Introduction to Data Science and the career of a Data Scientist.

1. Introduction to
Analytics/Data Science
Koo Ping Shung
pskoo@smu.edu.sg/koolanalytics@gmail.com

2. Acknowledgement
Most of the information are adapted from two books
by Thomas Davenport.
Competing on Analytics (2007)
Analytics at Work (2010)
For those who are interested in implementation of
Data Science, you are strongly encouraged to read
these two books.

3. 3
What are Analytics?
Analytics is the
extensive use of data,
statistical and quantitative analysis,
explanatory and predictive models,
and fact-based management to
drive decisions and actions.
~ Thomas H. Davenport
Competing on Analytics: The New Science of Winning

4. 4
What are Analytics?
Put it simply,
It is IMPROVING Performance in
KEY business domain using
DATA and ANALYSIS

5. 5
Benefits of Being Analytical
Better understanding of the dynamics of business environment,
Know the factors affecting business performance.
Get more value from previous IT investments
Cut costs and improve efficiency.
Using predictive models to anticipate market movement.
Detect changes in market conditions
Managing risk e.g. Basel III
Strong basis for making decisions
Provide a platform to learn from and improve on decisions made.
Better understanding of existing business processes.

6. Business Benefits of Analytics
6
From: Defining Business Analytics and Its Impact on Organizational
Decision Making by Computerworld

7. 7
Why Analytics?
Competitive Advantage comes from
capitalizing on what makes you unique.
Every organization is different and every
organization has the potential to exploit that
exact uniqueness in a way that no one else
can match. Doing this means taking
advantage of their single biggest resource:
THEIR DATA.
~ Evan Stubbs
The Value of Business Analytics: Identifying the Path to Profitability

8. 8
What is Analytics?
Source: Competing on Analytics (2007)

9. When is Analytics not Practical?
9
When there is no time.
To pull or not to pull?
How to Save?
When there is no precedent.
No data collected.
Start small to collect data.
When History is Misleading
Greatly dependent on training data.
Be prepared for ‘extreme’ times. E.g. Lehman Brother
Collapse

10. When is Analytics not Practical?
10
When the Decision Maker has a lot of Experience
Has massive amount of experience making a particular
decision.
Still better to have collected data collected.
When the Variables Cannot be Measured
Measure trust?

11. Cautionary Messages
11
Do not rely on Analytics solely to achieve
Success
Analytical decisions will not always be perfect
Better than a blind guess.
New Analytically Based insights have to be
developed conscientiously to Stay ahead of
Competition
It’s a journey, not a destination.

12. Cautionary Messages
12
Changes in the environment can invalidate your
models
Model Decay
Changes such as technology, economy, demographics.
Analytics are not all you need to make good
decisions
Always use all available tools to make better decisions.
Data is just part of the decision toolbox

13. Promises
13
Better strategic decisions
Better Tactical and Operational Decisions
Have Better Processes.
Better Ability to solve Problems
Make Faster Decisions and Get more Consistent
Results
Anticipate Shifting Trends and Market Conditions

14. Becoming a Data Scientist

15. Required Skills (I)
Quantitative Skills
Statistical & Operation Research
General Business Knowledge
Marketing, Strategic Management, Accounting, Team
Management
Experiment Design Skill
Collect the ‘Right’ data
Data Management Knowledge
High Quality Data

16. Required Skills (II)
Presentation & Communication Skills
Dashboard Design
Report Design…etc
Programming Skills
Using software to manage & analyse data.
Creativity
Solutions provider
Constraints in infrastructure & techniques
Observation Skills
Eye for details to provide good insights and
recommendations.
INTEGRITY!

17. Discussion (I)
What is the difference between Statistics and
Analytics/Data Science?
What is the difference between Academic
Research and Data Science?

18. Koo Ping Shung
pskoo@smu.edu.sg/koolanalytics@gmail.com