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JPL Summer School on Big Data Analytics – Caltech – Coursera

Coursera
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Intermediate Contact Institute
Online Big Data
Online Self Paced 2138
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It is the set of materials from a summer school offered by Caltech and JPL, in the sense used by most scientists: an intensive period of learning of some advanced topics, not on an introductory level.

The school will cover a variety of topics, with a focus on practical computing applications in research. The skills needed for a computational (“big data”) science, not computer science.  It is aimed at an audience of practicing researchers who already have a strong background in computation and data analysis.  The lecturers include computational science and technology experts from Caltech and JPL.

The anticipated schedule of lectures (subject to changes):

Each bullet bellow corresponds to a set of materials that includes approximately 2 hours of video lectures, various links and supplementary materials, plus some on-line, hands-on exercises.

  • Introduction to the school. Software architectures.  Introduction to Machine Learning.
  • Best programming practices. Information retrieval.
  • Introduction to R. Markov Chain Monte Carlo.
  • Statistical resampling and inference.
  • Databases
  • Data visualization.
  • Clustering and classification.
  • Decision trees and random forests.
  • Dimensionality reduction. Closing remarks.

Duration: 2 weeks

20-25 hours per week

Part time/ Full time:

Part time

Fees: – Join for Free

The students should have a solid background in scientific computing and data analysis. Good programming skills in at least one modern computer language (or the ability to quickly learn one) are needed, as well as some knowledge of statistics, and some experience with scientific data analysis.  Background knowledge in computer science is a plus.

Target Audience:

The target audience includes upper-level undergraduate and graduate students, postdocs, or other researchers in science and technology fields.

  • Amy Braver man: – Caltech, Daniel J. Crichton: – Caltech
  • Scott Davidoff: – Caltech, S. George Djorgovski: – Caltech
  • Ciro Donalek: – Caltech, Richard J. Doyle: – Caltech
  • Thomas Fuchs: – Caltech, Matthew Graham: – Caltech
  • Ashish Mahabal: – Caltech, Chris Mattmann: – Caltech
  • David R. Thompson: – Caltech
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