1. Secrets of
Managing
Data
Science
Projects

2. Data Science For
Project Managers
P M I - W V P R O F E S S I O N A L D E V E LO P M E N T C O N F E R E N C E
O C T O B E R 20 1 5
T Z E - Y I U YO N G

3. Topic Coverage
Exploration of data science from various contexts
Data products: Output of data science
Implications for project managers

4. HELLO!
My name is Tze-Yiu Yong.
Technology Manager
https://www.linkedin.com/in/tzeyiuyong

5. More About Tze
Electrical engineer by training (semiconductor / statistics)
MBA from Duke University
Passionate about bridging the gap between technologists
and non-technologists.
Currently studying for PMP – advice appreciated!

6. Data Generated Every Minute

7. How Big Is The Digital Universe?
IBM conservatively
reports that we create
2.5 Exabytes of data per
day!
(1EB = 1MTBs)
(1ZB = 1K EB or 1BTBs)
Michael Lesk, the Bell Labs computer scientist known for the Lesk Algorithm, wrote in 1997 (!), “There may be a
few thousand petabytes (1KTBs) of information all told … the typical piece of information will never be looked at
by a human being.”

8. Believe WhatYou See On Television?

9. Dictionary Terms – Sum Of Parts
DATA
• a set of values of qualitative or quantitative variables.
• pieces of data are individual pieces of information.
SCIENCE
• organizes knowledge in the form of testable explanations and predictions.
• a way of pursuing knowledge, not only the knowledge itself.
www.thefreedictionary.com

10. Google Search Interest
Data Science evolved from BusinessAnalytics which evolved from Data Mining.

11. Job Growth

12. Job Skills
In 2009, Drew Conway published thisVenn Diagram of Data Science
skills.

13. Job Skills
Which was then expanded on by Natalia Bilenko.

14. Job Skills
Now crystallized by reality.
Joel Grus Profile Pic

15. The Government Knows!

16. The Wikipedia

17. Data Product
“Any tool created with the
help of data to make a more
informed decision.”
Examples
Dashboards
Spreadsheets
Emails
‘People AlsoViewed’
‘More ItemsTo Consider’
Many others

18. Data Science Workflow
Hadoop / Cassandra (FB) / HBase
DB Schema
DBMS
HW: Cluster / node
Big Data
Descriptive Predictive
Analytics Analytics
(Now)
Real-time
Analytics
TIME
Unstructured data
Data munging
Machine learning
Data mining
Tableau
Datawrapper

19. Time Check / Questions? [6 more slides]
(4 MEATY ONES)

20. Implications For Project Managers
“Develop a reporting portal for internal stakeholders and customers that
incorporates and aggregates machine data from a 30 unit installed base.”
“Implement a smart meter program for a major utility that enables increased
accuracy of customer billings, improved outage management and reduced costs of
usage collection information.”
“Analyze existing credit related customer data to develop a predictive credit score
and risk of default model. Implement a user interface that facilitates broad usability
across a wide range of internal business analysts.”

21. Implications For Project Managers.
“Develop a reporting portal for internal stakeholders and customers that
incorporates and aggregates machine data from a 30 unit installed base.”
“Implement a smart meter program for a major utility that enables increased
accuracy of customer billings, improved outage management and reduced costs of
usage collection information.”
“Analyze existing credit related customer data to develop a predictive credit score
and risk of default model. Implement a user interface that facilitates broad usability
across a wide range of internal business analysts.”

22. Implications For Project Managers
You already have a good foundation.

23. Implications For Project Managers
Know your stakeholders – they are numerous. [Initiating]
Don’t let tools drive your policy or process. [Initiating / Planning]
Think about boundaries – external vs internal, functional. [Executing]
Manage up. [Monitor and Control]

24. Unique Considerations
Seek agreement on the quality of the data this is required for the data product.
Understand and then communicate where the data comes from and where its going.
Internalize the privacy concerns, regulations and practices for the data in your
project scope.

25. Thanks for your interest and attention!
I AM HOPING FOR DISCUSSION …
CONNECT WITH ME ON LINKEDIN
HTTPS://WWW.LINKEDIN.COM/IN/TZEYIUYONG