1. Let’s Get Started…
Introduction about analytics
with SAS+R Programming.
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2. Modern Analytics Vs Traditional Analytics
Traditional Modern
Limited to descriptive summaries, e.g. top 10
customers
Support predictive and prescriptive model.
Assumptions and guess work built into forecast. Reliable calculation of percentage of confidence
(ARIMA forecast and similar)
Analysis performed on a sample of the data. Analysis performed of full data.
Broad assumptions and conclusions, limited
granularity.
Mass personalization-data granularity down to the
individual level.
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3. The Requirement for Modern Analytics
Talent
Tech
tools
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4. Mckinsey projects by 2018
There will be a shortage of
1.7 M professionals
With analytics expertise in the U.S. alone.
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5. The Talent : Project Users
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
Line of Business Executives
Marketing Analytics,Finance Analytics(e.g Business Analysis)
Database Administration , Data Analysis(e.g IT analysis)
Data Scientists
External Users(e.g partners, Customers, service Providers)
Report writers and dashboard builders
Application Developers
Percentage of Projects(Approx)
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6. The Technology :EMA Hybrid Data Ecosystem
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7. What’s next after machine learning ?
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8. Data Mining ?
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9. Text Mining ?
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10. Best Practices : Breaking Down Data
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11. How do I achieve value from Modern Analytics
when the data, events and models are constantly
changing ?
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12. Best Practices : Using Modern Analytics to Data Business Value
Identify key
business objective
Define your value
chain
Brainstorm
Analytic Solutions
Prioritize Analytic
Solutions using
Decision Making
Create decision
model
Define analytic
Solutions via Day-
in-life Scenarios
Establish &
Communicate
analysis Roadmaps
Evolve your
Analytics
Roadmaps
Present as par
requirement
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13. What application fields can we use with
Modern Analytics ?
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14. What is your advice for a Business analyst
interested in building out modern analytics?
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15. How can real-time data preparation, model
training, etc. be done in SAS Analytic tool.
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16. How can real-time data preparation, model
training, etc. be done in R Programming.
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17. Overview Of SAS :
The SAS language is a
computer programming language used for
statistical analysis, originated by a project
at the North Carolina State University. It can
read in data from common spreadsheets
and databases and output the results of
statistical analyses in tables, graphs, and
as RTF, HTML and PDF documents. The
SAS language runs under compilers that
can be used on Microsoft Windows, Linux,
and various other UNIX and mainframe
computers.
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18. Continue…..
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Continue…..

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21. Overview Of R language :
R is a programming language and software
environment for statistical computing and
graphics. The R language is widely used
among statisticians and data miners for
developing statistical software and data
analysis. Polls, surveys of data miners, and
studies of scholarly literature databases
show that R's popularity has increased
substantially in recent years.
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22. Continue…..
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http://www.r-project.org/

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24. Learn More
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25. Learn more at https://sasanalyst.wordpress.com/
Presented by
Ravi Mandal
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