*开始时间: 09/02/2014
持续时间: 2 weeks*

课程主页: https://www.coursera.org/course/bigdataschool

课程评论：没有评论

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.

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.

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.

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.

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.