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Advanced Database Technologies - Self-paced

About This Course

Overcome your big data challenge. Trends show that digital data outpaces the human ability to process it. Start by understanding exactly what the big data challenge is, and then learn how to conquer it: What do we do with the data? How do we store it? What are the best practices? How do I get actionable information from the data?

At the end of this course you will understand the considerations and design decisions associated with developing an end-to-end big data system. Through the development of the end-to-end design, you will understand your role within the larger data analytics workflow as well as the needs and concerns of members working on other stages of the workflow.

Prerequisites

Success in this course requires familiarity with basic computer terminology, (e.g. memory, storage, network), knowledge of database terminology and linear algebra. Additionally, the course covers a number of applications that rely on a basic understanding of a computational thinking approach to problem solving.

Knowledge of one programming language such as Matlab/Octave, Python, R or Julia is helpful.

Course Staff

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Dr. Vijay Gadepally

Dr. Vijay Gadepally is a Senior Member of the Technical Staff at the Massachusetts Institute of Technology (MIT) Lincoln Laboratory and works closely with the Computer Science and Artificial Intelligence Laboratory (CSAIL). Vijay holds M.Sc. and PhD degrees in Electrical and Computer Engineering from The Ohio State University and a B.Tech degree in Electrical Engineering from the Indian Institute of Technology, Kanpur. In 2011, Vijay received an Outstanding Graduate Student Award at The Ohio State University. In 2016, Vijay received MIT Lincoln Laboratory’s Early Career Technical Achievement Award and in 2017, Vijay was named to AFCEA's inaugural 40 under 40 list. Vijay’s research interests are in high performance computing, machine learning, artificial intelligence and high-performance databases. Vijay is a Senior Member of the IEEE.

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Lauren Milechin

Lauren Milechin is Research Staff at MIT Earth, Atmospheric, and Planetary Sciences. Her work involves big data, database technology, and machine learning applied to problems in diverse domains. Lauren also facilitates researchers on the MIT SuperCloud supercomputing system. Previously, Lauren worked as Associate Technical Staff at the Lincoln Laboratory Supercomputing Center. Ms. Milechin received an MS degree in industrial mathematics from the University of Massachusetts, Lowell, focusing in computer science applications, such as machine learning and algorithms. She holds a BS degree in mathematical sciences from Worcester Polytechnic Institute, where she explored mathematical modeling of disease and of population dynamics.

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Dr. Jeremy Kepner

Dr. Jeremy Kepner is an MIT Lincoln Laboratory Fellow, and Senior Scientist at MIT CSAIL and the MIT Math Department. Prior to joining MIT he was a DoE Computational Science Fellow at Princeton University where he received his Ph.D. in astrophysics. Dr. Kepner leads the supercomputing and big data research efforts at MIT Lincoln Laboratory, which has 3,500 employees and is the largest lab at MIT and accounts for half MIT’s external research funding. His team conducts research in a wide range of computing areas and oversees the operation of a variety supercomputers that service hundreds of users at MIT. Throughout his career the focus of Dr. Kepner’s research has been creating and delivering computing systems that require minimal training to operate, thus allowing scientists to be scientists and engineers to be engineers.

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Dr. Julie Mullen

Dr. Julie Mullen is a member of the technical staff in the MIT Lincoln Laboratory Supercomputing Center (LLSC) where she assists researchers in maximizing their use of high performance computing resources in order to minimize their time to solution. Additionally, Dr. Mullen leads the design and creation of online professional education courseware for the LLSC, where she pursues research in learning analytics for adaptive learning design and the integration of hands-on physical construction and experimentation with MOOC platforms and technologies. Her work has been published in both the scientific computing and educational domains.

Frequently Asked Questions

Is there a textbook?

While a textbook is not required, we recommed "Mathematics of Big Data" by Jeremy Kepner and Hayden Jansen, published by SIAM.

How much time should I expect the course to take?

The course is roughly equivalent to a 3 day workshop.

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