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Python for Data Science¶

This is a tutorial on Data Science with Python. This immediately raises the question: What is Data Science? The term has become ubiquitous, but there is no single definition. Some even consider the term superfluous, because what science does not have to do with data? Nevertheless, it seems to me that data science is more than just hype: scientific data has become increasingly voluminous and often can no longer be adequately tapped with conventional mathematical and statistical methods alone – additional hacking skills are needed. However, it is not a new field of knowledge that you need to learn, but a set of skills that you can apply in your field. Whether you are analysing astronomical objects, analysing machines, forecasting stock prices or working with data in other fields, the goal of this tutorial is to enable you to solve tasks programmatically in your field.

This tutorial is not intended to be an introduction to Python or programming in general; for that there is the Python basics tutorial. Instead, it is intended to show the Python data science stack – libraries such as IPython, NumPy, pandas, and related tools – so that you can subsequently effectively analyse your data. We also offer the Jupyter Tutorial and the PyViz Tutorial as well as the instructions for data visualisation from the cusy Design System.

All tutorials serve as seminar documents for our harmonised training courses:

Duration

Topic

3 days

Introduction to Python

2 days

Advanced Python

2 days

Design patterns in Python

2 days

Efficient testing with Python

1 day

Software documentation with Sphinx

2 days

Technical writing

3 days

Jupyter notebooks for efficient data science workflows

2 days

Numerical calculations with NumPy

2 days

Analysing data with pandas

3 days

Read, write and provide data with Python

2 days

Cleanse and validate data with Python

5 days

Visualising data with Python

1 day

Designing data visualisations

2 days

Create dashboards

3 days

Versioned and reproducible storage of code and data

Subscription of 2 hours per quarter

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