Data science workshops

We bring to our data science workshops our extensive experience in solving complex data and analytics problems as well as modern methodologies and frameworks to help you build the infrastructure, skills and processes needed to realise tangible value from data quickly and at scale.

Your advantages

Customised workshops: Our workshops are customised to your individual needs and product requirements. This will quickly empower you to achieve your goals.
In-house or online: For our workshops, we either come to you or use our digital seminar room.
Data science workflow: Our workshops do not end with the basic use of tools and frameworks. We also teach how the various components interact with each other in order to analyse the data quickly and efficiently.

Our workshop services

Python mentoring and 1:1 coaching

After our workshops, we can support you directly in your projects to solve problems fundamentally and achieve your goals faster.

Regular updates

We are constantly analysing the latest techniques, tools, frameworks and platforms for data analysis. We are happy to share our new experiences with you at regular intervals so that you can always stay up to date.

Our workshops

Introduction to Python

— by Veit Schiele — last modified Nov 23, 2024 01:15 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 Nov 23, 2024 01:15 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 Nov 23, 2024 01:15 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.

Efficient testing with Python

— by Veit Schiele — last modified Nov 23, 2024 01:15 PM

Python can be used to test not only Python packages and applications, but also web services, embedded systems and hardware.

Software documentation with Sphinx

— by Veit Schiele — last modified Nov 23, 2024 01:16 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 Nov 23, 2024 01:16 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 Nov 23, 2024 01:16 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.

Numerical calculations with NumPy

— by Veit Schiele — last modified Nov 23, 2024 01:16 PM

NumPy is the abbreviation for Numerical Python. Many Python packages for scientific functions use the NumPy array objects as one of the standard interfaces for data exchange.

Analysing data with pandas

— by Veit Schiele — last modified Nov 23, 2024 01:17 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.

Read, write and provide data with Python

— by Veit Schiele — last modified Nov 23, 2024 01:17 PM

We will access remote file systems and geodata, read various serialisation formats such as CSV and JSON and provide data in suitable databases and interfaces (API).

Cleanse and validate data with Python

— by Veit Schiele — last modified Nov 23, 2024 01:17 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 Nov 23, 2024 01:18 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 Nov 23, 2024 01:18 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 Nov 23, 2024 01:18 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 Nov 23, 2024 01:19 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 Nov 23, 2024 01:19 PM

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.