DASC - Data Science (DASC)

This course is a graduate tour of techniques for processing big data that aims at future Data Scientists. It covers algorithms and software frameworks that are used for automating data analysis of big data. The course topics include Python for data science, big data stack, data analytics architecture, MapReduce, Hadoop and case studies such as recommendation engines. The course teaches practical skills in implementing big data analytics using industry-standard software, such as Python and MapReduce, and cloud computing services.

Networks have long served as a model of interactions and behaviors of complex systems. This course will discuss what a network is, distinguish among the various types of networks, develop criteria to determine which networks are "better" than the others, and determine which algorithms best aid in this analysis. Software to aid in the analysis, such as Mathematica, will be used.

The data engineering course offers a blend of theory, case studies, and hands-on experiences. At the end of the course, students will be able to build a data warehouse and data lake, automate date pipelines, work with massive datasets, and understand the concepts of a major platform for cloud computing, such a Amazon Wed Services (AWS), its terminologies, and benefits.

The special topics course exposes the students to emerging important topics in Data Science and Engineering.

This course covers areas of current interest in Data Science.

This course introduces the fundamental skill set needed for the engineering design and implementation of automated data science and machine learning software systems. As part of the course, the roles of engineering, software, object-oriented, abstract data type, and program design are discussed, emphasizing systematic design skills. Furthermore, the course includes designing software and developing automated machine learning projects in an industrial Continuous Integration and Continuous Delivery (CI/CD) environment. Hands-on work is carried out in the departmental lab and in the cloud. This is the first course in a sequence of two courses on Data Science Engineering projects.

Students design and implement an automated data science and machine learning project in an industrial Continuous Integration and Continuous Delivery (CI/CD) environment. The course requires a group project. This is the second course in a sequence of two courses on Data Science Engineering projects.

Students participate as a research team to solve a data science problem. The work is documented by a M.S. thesis. The thesis shall demonstrate that the students are able to solve a complex data science problem. This course represents the first phase towards completing the M.S. thesis consisting of practical research, solution of a data science challenge and a preliminary report document the work. The subsequent course M.S. Thesis represents the second phase completing the M.S. thesis.