The document outlines a presentation on data science product design at the Concur Data Science Summit 2016, highlighting the use of Apache Spark and GraphLab for enhancing data processing and addressing various challenges in the field. It discusses fostering innovation while maintaining quality service for business clients, emphasizing design approaches for knowledge gaps, IoT networks, and fairness in data science. Additionally, it promotes open-source initiatives and job opportunities at Concur Labs and affiliated organizations.

1. DESIGN FOR X
exploring data science product design with apache spark + graphlab {create}
@amcasari @Concur
data science summit 2016, san francisco
nasa

2. data science via random walks
senior product mgr +
data scientist
@ Concur Labs
control systems
engineering +
robotics + legos
officer in USN
operations research
analyst
wandering dirtbag +
conservation volunteer
EE +
applied math
+ complex systems
underwater robotics
engineer
technology
consultant
SAHM

3. INSANELY QUICK INTRO TO +
➤ Concur Accelerator Team
➤ Concur Labs
➤ Incubator (still brewing)
850K
Users log into Concur
300K
Expense reports
processed
120K
Trips booked
170M
Trips & expense
reports warehoused
Typical Day at Concur
How do we encourage a culture of innovation
while delivering quality service to our existing
33,000 business clients and 40M users?

4. DESIGN SPRINTS FOR DATA SCIENCEY PROTOTYPES
courtesy google ventures {we iterated…because data}

5. INSANELY QUICK INTRO TO
➤ “fast and general engine for large-scale data processing”
➤ advanced cyclic data flow and in-memory computing > runs
10x-100x faster than Hadoop MR
➤ interactive shells in several languages (incl. SQL)
➤ performant + scalable
courtesy databricks

6. ALMOST AS INSANELY QUICK INTRO TO +
➤ graphlab create is based on a python data science library
developed + (some) os’d by turi
➤ SFrame <<>> Spark DataFrame | SparkRDD
➤ (yes it works with Open Source SFrame and GLC)
courtesy turi

7. WHAT PROBLEM DO WE WANT TO DATA SCIENCE?
Knowledge
Gaps
IOT
Networks
Bots
Fairness
+

8. ➤ “We could {build this} {answer this better} if….”
➤ Reciprocal Data Applications
DESIGN FOR KNOWLEDGE GAPS
rda rdarda
choose
your data
storage
choose
your data
storage
choose
your data
storage
the app you
really
want to make

9. ➤ “Can we trust our sensors?”
➤ “Has our network been hacked?”
DESIGN FOR IOT NETWORKS
device
device
device
alerts,
notifications,
monitoring
dashboards
data
services
Anomaly Detection Toolkit
TimeSeries <<>> SFrame

10. ➤ “How do we create a conversational interface?”
….nothing new, just the burning question since Turing, 1950
DESIGN FOR BOTS
what NOT to do….
non-creepy
unisex
animal mascot
conversational
ui
choose
or
create
your
framework
choose your data storage
Advanced Deep Learning
Text Analysis Toolkit
Graph Analytics Toolkit

11. ➤ know your biases + limitations
➤ in your data, their data, all the data
➤ in your feature selection
➤ in your algorithm
…..because ethics (these ALL bias your results + communications)
DESIGN FOR FAIRNESS
learn more at data & society’s case studies
+ +
open source. reproducible. transparent.

12. {THANKS MUCH}
➤ Concur is hiring!
➤ SAP + SAP Ariba are
hiring!
concurlabs.com
github.com/
concurlabs
➤ example notebooks will
be posted on our
github in the future
@amcasari