2019 Course on (Big) Data Mining for IUEM Master in Physical Oceanography

This repo is a place holder for the class practice/tuto session and for projects developed by students.

Objectives of the class

• To get familiar with data mining concepts
  eg: clustering, classification, regression, reduction

• To learn about classic methods & specific vocabulary
  eg: KMeans, ANN, features, training sets

• Practice standard analysis workflow
  eg: scale, reduce, fit, predict, cross-validate

• To learn how to handle data mining of large datasets
  eg: xarray/dask-ml, pyspark, tensorflow

Organisation of the class

• Class 1: “Introduction to big data mining for oceanography” (4h00)
  Jan. 21st, D104, 9h00-12h00 / 13h30-14h30

• Class 2:“Identifying patterns: one method in details” (2h00)
  Jan. 21st, D104, 14h30-16h30

• Class 3: Tutorials (6h00)
  Jan. 25th, B014, 9h00-12h00 / 13h30-16h30

• Class 4: Projects (12h00)
  Jan. 11st, D109, 13h30-16h30 (3h00)
  Feb. 13rd, D109, 9h00-12h00 (3h00)
  Feb. 15th, D109, 9h00-12h00 / 13h30-16h30 (6h)

Acknowledgements

Elements of this class were taken from the xarray, Dask and scikit-learn documentations.

Practice about handling methods for big data and binder config folder are mostly based and inspired from some material already published elsewhere (R. Abernathey at pangeo-tutorial-agu-2018)

The amazing machinery allowing us to conduct our projects in a friendly and effective environment arises from the Pangeo community.

This work is licensed under a Creative Commons Attribution 4.0 International License.