate advanced analytics. To reinforce lecture topics, learning activities will be completed using parallel computing techniques for modern multicore and multi-node systems. Parallel algorithms will be investigated, selected, and then developed for various scientific data analytics problems. Programming projects will be completed using Python and R, leveraging various parallel and distributed computing infrastructure such as AWS Elastic Map Reduce and Google Big Query. Students will research emerging parallel and scalable architectures for data analytics.

Focuses on animated visualization design that builds on Infographic and Interactive Visualization Design techniques. Unlike many data visualization courses, this one focuses building animations and highly interactive representations of data. These principles are then implemented in popular contemporary visualization technologies. Students will develop an advanced knowledge of the appropriate selection, modeling, and evaluation of data visualizations.