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Science to Data Science: How and when PhDs should leave for industry

Data Science Retreat

Samstag, 28. Oktober 2017, von 10:00 Uhr bis 13:00 Uhr (MESZ)

Science to Data Science: How and when PhDs should...

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Ticketart Rest Verkaufsende Preis * Gebühr Anzahl
Early Bird 14 Tickets 9S 13M 6,00 € 1,26 €
Regular Ticket 15 Tickets 28.10.2017 12,00 € 1,53 €
* Preisangaben inklusive MwSt.

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At some point, most PhDs wonder whether to continue in academia or leave for a job in industry. More money, time or impact: Motives will vary, and will be equally valid.

In this workshop, we will discuss: How do I switch careers? When do I leave for industry? What mistakes must I avoid? Which destination do I pick? 

Secondly, we look at opportunities in Data Science and Machine Learning, the fastest growing job market for the very highly qualified. 

Target Audience/Workshop Participants  

Any PhD with a numerate background - in e.g. statistics, algebra, econometrics - from any research field that is curious about the opportunities in industry or is wondering whether an academic career is the best or only choice. We understand PhD as -2<+3.

Proof-of-concept

The workshop concept was piloted for the "Max Planck Symposium for Alumni and Early Career Researchers" (Berlin 2017) and is based on hundreds of applications and interviews at Data Science Retreat as well as participants' biographies. 

Wenn Sie Fragen zu "Science to Data Science: How and when PhDs should leave for industry " haben, wenden Sie sich an Data Science Retreat.

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Zeit und Ort


Data Science Retreat
Eislebener Straße 4
10789 Berlin
Deutschland

Samstag, 28. Oktober 2017, von 10:00 Uhr bis 13:00 Uhr (MESZ)


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Data Science Retreat

Dr Chris Armbruster is the Director, Data Science Retreat, Berlin. Earlier engagements include a) research and workshops on academic careers, particularly the rise of the postdoc; b) rolling out digital infrastructure to 80 Max Planck Institutes with >10,000 researchers; c) the largest data-driven study on academic readers and authors ever done. 

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Science to Data Science: How and when PhDs should leave for industry
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