1 April 10, 2023© Kalido I Kalido Confidential April 10, 2023

Data Scientist: Your Must-Have Business Investment NOW

2 April 10, 2023© Kalido I Kalido Confidential April 10, 2023

Gregory Piatetsky

Editor, Kdnuggets

co-founder KDD and ACM SIGKDD

David Smith

Data Scientist

Revolution Analytics

Carla Gentry

Data Scientist

Analytical Solution

Darren Peirce

CTO

Kalido

Eric Kavanagh

DM Radio Host

Information Management Magazine’s DM Radio

Today’s Speakers #DataScienceNow

Revolution Confidential

What is a data scientist?

3

© Dov Harrington, CC By-2.0

http://www.flickr.com/photos/idovermani/4110546683/

David SmithRevolution Analytics

@revodavid

Revolution Confidential

Statistician Data Scientist

Image Baseball (Cricket) HBR Sexiest Job of 21st Century

Mode Reactive Consultative

Works Solo In a team

Inputs Data File, Hypothesis A Business Problem

Data Pre-prepared, clean Distributed, messy, unstructured

Data Size Kilobytes Gigabytes

Tools SAS, Mainframe R, Python, awk, Hadoop, Linux, …

Nouns Tables Data Visualizations

Focus Inference (why) Prediction (what)

Output Report Data App / Data Product

Latency Weeks Seconds

Stars G.E.P BoxTrevor Hastie

Hilary MasonNate Silver

http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ 4

Revolution Confidential

Statistician Data Scientist

Image Baseball (Cricket) HBR Sexiest Job of 21st Century

Mode Reactive Consultative

Works Solo In a team

Inputs Data File, Hypothesis A Business Problem

Data Pre-prepared, clean Distributed, messy, unstructured

Data Size Kilobytes Gigabytes

Tools SAS, Mainframe R, Python, awk, Hadoop, Linux, …

Nouns Tables Data Visualizations

Focus Inference (why) Prediction (what)

Output Report Data App / Data Product

Latency Weeks Seconds

Stars G.E.P BoxTrevor Hastie

Hilary MasonNate Silver

5

Revolution ConfidentialThree Essential Skills of Data Scientists

6

Drew Conway

http://www.dataists.com/2010/09/the-data-science-venn-diagram/

Data IntegrationMashups

Applications

ModelsVisualizationPredictionsUncertainty

ProblemsData Sources

Credibility

EffectiveData

Applications

Revolution ConfidentialData Science to the Rescue!

Revolution Confidential

Business Intelligence Data Science

Perspective Looking backwards Looking forwards

Actions Slice and Dice Interact

Expertise Business User Data Scientist

Data Warehoused, Siloed Distributed, real-time

Scope Unlimited Specific business question

Questions What happened? What will happen?What if?

Output Table Answer

Applicability Historic, possible confounding factors

Future, correcting for influences

Tools SAP, Cognos, Microstrategy, SAS

Revolution R EnterpriseQlikView, Tableau, Jaspersoft

Hot or not? So 1997 Transformational

8

What is Data Science?

By Carla GentryData Scientist

Analytical-Solution

Data Science is….• The term "data science" has existed for over thirty

years – first mentioned by Peter Naur in 1960 but more recently it has gained a lot of attention!

Data Science can be broken down into 4 main areas of expertise.

• Data knowledge – design & structure

• Programming– SAS, R, SQL, NO-SQL

• Analytics– Insight

• Communication– Tell the story

Data Knowledge: Part analyst - part IT

• What kind of servers do you own?- Servers vs. Mainframe

• What kind of load can the server handle?- Iterations matter

– Why ask this?

Programming – Pick a language and use it wisely

• Efficiency is KING!- Why?

• Number of iterations & complex algorithms or scripts. Snowflakes vs. Star schema?-Design is import but why?

• Key things: normalize, index, there is more to Data Science than just analytics.

How can I learn about Data Science?

• For those who want to invest their time and talent there are resources.

• College Courses• Online• Webinars• Blogs

© KDnuggets 2013 15

Data Science and Data Scientists Now

Gregory Piatetsky, @kdnuggets

Analytics, Big Data, Data Mining, and Data Science Resources

© KDnuggets 2013 16

• Statistics, 1830-• Data mining, 1980-• Knowledge Discovery in Data (KDD), 1989-• Business Analytics, 1997-• Predictive Analytics, 2002-• Data Analytics,2011-• Data Science, 2011-• …?

Same Core Idea:

Finding Useful Patterns in Data

Different

Emphasis

Trends from Google Ngrams (1800-2008)

and Google Trends (2005-2013)

© KDnuggets 2013 17

Big Data > Data Mining > Business Analytics > Predictive Analytics

> Data Science

Big Data

Google Trends search, Jan 2008- Apr 2013, Worldwide

Data mining

© KDnuggets 2013 18

Data Scientist – sexiest job of the 21st Century (???)

say Thomas H. Davenport and D.J. Patil, (HBR, Oct 2012)

“Data Scientist”

Fastest growing term on

www.kdnuggets.com/jobs

1% of jobs in 2010

4% of jobs in 2011

19% of jobs in 2012

23% of jobs in 2013

© KDnuggets 2013 19

Data Mining

Big Data

Data Scientist

“Data mining” jobs are more common, but

“Big Data” jobs are surging much faster than “Data Scientist”

“Statistician” jobs are steady, but not growing

Statistician

© KDnuggets 2013 20

• Big Data can produce better predictions, but expect limited improvement

• Example: Netflix prize took 3 years to improve prediction of movie ranking from 0.95 stars to 0.86

• Inherent randomness in human behavior• Data Science should help separate hype from reality• Biggest effects from Big Data are from new platforms, like

Google, Facebook, LinkedIn; Personalized medicine• However, Big Data makes privacy online almost possible

Gregory Piatetsky-Shapiro, Big Data Hype and Reality, Harvard Business Review blog, Oct 2012

21© 2013 KDnuggets

Gartner Hype Cycle

Big Data

Gartner VP says Big Data is Falling into the Trough of Disillusionment, Jan 2013

© 2013 Kalido I Kalido Confidential I April 10, 202322

Q&A

Gregory PiatetskyEditor, Kdnuggetsco-founder KDD and ACM SIGKDD@kdnuggets

David SmithData ScientistRevolution Analytics@revodavid

Carla GentryData ScientistAnalytical Solution@data_nerd

Darren PeirceCTOKalido@DarrenPeirce

Eric KavanaghDM Radio HostInformation Management Magazine’s DM Radio@eric_kavanagh

© 2010 Kalido I Kalido Confidential I April 10, 202323

Summers Sessions: Two Tracks For YOU

Series Kickoff

May 14: Data Scientist: Your must-have business investment now.

(30 Minute Learning Sessions)

May 28 Rapid Data Integration tools and methods

June 4 Harmonizing Data for the Warehouse

June 11 Rapid Iteration Methodology Using Information Models

Series Kickoff

June 25: Find your data warehouse’s hidden costs before they find you.

(30 Minute Learning Sessions)

July 2 The real cost per release cycle

July 9 Automate to reduce operating costs

July 16 Reduce tool cost

July 23 Scale drives cost reductions

Agile Information Foundationfor the Data Scientist

TCO: Find data warehouse costs before they find you.

Visit get.kalido.com/summer-series to register