Data scientists often face ambiguous challenges and, as a group, should use and make use of the design process to address these challenges. These slides briefly make the case for using the design process. Interested in more, reach out!
Text of Strata preview 2014: Design thinking for dummies (data scientists)
design thinking for dummies (data scientists) tuesday, february 11, 9:00 a.m. @deanmalmgren @mstringer @laurieskelly 2014 february strata preview
data scientists thrive with ambiguity solve for x project evolution x=5+2 @deanmalmgren | bit.ly/design-data
data scientists thrive with ambiguity solve for x Ax=b project evolution x=5+2 @deanmalmgren | bit.ly/design-data
data scientists thrive with ambiguity solve for x Ax=b project evolution x=5+2 optimize Ax=b subject to f(x) > 0 @deanmalmgren | bit.ly/design-data
data scientists thrive with ambiguity solve for x Ax=b optimize f(x) project evolution x=5+2 optimize Ax=b subject to f(x) > 0 @deanmalmgren | bit.ly/design-data
data scientists thrive with ambiguity solve for x Ax=b optimize f(x) optimize our protability project evolution x=5+2 optimize Ax=b subject to f(x) > 0 @deanmalmgren | bit.ly/design-data
origins of ambiguity many feasible approaches @deanmalmgren | bit.ly/design-data
origins of ambiguity unclear problems identify the best locations to plant new trees @deanmalmgren | bit.ly/design-data
origins of ambiguity unclear problems identify the best locations to plant new trees how many? what kinds of trees? move old trees? replace old trees? @deanmalmgren | bit.ly/design-data
origins of ambiguity unclear problems identify the best locations to plant new trees aesthetically pleasing? maximize growth? increase folliage? offset CO2 emissions? how many? what kinds of trees? move old trees? replace old trees? @deanmalmgren | bit.ly/design-data
design process is used everywhere anticipate failure generate hypotheses evaluate feedback 1-4 week iterations build prototype @deanmalmgren | bit.ly/design-data
design and data science challenges in practice problem lost in translation evaluate feedback generate hypotheses 1-4 week iterations build prototype @deanmalmgren | bit.ly/design-data
design and data science challenges in practice problem lost in translation generate hypotheses takes a long time to collect data, analyze, and build visualization evaluate feedback 1-4 week iterations build prototype @deanmalmgren | bit.ly/design-data
design and data science challenges in practice problem lost in translation generate hypotheses takes a long time to collect data, analyze, and build visualization evaluate feedback 1-4 week iterations build prototype proof is in the pudding @deanmalmgren | bit.ly/design-data
solve ambiguous problems with an iterative approach http://bit.ly/design-data ! @deanmalmgren dean.malmgren@datascopeanalytics.com
