Data Science Training

In a digitalized world, the effective handling of data is the key to success. It is essential that your company is able to process data quickly, reliably and flexibly. We show you how to ensure data quality and how to combine information from different sources quickly.

Data, I.e. numbers, texts, images and more, can nowadays be flexibly shaped and therefore creating valuable information. This opens new possibilities for companies and institutions, - for example, by enabling them to control processes in real time or develop new business areas. However, one challenge is the evaluation: without a reliable and fast analysis as well as interpretation of the data, its value is lost.

That companies have recognized this value is shown by the demand for data scientists. According to a study by the McKinsey Global Institute, the demand far exceeds the supply of data scientist. But this should not worry you: KÖNIGSWEG has developed a training program for Data Science with Python, with which you can compensate this deficit.

Our program is tailored to your specific needs. We offer you a seminar program based on to the knowledge of your employees. Our seminars are interactive including application-oriented group work and collaboration. Our seminar leaders are mentors, teachers and trainers who quickly clarify any uncertainties and ensure that programming is fun. The added value for your company stays always in the focus - we will gladly adapt the exercises to your use cases.

Trainings are available as face-to-face at your site or remotely in our digital training room. There is no difference between the two options, neither in terms of content nor procedure. All training documents are also available to the participants after the training. A standard course lasts three days and can be held in German or English.


Python – Basic knowledge for Data Science

  • Data types
  • Functions
  • Classes
  • Program flow control

  • Jupyter Ecosystem
  • Jupyterlab Basics
  • Good Coding Practices in Jupyter Notebooks
  • Sharing use of Jupyter in the team

  • Reading and writing of data in CSV, Excel and other common file exchange formats
  • Organize data in DataSeries & DataFrames
  • Prepare data
  • Ensure data quality
  • Selecting data
  • Aggregate data
  • Merge data
  • Using built-in statistics functions
  • Data visualization via pandas
  • Making the best use of indexes: TimeSeries and MultiIndex
  • Integrate SciKit-Learn and create forecasts

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We thank you for your interest in targeted innovation and transformation and look forward to continuing to accompany you on your journey into the digitized future.

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