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Introduction about analytics with SAS+R Programming.

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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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The Requirement for Modern Analytics

Talent

Tech

tools

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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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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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The Technology :EMA Hybrid Data EcosystemR

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What’s next after machine learning ?

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Data Mining ?

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Text Mining ?

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Best Practices : Breaking Down DataR

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How do I achieve value from Modern Analyticswhen the data, events and models are constantlychanging ?

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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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What application fields can we use withModern Analytics ?

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What is your advice for a Business analystinterested in building out modern analytics?

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How can real-time data preparation, modeltraining, etc. be done in SAS Analytic tool.

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How can real-time data preparation, modeltraining, etc. be done in R Programming.

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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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Continue…..

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Continue…..

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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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Continue…..

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http://www.r-project.org/

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Learn More

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Learn more at https://sasanalyst.wordpress.com/

Presented by Ravi Mandal

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