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Introduction to
Analytics/Data Science
Koo Ping Shung
pskoo@smu.edu.sg/koolanalytics@gmail.com
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
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
What are Analytics?
Put it simply,
It is IMPROVING Performance in
KEY business domain using
DATA and ANALYSIS
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.
Business Benefits of Analytics
6
From: Defining Business Analytics and Its Impact on Organizational
Decision Making by Computerworld
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
What is Analytics?
Source: Competing on Analytics (2007)
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
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?
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.
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
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
Becoming a Data Scientist
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
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!
Discussion (I)
What is the difference between Statistics and
Analytics/Data Science?
What is the difference between Academic
Research and Data Science?
Koo Ping Shung
pskoo@smu.edu.sg/koolanalytics@gmail.com

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Introduction to data science

  • 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?