Learn employable
technical skills
on Big Data.

Undergraduate degree in CS, EE, or CpE. The emerging science of complex networks and their applications. Focus will be on algorithms, mathematical theories, and computational methods that analyze complex networks and predict their behavior.

Storage manager, implementation techniques for parallel DBMSs, distributed DBMS architectures, distributed database design, query processing, multidatabase systems.

The course introduces students to parallel computing across the hardware-software stack. Special emphasis is placed on parallel programming using emerging architectures and technologies.

Data analysis; statistical models; estimation; tests or hypotheses; analysis of variance, covariance, and multiple comparisons; regression and nonparametric methods.

Supervised data mining tools including boosting trees, SV machine, regression, and neural network will be covered. The Enterprise Miner (R or Python) will be used.

Unsupervised learning methods such as cluster analysis, association analysis and newly developed tools will be covered. The Enterprise Miner (R or Python) will be used.

A project-focused course that demonstrates mastery of data analytics through development of novel algorithms or innovative application of existing techniques for data mining applications.

Techniques developed by the computer science research community for analyzing social networks and social media datasets.

Computational concepts, principles, modeling and simulation approaches used to analyze complex social and economic phenomena, leveraging the availability of large amounts of data, and elements of complexity theory.

Summarize computational techniques for bridging two fields: machine learning and biomedical science to illustrate successful data mining and knowledge discovery in an interdisciplinary context.

Principles and techniques for interactive data visualization that are useful for analyzing, presenting and exploring information are covered. The emphasis will be on algorithmic aspects of developing interactive visualization. The students will receive practical experience of building interactive visualization systems.

Variable selections, missing value imputation, text, time series, and new data preparation method will be covered. The Enterprise Miner (R or Python) will be used.

Al theory of knowledge representation, “expert systems”, memory organization, problem solving, learning, planning, vision, and natural language.

A study of the different approaches to build programs to understand natural language. The theory of parsing, knowledge representation, memory, and inference will be studied.

Machine learning, the study of algorithms that allow computer programs to learn from experience, is a rapidly changing area. This course will be a deep dive into current topics in machine learning, collected from papers appearing at recent machine learning conferences.