“Debendra has a patient and dedicated approach to the way he teaches.He is very helpful and this course will help me get better grip on the platform.Towards the end one can relate real life business examples.”

Puneet Butan
Manager
The Royal Bank of Scotland N.V.
9th Floor, Tower – C, Cyber Greens, Sector 25 – A, Gurgaon – 122002.

sas training in delhi

“Nice experience of learning Base SAS through IISASTR institute.Got to know about SAS uses ad its flexibility.It was really wonderful to learn SAS through this institute.Keep up the Good work.”

Sunil Vaswani
The Royal Bank of Scotland N.V.
9th Floor, Tower – C, Cyber Greens, Sector 25 – A, Gurgaon – 122002.

SPSS Training in delhi

sas training in delhi

Anshuman Mirani
B.E from Delhi college of Engineering
Present Job: Analyst, GRAIL Research.

sas training in iisastr delhi

In one word what I can say is that I believed the institute and after completing the course I realized that I was wright.
INDRAJIT SAHA
M.A. (ECONOMICS)
JNU, NEW DELHI,
110067.

sas training in delhi

sas training in delhi

Raghuveer Sripada
B.E (Electical and communication)
Company: TCS : Working as SAS Analyst

sas training in delhi

Gourab Panda
MSc Integrated(Physcis)
IIT Kharagpur
Present job: TCS as SAS Analyst

I am anand modi from bhopal.I decided to shift my career path to analytics & came to know about SAS software .After visiting various institutes in Delhi, I choose IISASTR as Debendra Sir has best knowledge of the subject & is always ready to help his students .I made a lot of good friends over here, who did their Master’s & Bachelor’s from premier institutes of India like IITs, NITs. IISASTR provided me a good training & an intellectually simulating environment which is highly needed to learn SAS .I wish IISASTR all the best wishes for their future endeavors.

Anand Modi
B.Tech (NIT Trichy)
M.S. (University of Florida)

This Institute delivers best in class education in SAS and Analytics which i had experienced during my course here in base SAS and SAS in Analytics which set it Apart from other institutes in terms of quality, class and style of teaching.

Jitendra Singh
M.Tech B.Tech Dual Degree ,IIT Delhi

Got Sufficient time on terminals to practice independently and good attention by the instructor Mr Dhir to answer my queries.
The instructor Mr.dhir is a very nice and amicable person.
The Institutes environment has been very congenial.

Dr Nalin Kumar Gautam
Ph.D IIT Delhi

ravi kant sas training in delhi feedback

The faculty specially Sh. Devender Dhir is a unique person who teaches the SAS in a very pragmatic and easy form. The things become quite easy to understand after his explanation. He has a very good command over SAS and teaches in a step by step module which helps students to make a a good grip over the subject.

I recommend students looking for a training in SAS to just approach iisastr and get the real and practical exposure to SAS.

I thank you Sir for being my mentor for the subject, I wished to study.

With best Regards,
Ravi Kant Kumar
B. Tech (Mechanical Engg., Gold Medalist)
MBA(International Business)
Assistant Manager,
Apparel Export Promotion Council,
Spons. by Ministyry of Textiles,
Govt. of India.

sas training in delhi

sas training in delhi

With Regard
Gaurav Gupta
M.tech(Bio Technology)
Working in Coral Telecom
As Software Engineer

More detail visit http://www.iisastr.com

contact no:9312506496

More detail visit http://www.iisastr.com

contact no:9312506496

More detail visit http://www.iisastr.com

contact no:9312506496

Duration:30 hours

course  fee :Rs 30,000/-

More detail visit http://www.iisastr.com

contact  no:9312506496

Clinical Trial    using  SAS Syllabus

.1. Introduction & Basics

Statistics—the Field

Probability Distributions
Study Design Features

Descriptive Statistics

Inferential Statistics

2. Topics in Hypothesis Testing

Significance Levels

Power

One-Tailed and Two-Tailed Tests

p-Values

. Sample Size Determination

Multiple Testing

3. The Data Set TRIAL

Introduction

Data Collection
Creating the Data Set TRIAL

Statistical Summarization

4. The One-Sample t-Test

5. The Two-Sample t-Test

6.One-Way ANOVA

7. Two-Way ANOVA

8. Repeated Measures ANOVA

9. The Crossover Design

10.Linear Regression

11Analysis of Covariance

12. The Wilcoxon Signed-Rank Test

13. The Wilcoxon Rank-Sum Test

14. The Kruskal-Wallis Test

15. The Binomial Test

16. The Chi-Square Test

17. Fisher’s Exact Test

18. McNemar’s Test

19. The Cochran-Mantel-Haenszel Test

20. Logistic Regression

21. The Log-Rank Test

22. The Cox Proportional Hazards Model

23. Appropriateness of the Methods

24. Analysis of Strati?ed Data Introduction 1
Continuous Endpoints
Categorical Endpoints
Time-to-Event Endpoints
Tests for Qualitative Interactions

25 Multiple Comparisons and Multiple Endpoints

Single-Step Tests
Closed Testing Methods
Fixed-Sequence Testing Methods
Resampling-Based Testing Methods
Testing Procedures for Multiple Endpoints
Gatekeeping Strategies
26
Analysis of Safety and Diagnostic Data
Introduction
Reference Intervals for Safety and Diagnostic Measures
Analysis of Shift Tables
27 Interim Data Monitoring
Introduction
Repeated Signi?cance Tests
Stochastic Curtailment Tests

28 Analysis of Incomplete Data
Introduction
Case Studies

29.Data Setting and Modeling Framework
Analysis of Complete Growth Data
Simple Methods and MCAR
Available Case Methods
Likelihood-Based Ignorable Analyses

30:A Practical Guide
Multiple Imputation
The EM Algorithm
Categorical Data
MNAR and Sensitivity Analysis

More detail visit http://www.iisastr.com

contact  no:9312506496

Duration:30 hours

course  fee :Rs 21,000/-

More detail visit http://www.iisastr.com

contact  no:9312506496

Financial Research   using  SAS  Syllabus

1.INTRODUCTION
Introduction to  SAS
Working with SAS
Ground Rules
SAS Data Sets

2.   RANDOM WALKING or WALKING RANDOMLY: USING
SAS TO CONDUCT VARIANCE RATIO TESTING OF ASSET PRICES
Background for the random walk theory of Asset prices
The Data
Sample SAS code for variance Ratio Specification Testing

3   ANALYZING WINNERS AND LOSER : USING SAS TO TEST THE OVERREACTION HYPOTHESIS
Background on Behavioral issues Specially Related to Overreaction
Data Used for Analysis
Sample SAS Code Evaluating the Existence of Return Reversals

4   CROSS-SECTIONAL APPROACH TO THE EMPIRICAL TEST OF THE CAPITAL ASSET PRICING MODEL
The Data
Sample SAS

5  EVENT STUDIES
Measuring Abnormal Stock Returns
The Data

6  EFFECTIVE USE OF SAS MACROS : AN APPLICATION TO EVENT STUDIES
Alternative test Statistics in Event Studies
The Data
Sample Program
Program Listing

7  ASSOCIATION TYPES OF STUDIES: INVESTIGATING THE PRICES- EARNING RELATINSHIP
Background for Association Types of Studies
The Data
Sample SAS Code for Prices Earning Regressions
Program Listing

8  PREDICTING BANKRUPTCY FROM FINANCIAL DISTRESS CHARACTERIZATION MODELS
Background for Characterizing firms in Financial Distress
The Data
Sample SAS Program to Evaluate Financial Distress Characterization

9  USING ACCOUNTIONG INFORMATION TO FORECAST MARKET PERFORMANCE
Background for Analyzing Fundamental Accounting Information And Market Performance
The Data
Sample Program to Evaluate Financial Information and Market Performance

10  ANALYSIS OF TRANSACTION DATA
Background
The Data
Combining Identical Trades
Correcting Time Stamps and computing the Tick Test
Computing Quote Changes and combining them with Trades
Estimation of trade costs
VAR Estimation

Program Listing

More detail visit http://www.iisastr.com

contact  no:9312506496

SAS Analytics,Data analysis   using  SAS

Duration:60 hours

course  fee :Rs 30,000/-

More detail visit http://www.iisastr.com

phone no:9312506496

Course content

1. Introduction to the SAS Language

1.1 Introduction

1.2 Basic Language: Rules and Syntax

1.3 Creating SAS Data Sets

1.4 The INPUT Statement

1.5 SAS Data Step Programming Statements Their Uses

1.6 Data Step Processing

1.7 More on INPUT Statement

1.7.1 Use of Pointer controls

1.7.2 The trailing@ line-hold specifier

1.7.3 The trailing@ @ line-hold specifier

1.7.4 Uses of RETAIN statement

1.7.5 The use of line pointer controls

1.8 Using SAS Procedures

2. More on SAS Programming and some Applications

2.1 More on the DATA and PROC STEPS

2.1.1 Reading data from files

2.1.2 Combing SAS data sets

2.1.3 Saving and retrieving permanent SAS data Sets

2.1.4 User- defined in formats and formats

2.1.5 Creating SAS data sets in procedure steps

2.2 SAS Procedures for Computing Statistics

2.2.1 The UNIVARIATE procedures

2.2.2 The FREQ procedure

2.3 Some Useful Base SAS Procedures

2.3.1 The PLOT procedures

2.3.2 The CHART procedures

2.3.3 The TABULATE procedure

3. Statistical Graphics Using SAS / GRAPH

3.1 INTRODUCTINS

3.2 An INTRODUCTINS to SAS / GRAPH

3.2.1 Useful SAS / GRAPH procedures

GPLOT procedure

GCHART procedure

3.2.2 Writing SAS/Graph programs

3.3 Quantile Plots

3.4 Empirical Quantile- Quantile Plots

3.5 Theoretical Quantile- Quantile Plots or probability Plots

3.6 Profile Plots of Means or Interaction Plots

3.7 Two Dimensional Scatter Plots and Scatter Plot Matrices

3.7.1 Two –Dimensional Scatter Plots

3.7.2 Scatter plots Matrices

3.8 Histograms Bar Charts and Pie Charts

3.9 Other SAS Procedures for High- resolutions Graphics

4. Statistical Analysis of Regression Models

1. 1. An Introduction to Simple Linear Regression

4.1.1 Simple linear regression using PROC REG

4.1.2 Lack of fit test using PROC ANOVA

4.1.3 Diagnostics use of case statistics

4.1.4 Predictions of new y values using regressions

4.2 An Introduction to Multiple Regression Analysis

4.2.1 Multiple regression analysis using PROC REG

4.2.2 Case Statistics and residual analysis

4.2.3 Residual Plots

4.2.4 Examining relationships among regression variables

4.3 Types of Sums of Squares Computed in PROC REG and PROC GLM

4.3.1 Model comparison technique and extra sum of squares

4.3.2 Types of sums of squares in SAS

4.4 Subset selection using PROC REG for Model selection

4.4.1 Subset selection using PROC REG

4.4.2 Other options available in PROC REG for model selection

4.5 Inclusion of squared Terms Product terms in Regression Models

4.5.1 Including interaction terms in the model

4.5.2 Comparing slopes of regression lines using interaction

4.5.3 Analysis of models with higher-order terms with PROC REG

5. Analysis of Variance Model

5.1 Introduction

5.1.1 Treatment Structure

5.1.2 Experimental Designs

5.1.3 Linear Models

5.2 One-way Classification

5.2.1 Using PROC ANOVA to analyze one-way Classifications

5.2.2 Making preplanned (or a priori) comparisons using PROC GLM

5.2.3 Testing orthogonal polynomials using contrasts

5.3 One-Way Analysis of Covariance

5.3.1 Using PROC GLM to perform one-way covariance analysis

5.3.2 One-way covariance analysis: Testing for equal slopes

5.4 A two Factorial in a Completely Randomized Design

5.4.1 Analysis of a two-way factorial using PROC GLM

5.4.2 Residual Analysis of Interaction

5.5 Two-Way Factorial: Analysis of Interaction

5.6 Two-Way Factorial: Unequal Sample sizes

5.7 Two way Classification: Randomized Complete Block Design

5.7.1 Using PROC GLM to analyze a RCBD

5.7.2 Using PROC GLM to test for nonadditivity

6. Analysis of Variance: Random and Mixed Effects Models

6.1 Introduction

6.2 One-way Random Effects Model

6.2.1 Using PROC GLM to analyze one-way Random Effects Models

6.2.2 Using PROC MIXED to analyze one-way Random Effects Models

6.3 Two –way Crossed Random Effects Model

6.3.1 Using PROC GLM and PROC MIXED to analyze two –way Crossed Random Effects Model

6.3.2 Randomized complete block design: Blocking when treatment factors are random

6.4 Two-Way Nested Random Effects Model

6.4.1 Using PROC GLM to analyze two-way nested random effects models

6.4.2 Using PROC MIXED to analyze two-way Nested Random Effects Models

6.5 Two-way Mixed Effects Models

6.5.1 Two-way Mixed Effects Models: Randomized Complete Blocks Design

6.5.2 Two-way Mixed Effects Models: Crossed Classification

6.5.3 Two-way Mixed Effects Models: Nested Classification

6.6 Models with Random and Nested Effects for More Complex Experiments

6.6.1 Models for nested factorials

6.6.2 Models for split-plot experiments

6.6.3 Analysis of split-plot experiments using PROC GLM

6.6.4 Analysis of split-plot experiments using PROC MIXED

• fit polymonial regression models using the REG procedure
• select models based on several statistics and automatic model selection methods using PROC REG
• evaluate model fit and model assumptions using the REG, GLM, GENMOD, and UNIVARIATE procedures
• fit Poisson, negative binomial, and gamma regression models using the GENMOD procedure
• perform analysis of variance using the GLM procedure
• write CONTRAST and ESTIMATE statements in PROC GLM
• fit regression models with dummy variables using PROC REG and ANCOVA models using PROC GLM
• fit models with random effects using the MIXED procedure
• create a variety of statistical graphs.

Course  duration :30 hours

fee :Rs 15,000/-

Course Content

More  detail  visit http://www.iisastr.com

Contact :9312506496

Contact :9312506496

This course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t-tests, ANOVA, linear regression and logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum.

Duration:30 hours

Fee : Rs 15,000/-

Course content

More  detail visit http://www.iisastr.com

contact  at : 9312506496

Explore the inner workings of data mining techniques  for  Modeling   data   for   Marketing , Risk   and Customer   Relationship  Management :Using  SAS and how to make them work for you.

Learn how to

Modeling   data   for   Marketing , Risk   and Customer   Relationship  Management :Using  SAS
Who should attend

Business analysts, their managers, and statisticians

Duration:36 hours

Course Content:

1.Defining the goal,Profile analysis,Segmentation,Response,Risk

Activation,Cross sell and Upsell,Attrition,Net present value

Lifetime value

2.Choosing the modeling methodology

Liner Regression,Logistic regression,Hiring and team work

Product focus versus customer focus

3.Selecting the data sources

Source of data,Internal sources,External sources,Selecting Data for modeling

Data for prospecting,Data for customer Models,Data for Risk Models

Constructing the modeling data set,How big should my sample be?

Sampling method,

4. Preparing for data modeling

Accessing the data,Classifying data,Reading raw data

Creating the Modeling data set,Sampling,Cleaning the data

Continuous Variable,Categorical variables

5.processing and evaluating model

Processing the data,Splitting the data

Method 1: One model,Method 2:two model –response

Two model activation,Comparing method 1 and method 2

.validating the model,Implementing and maintaining the model

Implementing the modeling,Optimizing customer profitability

Retaining customers proactively
6.Understanding  your  customer :profiling  and  segmentation

What is the  importance  of understanding   your  customers?

Types  of profiling and  segmentation

RFM  analysis penetration analysis

Developing a  customer value matrix   for  a  credit

Card  company

Customer value  analysis

Performing cluster analysis   to  discover   customer   segments

Targeting  new prospects: Modeling Response

7. Avoiding High –Risk customers :Modeling Risk

Credit  scoring   and Risk Modeling

Defining  the  objectives

Preparing  the  variables

Processing  the  model

Validating the   model

Bootstrapping

Implementing the  model

Scaling the Risk score

A different  kind  of  Risk: fraud

8.Retaining  the profitable customers: Modeling churn

9.Targeting profitable   customers: Life time value

10. web mining  and  modeling

More  detail   visit http://www.iisastr.com

contact  at : 9312506496