We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

How To Fail

Formal Metadata

Title
How To Fail
Subtitle
The human factor in data science projects
Title of Series
Number of Parts
27
Author
License
CC Attribution - NonCommercial - NoDerivatives 2.5 Switzerland:
You are free to use, copy, distribute and transmit the work or content in unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
Identifiers
Publisher
Release Date
Language

Content Metadata

Subject Area
Genre
Abstract
Bringing 15 years of experience and over 75 projects in data analytics & AI, Jonas Dischl can tell numerous stories why some projects failed to reach production stage and create additional value. It isn’t the fanciest algorithm that wins – it is the understanding of common pitfalls and human behaviour/ biases on both the data scientists’ and client’s side. This talk explores the lessons learned and dives into how future data scientists can prevent mistakes from reoccurring.