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!

1. design thinking for dummies (data scientists)
tuesday, february 11, 9:00 a.m.
@deanmalmgren
@mstringer
@laurieskelly
2014 february
strata preview

2. data scientists thrive with ambiguity
solve for x
project evolution
x=5+2
@deanmalmgren | bit.ly/design-data

3. data scientists thrive with ambiguity
solve for x
Ax=b
project evolution
x=5+2
@deanmalmgren | bit.ly/design-data

4. 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

5. 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

6. data scientists thrive with ambiguity
solve for x
Ax=b
optimize
f(x)
optimize
“our profitability”
project evolution
x=5+2
optimize
Ax=b
subject to
f(x) > 0
@deanmalmgren | bit.ly/design-data

7. origins of ambiguity
many feasible approaches
@deanmalmgren | bit.ly/design-data

8. origins of ambiguity
unclear problems
identify the best locations to plant new trees
@deanmalmgren | bit.ly/design-data

9. 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

10. 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

11. “design process” is used everywhere
anticipate failure
generate
hypotheses
evaluate
feedback
1-4 week
iterations
build
prototype
@deanmalmgren | bit.ly/design-data

12. “design process” is used everywhere
anticipate failure
human-centered design
lean startup
agile programming
evaluate
feedback
generate
hypotheses
personas, scenarios, use
cases
business/product
requirements
story/user cards
1-4 week
iterations
build
prototype
surveys, interviews, focus groups
split testing, A/B testing
QA; requirements churn
build device prototypes
minimum viable product
write code
@deanmalmgren | bit.ly/design-data

13. 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

14. 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

15. 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

16. solve ambiguous problems
with an iterative approach
http://bit.ly/design-data
!
@deanmalmgren
dean.malmgren@datascopeanalytics.com