This SAS Training from Edureka will help you to understand all the concepts of Data Analytics tools - SAS, its components, features and example programs, Web Scraping use case and how it can be used in the industry. Below are the topics covered in this tutorial:
1. What is Data Analytics?
2. Data Analytics Tools
3. Why SAS?
4. What is SAS?
5. SAS Features
6. Programming in SAS
7. Case Study - Web Scraping using SAS
8. SAS Job Trends
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What to expect?
Data Analytics
Data Analytics Tools
Why SAS?
What is SAS?
SAS Features
Coding in SAS
Use Case – Bank Data Analysis
SAS Job Trends
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Why Data Analytics?
Cost
Reduction
Improved
Services or
Products
Faster and Better
Decision Making
Next Generation
Products
Data
Analytics
Data Analytics help
manage resources so
as to reduce costs
Analytics enables
better work related
decisions
Meeting customer
needs through better
services
Data Analytics paves the
way for the creation of
next gen products
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Why Data Analytics?
Data analytics examines large and different
types of data to uncover hidden patterns,
correlations and other insights
DATA ANALYTICS DECISIONS
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Data Analytics Tools
There are many tools to perform Data Analytics and the popular ones are:
Tableau
Excel
QlikView
Splunk
SAS
Python
Apache Spark
Apache Storm
Pig & Hive
R
Paid Tools Open Source Tools
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Why SAS?
We will compare SAS with the popular alternatives in the market on the following aspects:
Ease of Learning
Data Handling Capabilities
Graphical Capabilities
Advancements in tool
Job Scenario
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Why SAS?
We will compare SAS with the popular alternatives in the market on the following aspects:
Ease of Learning:
Data Handling Capabilities
Graphical Capabilities
Advancements in tool
Job Scenario
SAS is easy to learn and provides easy option (PROC SQL) for people
who already know SQL. R on the other hand has a very steep
learning curve as it is a low level programming language.
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Why SAS?
We will compare SAS with the popular alternatives in the market on the following aspects:
Ease of Learning
Data Handling Capabilities:
Graphical Capabilities
Advancements in tool
Job Scenario
SAS is on par with all leading tools including R & Python when
it comes to handling huge amount of data and options for
parallel computations.
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Why SAS?
We will compare SAS with the popular alternatives in the market on the following aspects:
Ease of Learning
Data Handling Capabilities
Graphical Capabilities:
Advancements in tool
Job Scenario
SAS provides functional graphical capabilities and with a little
bit of learning, it is possible to customize on these plots.
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Why SAS?
We will compare SAS with the popular alternatives in the market on the following aspects:
Ease of Learning
Data Handling Capabilities
Graphical Capabilities:
Advancements in tool:
Job Scenario
SAS releases updates in controlled environment, hence they
are well tested. R & Python on the other hand, have open
contribution and there are chances of errors in latest
developments.
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Why SAS?
We will compare SAS with the popular alternatives in the market on the following aspects:
Ease of Learning
Data Handling Capabilities
Graphical Capabilities
Advancements in tool
Job Scenario: Globally, SAS is the market leader in available corporate jobs.
In India, SAS controls about 70% of the data analytics market
share compared to 15% for R.
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What is SAS?
SAS (Statistical Analytics System) is a software suite for advanced analytics,
multivariate analyses, business intelligence, data management and predictive
analytics.
It is developed by SAS Institute.
SAS provides a graphical point-and-click user interface for non-technical users
and more advanced options through the SAS language.
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SAS Features
Base SAS
Flexible Extensible Integrated Powerful
Business
Solutions
Analytics
Reporting
and Graphics
Data Access and
Management
Visualization
and Discovery
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Base SAS
Flexible Extensible Integrated Powerful
SAS Features
Reporting
Data Access
Transformation
Let us look at some of the features of SAS in detail
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Access
ManageAnalyze
Present
SAS Framework
Data
SAS Framework - Manage
SAS provides excellent Data
Management capabilities to:
1. Subset Data
2. Create Variables
3. Validate & Clean Data
2. Manage
Subset Data
Create Variables
Validate & Clean
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Access
ManageAnalyze
Present
SAS Framework
Data
SAS Framework - Analyze
We can perform simple analyses like
frequency and averages.
Complex analyses include regression and
forecasting.
SAS is the gold standard for statistical
analyses.
3. Analyze
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Access
ManageAnalyze
Present
SAS Framework
Data
SAS Framework - Present
4. Present
We can present our analysis in the form of:
List Reports
Summary Reports
Graphic Reports
We can either print these reports, write
them to data file or publish them online
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SAS Program
All of these statements can represent a step boundary by indicating either the end of a step or the beginning
of a new step.
RUN Statement
QUIT Statement
DATA Statement
PROC Statement
A DATA step typically creates a SAS data set. However, you can use DATA steps to create raw data, program
files, and reports. The DATA step is very flexible.
A PROC step typically creates a report.
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SAS Program
data work.newsalesemps;
set orion.sales;
where Country='AU';
run;
title 'New Sales Employees';
proc print data=work.newsalesemps;
run;
proc means data=work.newsalesemps;
class Job_Title;
var Salary;
run;
title;
Let us look at a simple SAS program
A SAS program is comprised of a sequence of
steps, and a step is comprised of a sequence of
statements.
Every step has a beginning and ending boundary.
These are called step boundaries.
SAS compiles and executes each step
independently based on the step boundaries.
Data Step
Proc Step
Proc Step
Global
Statement
End of
Global
Statement
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SAS Program
data work.newsalesemps;
set orion.sales;
where Country='AU';
run;
title 'New Sales Employees';
proc print data=work.newsalesemps;
run;
proc means data=work.newsalesemps;
class Job_Title;
var Salary;
run;
title;
A DATA step begins with a DATA statement and
a PROC step begins with a PROC statement.
SAS detects the end of a step when it
encounters one of the following:
1. A RUN statement for most steps
2. A QUIT statement for some procedures
3. The beginning of another step.
Beginning
Ending
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SAS Program - Result
We will submit the code and check the log. The results
are as follows.
The first report is the PROC PRINT report.
Notice that the title you specified appears at the top
of the report.
The next report is the PROC MEANS report. Here, SAS
calculated statistics for the analysis variable Salary.
The MEANS procedure provides data summarization
tools to compute descriptive statistics on your data,
and displays output by default.
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SAS Program - Result
The next report is the PROC MEANS report. Here, SAS
calculated statistics for the analysis variable Salary.
The MEANS procedure provides data summarization
tools to compute descriptive statistics on your data,
and displays output by default.
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SAS Supported Files
Depending on your results, you might need to repeat some of the steps.
The power of SAS is that you can use it to read any type of data, including the following three major file
types:
Raw data files contain data that has not been processed by any other computer program. They are text files
that contain one record per line, and the record typically contains multiple fields. Raw data files aren’t
reports; they are unformatted text.
SAS data sets are specific to SAS. A SAS data set is data in a form that SAS can understand. Like raw data
files, SAS data sets contain data. But in SAS data sets, the data is created only by SAS and can be read only by
SAS.
SAS program files contain SAS programming code. These instructions tell SAS how to process your data and
what output to create. You can save and reuse SAS program files.
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Use Case – Bank Data Analysis
Bank Data Analysis using SAS
Introduction:
KM Bank is a commercial bank in Delhi. It has two branches: North and South
East.
The bank has a call centre in each of these two branch regions to cater to the
needs of the customer.
Data is stored in different excel files due to which it sometimes gets
complicated and time consuming to retrieve information about a customer.
Therefore, they are looking for a way to blend the data such that they have
the information they need in one dataset & also with that they are able to
draw some insights through the data.
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Use Case – Bank Data Analysis
Problem Statement:
Extract meaningful business side insights from the bank customer data by
merging both the branches’ data using SAS functions.
Use these insights to find high-value customers, value correlation and the
effect of sales calls on various products the bank has to offer.
Analyses to be done:
Combine the data
from both the
bank branches for
further analytics
1
Find out the total
number of calls made
to every customer from
outside his/her branch
2
Give bi-variate
frequency of
customers by gender
and age-group
3
Find a relation between
a customer’s age and
bank balance
4
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Job Trends in SAS
The following is the Job Trend of SAS
& SAS Modeling across the world
SAS has been a market leader when
it comes to Data Analytics Jobs
Source: www.indeed.com