Project Kickoff Data Science
We put your Data Science project on the road to success!
Especially in data science, the mentality of getting started quickly and looking at the data is predominant. The result is a confusing collection of notebooks in which everything is contained somewhere. However, clean software cannot emerge from this. Therefore it is important, especially at the beginning of data science projects, to lay a solid foundation and establish standards. The central point for this is a structured project basis that allows test automation, documentation and versioning. In our 2-day workshop we would like to lay this foundation together with you.
In an initial meeting we get a first overview of your data science department and the challenges. You will get to know us and our method kit and can set individual priorities.
In a 2-day workshop with your data science department we work out the project basis together. Based on this, we look at topics such as test automation, documentation, versioning and various best practices. All this takes place in a practical way.
Final report with results of the workshop and concrete recommendations for action for your Data Science Team
In a 2-day workshop, you will gain bundled experience from a total of more than 20 data science projects to put your project on the road to success. Together we will work out a project structure in which topics such as documentation, continuous integration and code versioning are seamlessly integrated. With this basis, your Data Science department can continue to work and is also equipped for complex requirements.
Who will accompany you at the project Kickoff
Nicolas Kuhaupt studied mathematics and already during his studies he dealt with Big Data and Data Science. He worked as a Big Data Consultant and subsequently promoted the digitization of the energy system transformation as a Research Data Scientist at Fraunhofer IEE. Today, he works as a freelancer in data science.
Andreas Wygrabek is a freelance Data Science Consultant and experienced trainer in programming and statistical methods. With his project Data-Science-Achitect he offers trainings and full stack services in the field of Data Science. His central analysis tools are R and Python.