Python training for Data Scientists and Research Software Engineers

About these courses

The courses are aimed at data and business analysts, scientists and research software engineers.

About Python

Python is not a programming language specifically designed for numerical and scientific computing like Julia or R. Rather, it emerged in the 1980s as a teaching language designed to bridge the gap between shell and C.

It was not until the 2000s that Python positioned itself as an alternative to MATLAB as matplotlib for data visualisation, SciPy for scientific computing, iPython as a shell and NumPy for arrays emerged.

In the 2010s, Python evolved more and more into an alternative for R:

«At the time, I had a distinct set of requirements that were not well addressed by any single tool at my disposal:

  • Data structures with labeled axes …
  • Integrated time series functionality
  • Arithmetic operations and reductions
  • Flexible handling of missing data
  • Merge and other relational operations …

I wanted to be able to do all of these things in one place, preferably in a language well suited to general-purpose software development.»

– Wes McKinney, author of pandas, in Python for Data Analysis

In addition to pandas for labelled data, scikit-learn for machine learning and Jupyter notebooks for an interactive, web-based development environment were created.

Python is an easy-to-learn general-purpose language, and that is probably its greatest strength for Data Science.

Requirements

First experience with Python is helpful, but not required. In our Python for DataScience seminar, you will receive an introduction to the general programming concepts of Python such as object orientation, dynamic typing, variables, loops and functions.

Training types

Open training
We have been offering seminars for up to 8 people for many years.
In-house training
This is an inexpensive way for you to have multiple employees have the same subject trained in your classrooms.
Individual training
The demand-oriented training, in which we offer you quick and individual the desired knowledge on your desired date.

Voices about our courses

„The trainer addressed all open questions and adapted the training perfectly to our level of knowledge!“

– Daniel Kissel, Hays AG

„The data visualization training with cusy helped me improve greatly on robust Data Science fundamentals, leading to a prolonged understanding of the subject matter and replicable and robust results going forward.“

– Dennis K., Civil Servant

„The structure of the training, based on online documentation (PyViz tutorial & Jupyter tutorial), not only explained tasks and their solutions, but also taught a method for retrieving the documentation and reusing it in one‘s own company.“

– Johannes Zieher, Engineering Team Leader, Women’s Health Ultrasound GE Healthcare

„Good understanding of the material, good balance between lecture and practical exercises. Questions were answered very well.“

– Willy Macke, Process Engineering IT, Bizlink Special Cables Germany GmbH

Python mentoring and 1:1 coaching

Work directly with the cusy Python team in one-to-one sessions to learn new skills.

If you need extra help with your Python project, or are stuck troubleshooting complex issues, we can help. Whether in a single session or long-term support, work with us to solve your problems and achieve your goals:

In our individual sessions, we review and improve your code with you in pair programming.

In mentoring, we adapt our curriculum and tailor sessions to your individual needs so that you achieve your goals as quickly as possible.

Beyond Python, we can also provide expert help in JavaScript, HTML/CSS, Vue and Git, as well as web development, software design and architecture, and automation.

Further information

Do you have a question that is not answered here? Contact us or call us: Tel: +49 30 22430082.

Introduction to Python

by Veit Schiele — last modified Jul 21, 2024 09:15 PM
Python has become very widespread and one of the reasons is probably that it runs on many different platforms, from IoT devices to common operating systems and supercomputers. It can be used to develop applications and libraries. There are already countless software libraries that make your work easier.

Advanced Python

by Veit Schiele — last modified Jul 21, 2024 09:39 PM
The Python programming language is easy to learn and makes it possible to solve problems quickly. But it also offers advanced solutions that can make creating an app or a software library much easier.

Design patterns in Python

by Veit Schiele — last modified Jul 21, 2024 11:08 PM
Design patterns are proven solution templates for recurring problems in software architecture and development. There are Python-specific design patterns such as global object, prebound method and sentinel object patterns. These design patterns differ significantly from the classic design patterns. Finally, the SOLID principles will help you to better maintain and extend your software in the future.

Software documentation with Sphinx

by Veit Schiele — last modified Jul 21, 2024 11:10 PM
In order for your software package to be useful, documentation is required that describes how your software can be installed, operated, used and improved. For extensive documentation you can use Sphinx, a documentation tool that converts reStructuredText into HTML or PDF, EPub and man pages.

Technical writing

by Veit Schiele — last modified Jul 28, 2024 04:48 PM
Technical writing conveys complex information clearly and precisely to the respective user. Most technical texts are based on simplified grammar supported by easy-to-understand visual communication.

Jupyter notebooks for efficient data science workflows

by Veit Schiele — last modified Jul 25, 2024 12:16 PM
Jupyter notebooks are ideal for exploratory data analysis. They have therefore become the de facto standard for exploratory data analysis and rapid prototyping. But that’s not all: the range of functions continues to grow thanks to countless extensions and opens up further utilisation options.

Analysing data with pandas

by Veit Schiele — last modified Jul 26, 2024 07:09 PM
pandas is a Python library for data analysis that has become very popular in recent years. More specifically, pandas is an in-memory analytics tool that offers SQL-like constructs as well as statistical and analytical tools. It is increasingly replacing Excel and Power BI, processes CSV and JSON files and prepares data for machine learning.

Cleanse and validate data with Python

by Veit Schiele — last modified Jul 27, 2024 04:29 PM
There are many different Python libraries that make it much easier to clean and validate data. We will use these libraries in practical examples to recognise and clean up problems in the data.

Visualising data with Python

by Veit Schiele — last modified Jul 27, 2024 04:27 PM
There are many Python libraries for visualising data, each with a different focus. This course will give you an overview of the various libraries and show you how to use these libraries using practical examples.

Designing data visualisations

by Veit Schiele — last modified Jul 27, 2024 04:23 PM
The basic design principles are indispensable for both explorative and explanatory data visualisation. Visual hierarchies can be used to focus on specific statements so that your data can be used for coherent storytelling for your target group.

Create dashboards

by Veit Schiele — last modified Jul 28, 2024 04:25 PM
Dashboards present the most important information for achieving one or more goals. They consolidate and organise the information so that it can be viewed at a glance. This can be access figures, response times and error messages for a web application or KPIs for a business dashboard.

Versioned and reproducible storage of code and data

by Veit Schiele — last modified Aug 04, 2024 03:19 PM
‘Single occurrences that cannot be reproduced are of no significance to science’ wrote Karl Popper in 1935 in The Logic of Research. This has not changed to this day. What is new is that research data and research software must be managed sensibly. To do this, you must not silently rely on certain resources and development environments. Changes to your data and software can be tracked and team collaboration can be facilitated.

News from Python for data science

by Veit Schiele — last modified Jul 28, 2024 11:39 AM
The Python for Data Science stack should be continuously adapted to current conditions and benefit from better data science workflows. In this workshop, we will share the latest developments and our current best practices with you.