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The Caltech-JPL Summer School on Big Data Analytics

California Institute of Technology via Coursera

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Overview

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This is not a class as it is commonly understood; 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.  The specific focus will be on applications in astrophysics, earth science (e.g., climate science) and other areas of space science, but with an emphasis on the general tools, methods, and skills that would apply across other domains as well.  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.

Students can evaluate their own progress, but there will be no tests, exams, and no formal credit or certificates will be offered.

Syllabus

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.

1. Introduction to the school.  Software architectures.  Introduction to Machine Learning.

2. Best programming practices.  Information retrieval.

3. Introduction to R.  Markov Chain Monte Carlo.

4. Statistical resampling and inference.

5. Databases.

6. Data visualization.

7. Clustering and classification.

8. Decision trees and random forests.

9. Dimensionality reduction.  Closing remarks.

Taught by

Amy Braverman, Daniel Crichton, Scott Davidoff, S. Djorgovski, Ciro Donalek, Richard Doyle, Thomas Fuchs, Matthew Graham, Ashish Mahabal, Chris Mattmann and David Thompson

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