Block course for teaching tools (Python) and methods (Statistics, Machine Learning) for Data Science.
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README.md

README.md

data-science-course

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

A block course about Data Science in Python for people who know basic programming in Python, R or Matlab meant to be taught in 5 days à 8h.

This course has been taught at the Georg August University Göttingen in April 2017 and February 2018 to PhD students of the GGNB (http://www.uni-goettingen.de/en/sh/56640.html) and master students of the physics department. It covers topics ranging from tools for open science applications to methods from statistics and machine learning.

The course materials have initially been created by Jana Lasser and Debsankha Manik and were later extended by Chamkor Singh, Jeremy Vachier and Christian Holme.

The course material covers

  • Day 1
    • Introduction to Git
    • Introduction to Jupyter Notebooks
    • Basic plotting with Matplotlib
    • Introduction to NumPy
  • Day 2
    • Data cleaning
    • Introduction to Pandas
    • Advanced plotting with Seaborn
    • Handling and plotting big data with HDF5 and Bokeh
  • Day 3
    • Introduction to SciPy
    • Linear regression
    • Clustering
  • Day 4
    • Support Vector Machines
    • Classification
    • Neural nets
  • Day 5
    • More neural nets
    • Open Science