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assignment Level : Introductive
chat_bubble_outline Language : English
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Key Information

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timer 40 hours in total

About the content

This is an intensive, advanced summer school (in the sense used by scientists) in some of the methods of computational, data-intensive science. It covers a variety of topics from applied computer science and engineering, and statistics, and it requires a strong background in computing, statistics, and data-intensive research.

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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.
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Instructors

  • Amy Braverman - Jet Propulsion Laboratory (JPL)
  • Thomas Fuchs - Jet Propulsion Laboratory (JPL)
  • Julian Bunn - Center for Data-Driven Discovery
  • Chris Mattmann - Jet Propulsion Laboratory (JPL)
  • Santiago Lombeyda - Center for Data-Driven Discovery
  • Ciro Donalek
  • Daniel Crichton - Jet Propulsion Laboratory (JPL)
  • Richard Doyle - Jet Propulsion Laboratory (JPL)
  • Ashish Mahabal
  • David Thompson - Jet Propulsion Laboratory (JPL)
  • Scott Davidoff - Jet Propulsion Laboratory (JPL)
  • S. Djorgovski - Astronomy
  • Matthew Graham
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Platform

Coursera

Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California. 

Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.

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