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
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. 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
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4. HOWITENDS
Most of the times there is a huge
mismatch between expectations
and reality
Both for companies and employees
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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…
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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…
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12. 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
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13. 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
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14. 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
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15. 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
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16. 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
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17. 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
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