Synopsis: This course leverages the foundations in statistics and modeling to teach applied concepts in machine learning. Participants will learn various classes of machine learning and modeling techniques, and gain an in-depth understanding how to select appropriate techniques for various data science tasks. Topics cover a spectrum from simple Bayesian modeling to more advanced algorithms such as support vector machines, decision trees/forests, and neural networks. Students learn to incorporate machine learning workflows into data-intensive analytical processes.

3 – Credit Hour

Synopsis: 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 cloud services, and others.

3 – Credit Hour

Synopsis: 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 and various other parallel computing architectures.  Students will research emerging parallel and scalable architectures for data analytics.

3 – Credit Hour