Paul Rodriguez




Paul Rodriguez is a Research Programmer / Analyst at the San Diego Supercomputer Center on UCSD's main campus. He spent several years doing research in neural network modeling, dynamical systems simulations, time series analysis, and statistical methods for analysis and predictions in fMRI data. He has more recently worked in data mining for health care fraud identification, and optimization of data intensive network flow models. Paul received his PhD in Cognitive Science at University of California, San Diego (UCSD) in 1999.

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Feb 20th 2017

This course is for novice programmers or business people who'd like to understand the core tools used to wrangle and analyze big data. With no prior experience, you'll have the opportunity to walk through hands-on examples with Hadoop and Spark frameworks, two of the most common in the industry. You will be comfortable explaining the specific components and basic processes of the Hadoop architecture, software stack, and execution environment. In the assignments you will be guided in how data scientists apply the important concepts and techniques, such as Map-Reduce that are used to solve fundamental problems in big data. You'll feel empowered to have conversations about big data and the data analysis processes.

Average: 6.5 (8 votes)
Feb 13th 2017

Want to learn the basics of large-scale data processing? Need to make predictive models but don’t know the right tools? This course will introduce you to open source tools you can use for parallel, distributed and scalable machine learning.

Average: 8 (7 votes)