Introduction to data science
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Introduction to Data Science and the career of a Data Scientist.
Transcript of Introduction to data science
Introduction to Analytics/Data Science
Koo Ping Shung
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.
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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. DavenportCompeting on Analytics: The New Science of Winning
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What are Analytics?
Put it simply,
It is IMPROVING Performance in KEY business domain using
DATA and ANALYSIS
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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
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From: Defining Business Analytics and Its Impact on Organizational Decision Making by Computerworld
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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 StubbsThe Value of Business Analytics: Identifying the Path to Profitability
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What is Analytics?
Source: Competing on Analytics (2007)
When is Analytics not Practical?
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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?
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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
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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
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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
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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?
