This document discusses data mining, business intelligence, and data science. It begins with an introduction to data mining, defining it as the application of algorithms to extract patterns from data. Business intelligence is defined as applications, infrastructure, tools, and practices that enable access to and analysis of information to improve decisions and performance. Data science is related to data mining, analytics, machine learning, and uses techniques from statistics and computer science to discover patterns in large datasets. The document provides examples of how data is used in areas like understanding customers, healthcare, sports, and financial trading.

1. Asst. Prof. Dr. Jirapun Daengdej
Faculty of Science and Technology
Assumption University
jirapun@scitech.au.edu
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http://www.greenbookblog.org/2013/05/16/are-you-burning-away-your-data-fuel/

3. 3
The problem is…..

4. 
 Background
 Your Expectations & Pain Points?
 What is “Data Mining”?
 What is “Business Intelligence”?
 What is “Data Science”?
 Real-World Cases
Contents
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5. 
Background
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6. 
Background
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7. 
Background
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8. 
Background
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9. 
Background
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10. 
What about “Data Game”?
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Figures don't lie, the old
saying, but liars can figure. Put
another way, even accurate and
honest-in-itself data can be
presented in misleading ways
to support a less-than-honest
result. To protect against data-
rich lies, we must learn to
understand the
limitations of data and
how it can be used - even
inadvertently - to mislead.
http://www.grtcorp.com/content/data-may-not-lie-liars-can

12. 
Your Expectations &
Pain Points?
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13. 
YOUR Expectation(s) and Pain Points?
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14. 
What is “Data Mining”?
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15. 
Definition
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Data mining is the application of specific algorithms
for extracting patterns from data. The distinction
between the KDD process and the data-mining step
(within the process) is a central point…

16. 
"Data mining" was introduced in the 1990s, but data
mining is the evolution of a field with a long history.
History
http://www.unc.edu/~xluan/258/datamining.html
Data mining roots are traced back along three
family lines:
• classical statistics,
• artificial intelligence,
• and machine learning.
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17. 
Data Mining & Stats?
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18. 
What is “Business Intelligence”?
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19. 
Business intelligence (BI) is an umbrella term that
includes the applications, infrastructure and tools, and
best practices that enable access to and analysis of
information to improve and optimize decisions and
performance.
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Definitions

20. 
BI 1.0 - 2.0 - 3.0
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http://smartdatacollective.com/yellowfin/195811/defining-business-intelligence-30

21. 
What Business want from BI?
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Buyers Overwhelmingly Want Better Data Visualization
http://www.softwareadvice.com/bi/buyerview/report-2014/

22. 
What is “Data Science”?
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http://www.datasciencecentral.com/profiles/blogs/17-analytic-disciplines-compared
http://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1
http://www.kdnuggets.com/2013/10/7-steps-learning-data-mining-data-science.html

24. 
Definition?
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25. 
Related Qualification?
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http://www.becomingadatascientist.com/2014/06/13/doing-data-science-review/

26. 
Data Science vs. Data Analytics
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http://datascientistinsights.com/2013/09/09/data-analytics-vs-data-science-two-separate-but-interconnected-disciplines/

27. 
Relationship between them?
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28. 
What do you think?
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29. 
Real-World Cases
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30. 
Real-World Cases
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2005….Yahoo!'s users,
through their use of our
network of products,
generate over 10 terabytes
of data per day. This is the
equivalent of the entire text
contents of the library of
Congress. This is data that
describes product usage, and
does not include content,
email, or images, etc.
http://www.kdd.org/newsletter/explorations-october-2005

31. 
From Yahoo! To DigiMine
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32. 
1. Understanding and Targeting Customers
2. Understanding and Optimizing Business Processes
3. Personal Quantification and Performance Optimization
4. Improving Healthcare and Public Health
5. Improving Sports Performance
6. Improving Science and Research
7. Optimizing Machine and Device Performance
8. Improving Security and Law Enforcement.
9. Improving and Optimizing Cities and Countries
10. Financial Trading
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The Awesome Ways Big Data Is
Used Today To Change Our World
http://www.datasciencecentral.com/profiles/blogs/the-awesome-ways-big-data-is-used-today-to-change-our-world

33. 
Q & A
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