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Data Science or Do you believe in magic?

A lot of people want to work in data science and a lot of companies want to have successful data science teams. Nevertheless, we often hear stories about unmet expectations on both employee and employer sides. In this talk we will navigate the world of data science in industry and see where the line between magic and reality lies.

Talk given at Big Data & AI World Frankfurt 2020

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Data Science or Do you believe in magic?

  1. 1. DOYOU BELIEVEIN MAGIC?
  2. 2. Co-organizer of PyLadies Hamburg Board member of Python Software Verband Art: tiyepyep Past: L3S, New Work, Free Now TEREZAIOFCIU HEAD COACH DATA SCIENCE @NEUEFISCHE
  3. 3. HOWITSTARTS Lots of people want to work as data scientist Lots of people are qualified Many jobs are open and companies are looking for all sorts of data scientists TEREZA IOFCIU
  4. 4. HOWITENDS Most of the times there is a huge mismatch between expectations and reality Both for companies and employees TEREZA IOFCIU
  5. 5. ASADATASCIENCEMANAGER.. LESSTHAN5%*OFTHEPEOPLEINMYCOMPANY KNEWWHATIWASDOING TRUE STORY *the number is made up, but it feels accurate
  6. 6. DATASCIENTISTSAREEXPECTING Work on really interesting stories Work on novel topics Focused work Employe cool models Find awesome results Nice data TEREZA IOFCIU
  7. 7. COMPANIESAREEXPECTING Clean data Find the right problems to solve Bring value to the business Build lots of models Build lots of dashboards Prioritise well your work Be fast… TEREZA IOFCIU
  8. 8. FROM ONE PERSON COMPANIESAREEXPECTING Clean data Find the right problems to solve Bring value to the business Build lots of models Build lots of dashboards Prioritise well your work Be fast… TEREZA IOFCIU
  9. 9. DECISION MAKERS THATARE DATA LITERATE
  10. 10. INANIDEALWORLD COMPANIESHIREPEOPLETHEYNEEDNOT PEOPLETHEY“WANT”
  11. 11. BEFOREDATA Data is the new oil You are our first data scientist Data science is magic Immediate results All our problems solved Decisions made based on gut feeling No support from upper mgmt for going data driven Reporting and data flows are still at a manual level TEREZA IOFCIU
  12. 12. BEFOREDATA Assisting with reporting or doing all the reporting Convincing that data needs to be tracked, collected and analysed Most effort will be spent on politics Company needs are: business intelligence and data engineering TEREZA IOFCIU
  13. 13. LINKINGDATA We want to be data driven Data science is still magic Immediate results and on demand improvements Lack of company wide data culture Some decisions based on data insights Solid data infrastructure TEREZA IOFCIU
  14. 14. LINKINGDATA Convincing people that decisions should be backed by data Do analysis and modelling, though many models will not make it live Lots of effort spent on politics and educating others Lessons learned in prioritising of work Company needs: data literacy training TEREZA IOFCIU
  15. 15. DATADRIVEN We publish research Data is at the core of the product Complex problems should be solved by data science Data is in every product/team Data literacy in over 50% of the company Decisions based on data insights Open source, research, publications TEREZA IOFCIU
  16. 16. DATADRIVEN Building data products with the team Advancing the state of the art of research Advocating for data science / your team / product You will be doing data science and more TEREZA IOFCIU
  17. 17. GENE WOLFE, SHADOW & CLAW “THEREISNOMAGIC.THERE ISONLYKNOWLEDGE, MOREORLESSHIDDEN.”
  18. 18. TEREZAIOFCIU @TEREZAIF

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