Although both disciplines are unique in their own ways, Software Engineering and Data Science make heavy use of programing languages to do their respective jobs. Data Science is a relatively new discipline and many of its practitioners have not previously been professional software engineers. There are a few techniques that Data Scientists can leverage from Software Engineering in order to make their tooling and environments, faster to design, more easily debugged and most importantly, clearer to read. This talk will be going over some practical tips that anyone can use to help better understand their code; give clarity around cloud environments, their uses and drawbacks and finally briefly touching on the Software Development Lifecycle.

1. Engineering the Data Science

2. 2
Data is like gunpowder!
You can make a marvelous firework
OR
a dangerous weapon from it

3. Core Team Building
Lead Data Scientist
Understands the needs of
stakeholders, identifies the
KPI, data sources and
capabilities
Data Engineer
Cleanses, provide and ETL
the data
Data Scientist
Makes the data actionable:
exploring the data, modeling,
leveraging the models and
developing patterns
Application Developer
Controls Enterprise open
source libraries and versions,
develops and leverage current
APIs
Data Data Visualization Specialist
Coordinates with data
scientists to design and
implement KPIs
&
Summarizing the data and increasing ROIs

4. Data Science Delivery Models
Information Delivery /
Data Visualization Data in Mart?
User Developed
Application?
Requirements
Uncertain?
Existing
Sandbox?
Self Service
Analytics &
Reporting
Hours/Days
Days/Weeks
Weeks/Months
Months
Self Service
Discovery
Prototype /
Discovery
Data Discovery and
Advanced Analytics
Operational Reporting &
Integration
Data for
Operational?
Data being
captured?
No Statistical
Modeling
needed?
Business
Performance
Reporting
Model
Integration
New Subject Area / Source
System Acquisition
Operational System
Enhancements
Foundational Data for
Analytics
Y
Y
Y
Y
Y
Y
Y
N
N
N
N
N
N
N

5. ROI / Competitive
Advantage
Clarity deploys innovative
technology and advanced
analytics to deliver full life-
cycle analytic solutions that
deliver ROI and competitive
advantage.
Competitive advantage
requires robust scenario
planning and prudent risk
management
Regardless of shape or structure,
Clarity can turn data into
insight… enabling a deep
understanding of your business
trends
Clarity can imbed systemic,
forward looking analytics into
strategic decision and risk
management processes to
improve strategy and reduce risk
Clarity can operationalize predictive
insights to drive proactive decisions
and guide customer interactions
real time
Descriptive Analytics
Understand business performance
Discover patterns, explore trends
and establish relationships to
explain past performance
Predictive Analytics
Identify risk and opportunity, guide
decision making
Exploit patterns, trends and
relationships to predict likely
outcome of future event or
situation
Prescriptive Analytics
Prescribe action, understand
options, manage risk
Synthesize models with business
rules and constraints to understand
implications of available courses of
actions
From Hopeful to
Thoughtful
Competitive advantage
requires acting upon trends
and insights
From Reactive to
Proactive
Competitive advantage in
today’s business
environment requires a
deep understanding of why,
not just how much
From Data to Insight
Forward thinking, data enabled organizations
outperform peers…

6. Key Elements of Implementation
Readable and Reusable Models/Scripts
Easy to Migrate

7. How to Implement
IT Strategy
Hypothesis driven approach
Departmental integration and collaboration

8. Version Control
8
Open Source
Proprietary

9. Code Check: BAD Code
9
dat_dropship<- read.csv("~/Projects/STB_dropships_Apr2015_csv.csv", colClasses = "character")
dat_dropship_march <- read.csv("~/Projects/STB_dropships_Mar2015_cvs.csv", colClasses =
"character")
dat_dropship_feb <- read.csv("~/Projects/STB_dropships_Feb2015_cvs.csv", colClasses =
"character")
dat_dropship_jan<- read.csv("~/Projects/STB_dropships_Jan2015_csv.csv", colClasses =
"character")
dat_pass <- read.csv("~/Projects/AprilRepairSTBlatest_circuit.csv", colClasses = "character")
dat_all_Apr_June <- read.csv("~/Projects/Apr-Jun2015STBRepair_allBItables.csv", colClasses =
"character")
dat_pass_Apr_June<-dat_all_Apr_June
# dat_pass_Apr_June<-subset(dat_all_Apr_June,dat_all_Apr_June$FIRST_TEST_RESULT_IND=="FAIL")
##All STBs Repair return type
dat_Repair_Apr_June<-subset(dat_all_Apr_June,dat_all_Apr_June$RETURN_TYPE=="Repair")
dat_Repair_Apr_June_US<-dat_Repair_Apr_June[grep("^US",
dat_Repair_Apr_June$RETURN_LOCATION),]
dat_Repair_Apr_June_US<-
subset(dat_Repair_Apr_June_US,dat_Repair_Apr_June_US$RETURN_AGENT!="V000000")
dat_Repair_Apr_June_US<-
subset(dat_Repair_Apr_June_US,dat_Repair_Apr_June_US$RETURN_AGENT!="AUTOSYNC")
dat_Repair_Apr_June_US<-
subset(dat_Repair_Apr_June_US,dat_Repair_Apr_June_US$RETURN_AGENT!="UNDOC")
Repair_Apr_Jun_Loc_all<-split(dat_Repair_Apr_June,dat_Repair_Apr_June$RETURN_LOCATION)
Locs_Repair_Apr_Jun<-sapply(Repair_Apr_Jun_Loc_all, nrow)
Repair_Apr_Jun_Loc<-split(dat_Repair_Apr_June_US,
paste(dat_Repair_Apr_June_US$RETURN_LOCATION,dat_Repair_Apr_June_US$RETURN_AGENT))
Locs_Agent_Repair_Apr_Jun<-sapply(Repair_Apr_Jun_Loc, nrow)

10. Code Check: Problem
10
-Nearly impossible to debug
-Endless Copy and Pastes
-Hard to Control the version
-Impossible to work with a Peer

11. Code Check: What to Do
11
- Version control on a shared platform
- Library Version Control
- Functional and Modular Coding