• Bangalore - 560076
  • 8904740434
  • learn@techieventures.in

SAS training institute in Bangalore

Description

SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW.

SAS helps you make sense of the message. As the leader in business analytics software and services, SAS transforms your data into insights that give you a fresh perspective on your business.

Duration

Duration: 3Months

Career Option
  • Business Analyst
  • Data Analyst
  • SAS Programmer
Opportunities

Not Mentioned

Major Companies Using SAS
  • SAS India
  • Citianalytics
  • Tesco
  • Musigma
  • Manthan Systems
  • CrossTab
  • Modelytics
  • Pharmarc
  • Amba Research
  • Genpact and Symphony Marketing solutions
  • Infosys
  • Wipro
  • Capgemini
  • Mahindra satyam
  • IBM
  • Accenture
Course Content
TOPICS
1. SAS for Analyst Programmers

SAS/BASE
Introduction
Introduction to SAS
Introduction to Analytics
Editor File
Log File
Output File
Result, Explorer windows
Permanent SAS datasets
Environments where SAS runs
SAS Libraries
SAS Data types
Step boundaries and run-group processing
Advanced Input Features
Read Data and Raw Data files
INFILE Statement
Input Statement
DATALINES, CARDS
List INPUT
Column INPUT
Formatted INPUT
Mixed INPUT
:(colon) and & (Ampersand) Modifiers
double trailing @@
Black box SAS programming
/ and # pointers,Single trailing @, Character Pointer
MISSOVER,TRUNCOVER,SCANOVER
DLM,DSD,Firstobs,Obs
Data Step functions
Character functions (left,right,tranwrd,translate ,index, indexw, indexc,find,trim, catx,catt, cat,cats,compress, scan,substr, upcase,lowcase,strip,etc.
Numaricfunctions (int,ceil,floor,Round, abs, Min,Max,Sum,mean, lag, dif).
Date function (Today, Datetime, Time, Timepart, Datepart, Day, Month, Year, Qtr, MDY etc).
SAS System options
SAS Structures and Flow
Data step overview
LENGTH
FORMATS,IMFORMATS, LABLES
Titles and Footnotes
reading existing SAS datasets with SET
Assignment statements
RENAME
DROP and KEEP
Subsetting observations
Subsetting IF statement
If, If-then, Else-if, If then Do.
WHERE statement
Syntax
How to create Do loops
Conditional Do loops(DO until,DO while, by clause).
Nesting Do loops
Arrays
SAS Executable Statements
Accumulating totals
RETAIN and SUM
SUM statement
SELECT statement
Deleting observations
Numeric-character conversion
OUTPUT,PUT
Procedures
Proc Print
Proc Contents
Proc Sort
Proc Means
Proc Freq
Proc Report
Proc Tabulate
Proc Printto
Proc Dataset
Proc Compare
Proc Transpose
Proc Format (input, put function, creating permanent formats, informats)
Proc Import
Proc Export
Proc Append
Proc Summary
Merging SAS Datasets
Syntax
one to one merges
Match merging
Multiple OBS with the same BY variable
Merging with identical variable names
Merging without a common variable
Update statement
SAS/ODS
ODS/Trace/Select/Exclude
ODS/HTML FILE
ODS/PDF FILE
ODS/RTF FILE
PROC PRINT WITH STYLE OPTION
PROC REPORT WITH STYLE OPTION
PROC TABULATE WITH STYLE OPTION
SAS/GRAPH
Proc PLOT
Proc GPLOT
Proc CHART
Proc GCHART
SAS/SQL
SQL DDL Statements (CREATE,ALTER,DROP)
SQL DML Statements (INSERT,UPDATE,SELECT,DELETE)
SQL Filter Clauses (WHERE, GROUP BY, HAVING, ORDER BY)
SQL Horizontal Joins (INNER, LEFT,RIGHT, FULL, FULL with condition, Cartesian product)
SQL Vertical Joins (UNION,UNION ALL,INTERSECT,EXCEPT)
SAS/MACROS
An Introduction to SAS Macros
Functions of the SAS macro processor
Macro processor flow
Macro and macro variable
Defining and using a macro
Creating macro variables- 3 ways
Local and global Macro
Automatic macro variables
Avoid macro errors
Positional macro parameters
Keyword macro parameters
Call Symput and symget
System options for debugging macro
SAS/STATISTICS
Proc UNIVARIATE
Proc MEANS
Proc FREQ(Chi-Square)
Proc GLM
Proc RANK
Proc ANOVA
Proc REG
Proc LOGISTIC(logistic Regression)
Proc TTEST(Paired)
Proc CORR (correlation)
SAS/ACCESS
How to connect with data server
libname code
Sql Code
Projects
3 Projects have to be completed

2. Advance Analytics Using SAS

Introduction to Statistics/analytics
Need for analytics
Analytics use in different industries
Challenges in adoption of analytics
Overview of Course Contents
Data understanding : Data types (nominal, Ordinal, Interval and ratio)
Parametric & Non-Parametric test
Estimation
Descriptive statistics
Tabular & Graphical Method, Summary statistics,Means,Freq, Correlation,Rank etc
Linear Regression
fit a multiple linear regression model using the REG and GLM procedures
Analyze the output of the REG procedure for multiple linear regression models
Use the REG procedure to perform model selection
Assess the validity of a given regression model through the use of diagnostic and residual analysis
Logistic Regression
Perform logistic regression with the LOGISTIC procedure
Optimize model performance through input selection
Interpret the output of the LOGISTIC procedure
Score new data sets using the LOGISTIC and SCORE procedures
Introduction to some statistical terminologies and inferences
Population, Sample and random variables
Point and interval Estimations
Probability
Discrete/Continuous probability Distributions
Hypothesis Testing
T-Test
One-Tailed, Two-Tailed, Z-Test
Anova
Verify the assumptions of ANOVA
Analyze differences between population means using the GLM and TTEST procedures
perform ANOVA post hoc test to evaluate treatment effects
Detect and analyze interactions between factors
CHI-SQUARE
Prepare Inputs for Predictive Model Performance
Identify potential problems with input data
use the DATA step to maipulate data with loops, arrays,conditional statements and functions
Reduce the number of categorical levels in a predictive model
Screen variables for irrelevance using the CORR procedure
Screen variables for non-linearity using empirical logit plots
Measure Model performance
Apply the principles of honest assessment to model performance measurement
Assess classifier performance using the confusion matrix
Model selection and validation using training and validation data
Cluster Analysis
Case study on cluster Analysis
factor Analysis
Case Study on factor Analysis

3. SQL FOR ANALYST

SQL SERVER 2008
Introduction to SQL Server Concepts
Introduction to DBMS & RDBMS Concepts
SQL Introduction
Sql Commands
Data Types
Data Definition Languages
Create table command
Alter table command
Truncate table command
Drop table command
Constraints
Not Null
Unique Key
Primary key
Foreign Key
Check
Default
Data Manipulation Language
Select Command
Insert Command
Update Command
Delete command
Filter Clause
WHERE Clause
GROUP BY Clause
Having Clause
Order by clause
Operators
Arithmetic Operators
Comparison Operators
Logical Operators
Range Operators
IN/NOT IN
Between
Set Operators
Union
Union All
Intersect
Except
Identity Properties
Column and table Alias Joins
Simple join:
Non equi join
Equi join
Self join
inner join
Outer join
Left Outer join
Right Outer join
Full Outer join
Cross join
Different Types of queries
Simple Queries
Sub Queries
Nested Sub Queries
Correlated Sub Queries
Temporary tables
Common Table Expressions(CTEs)
Derived Tables
Scalar Expressions
INDEXES
VIEWS
Stored Procedures
Triggers
DDL,DML,LOGON,Triggers
Cursors
Search expressions like
Dealing with Nulls views and Derived Tables
Exercises and Real Time examples