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Data Science Practices: The Good, the Bad, and the Successful

Did you ever look at a code snippet you wrote a few months ago and asked yourself what it was about? Have you ever tried to reproduce results but just couldn't do so because the data had changed in the meantime? Have you ever felt disconnected from stakeholders because your topic is just too complex?

These are just a few scenarios I experienced as a Lead Data Scientist. While spending time with different cross-functional teams at GfK, closely supporting them on a wide range of topics, I collected a valuable set of data science best practices.

In this talk, I will use different examples to show the most interesting ones, helping you overcome the challenges you face when working as a Data Scientist.


  • Experience in the field of Data Science, Software Development, Engineering or similar


In this talk we will:

  • Identify common challenges faced by Data Scientists
  • Understand the importance of best practices in Data Science
  • Discover how to apply best practices to various scenarios



Lisa Maag
Lisa Maag joined GfK in 2019. Her focus as Lead Data Scientist is on the development and implementation of state-of-the-art models for the integration of incoming data into the GfK retail panel. Lisas work focuses on NLP, statistical modeling and machine learning.




HMS Analytical Software




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