January 3rd, 2012
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Time series data analysis using R training in delhi
Syllabus:
MODELS FOR STATIONARY TIME SERIES
General Linear Processes
Moving Average Processes
Autoregressive Processes
The Mixed Autoregressive Moving Average Model
MODELS FOR NONSTATIONARY TIME SERIES
Stationarity Through Differencing
ARIMA Models
Constant Terms in ARIMA Models
MODEL SPECIFICATION
Properties of the Sample Autocorrelation Function
The Partial and Extended Autocorrelation Functions
Specification of Some Simulated Time Series
Nonstationarity
PARAMETER ESTIMATION
The Method of Moments
Least Squares Estimation
Maximum Likelihood and Unconditional Least Squares
Properties of the Estimates
Illustrations of Parameter Estimation
Bootstrapping ARIMA Models
MODEL DIAGNOSTICS
Residual Analysis
Overfitting and Parameter Redundancy
Day-4
FORECASTING
Minimum Mean Square Error Forecasting
Deterministic Trends
ARIMA Forecasting
Prediction Limits
Forecasting Illustrations
Updating ARIMA Forecasts
Forecast Weights and Exponentially Weighted
Moving Averages
Forecasting Transformed Series
SEASONAL MODELS
Seasonal ARIMA Models
Multiplicative Seasonal ARMA Models
Nonstationary Seasonal ARIMA Models
Model Specification, Fitting, and Checking
Forecasting Seasonal Models
TIME SERIES REGRESSION MODELS
Intervention Analysis
Outliers
Spurious Correlation
Prewhitening and Stochastic Regression
TIME SERIES MODELS OF
HETEROSCEDASTICITY
Some Common Features of Financial Time Series
The ARCH(1) Model
GARCH Models
Maximum Likelihood Estimation
Model Diagnostics
Conditions for the Nonnegativity of the
Conditional Variances
Some Extensions of the GARCH Model
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January 3rd, 2012
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Time Series data analysis using SAS training in Delhi
Syllabus:
Session 1 Overview of Time Series
1.1 Introduction
1.2 Analysis Methods and SAS/ETS Software
1.2.1 Options
1.2.2 How SAS/ETS Software Procedures Interrelate
1.3 Simple Models: Regression
1.3.1 Linear Regression
1.3.2 Highly Regular Seasonality
1.3.3 Regression with Transformed Data
Session 2 Simple Models: Autoregression
2.1 Introduction
2.1.1 Terminology and Notation
2.1.2 Statistical Background
2.2 Forecasting
2.2.1 Forecasting with PROC ARIMA
2.2.2 Backshift Notation B for Time Series
2.2.3 Yule-Walker Equations for Covariances
2.3 Fitting an AR Model in PROC REG
Session 3 The General ARIMA Model
3.1 Introduction
3.1.1 Statistical Background
3.1.2 Terminology and Notation
3.2 Prediction
3.2.1 One-Step-Ahead Predictions
3.2.2 Future Predictions
3.3 Model Identification
3.3.1 Stationarity and Invertibility
3.3.2 Time Series Identification
3.3.3 Chi-Square Check of Residuals
3.3.4 Summary of Model Identification
3.4 Examples and Instructions
3.4.1 IDENTIFY Statement for Series
3.4.2 Example: Iron and Steel Export Analysis
3.4.3 Estimation Methods Used in PROC ARIMA
3.4.4 ESTIMATE Statement for Series
3.4.5 Nonstationary Series
3.4.6 Effect of Differencing on Forecasts
3.4.7 Models for Nonstationary Data
3.4.8 Differencing to Remove a Linear Trend
3.4.9 Other Identification Techniques
Session 4 The ARIMA Model
4.1.1 Introduction to Seasonal Modeling
4.1.2 Model Identification
4.2 Models with Explanatory Variables
4.2.1 Case 1: Regression with Time Series Errors
4.2.2 Case 1A: Intervention
4.2.3 Case 2: Simple Transfer Fun
4.2.4 Case 3: General Transfer Function
4.2.5 Case 3A: Leading Indicators
4.2.6 Case 3B: Intervention
4.3 Methodology and Example
4.3.1 Case 1: Regression with Time Series Errors
4.3.2 Case 2: Simple Transfer Function
4.3.3 Case 3: General Transfer Functions
4.3.4 Case 3B: Intervention
Session 5 The ARIMA Model: Special Applications
5.1 Regression with Time Series Errors and Unequal Variances
5.1.1 Autoregressive Errors
5.1.2 Example: Energy Demand at a University
5.1.3 Unequal Variances
5.1.4 ARCH, GARCH, and IGARCH for Unequal Variances
5.2 Cointegration
5.2.1 Introduction
5.2.2 Cointegration and Eigenvalues
5.2.3 Impulse Response Function
5.2.4 Roots in Higher-Order Models
5.2.5 Cointegration and Unit Roots
5.2.6 An Illustrative Example
5.2.7 Estimating the Cointegrating Vector
5.2.8 Intercepts and More Lags
5.2.9 PROC VARMAX
5.2.10 Interpreting the Estimates
5.2.11 Diagnostics and Forecasts
Session 6 State Space Modeling
6.1 Introduction
6.1.1 Some Simple Univariate Examples
6.1.2 A Simple Multivariate Example
6.1.3 Equivalence of State Space and Vector ARMA Models
6.2 More Examples
6.2.1 Some Univariate Examples
6.2.2 ARMA(1,1) of Dimension 2
6.3 PROC STATESPACE
6.3.1 State Vectors Determined from Covariances
6.3.2 Canonical Correlations
6.3.3 Simulated Example
and Pure Delay
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January 3rd, 2012
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Data Analysis and Business Modeling Using Excel 2010
Syllabus :
1 What’s New in to Excel 2010
? Customizable Ribbon
? Sparklines
? Slicers
? PowerPivot
? New Excel Solver
? File Tab
? New Excel Functions
? New Equation Editor
? Improved Data Bars
? Paste Special Live Preview
2 Range Names
How Can I Create Named Ranges?
3 Lookup Functions
4 The INDEX Function
5 The MATCH Function
6 Text Functions
7 Dates and Date Functions
8 Evaluating Investments by Using Net Present Value Criteria
9 Internal Rate of Return
10 More Excel Financial Functions
11 Circular References
12 IF Statements
13 Time and Time Functions
14 The Paste Special Command
15 Three-Dimensional Formulas
16 The Auditing Tool
17 Sensitivity Analysis with Data Tables
18 The Goal Seek Command
19 Using the Scenario Manager for Sensitivity Analysis
20 The COUNTIF, COUNTIFS, COUNT, COUNTA, and COUNTBLANK Functions
21 The SUMIF, AVERAGEIF, SUMIFS, and AVERAGEIFS Functions
22 The OFFSET Function
23 The INDIRECT Function
24 Conditional Formatting
25 Sorting in Excel
26 Tables
27 Spin Buttons, Scroll Bars, Option Buttons, Check Boxes, Combo Boxes, and Group List Boxes
28 An Introduction to Optimization with Excel Solver
29 Using Solver to Determine the Optimal Product Mix
30 Using Solver to Schedule Your Workforce
31 Using Solver to Solve Transportation or Distribution Problems
32 Using Solver for Capital Budgeting
33 Using Solver for Financial Planning
34 Using Solver to Rate Sports Teams
35 Warehouse Location and the GRG Multistart and Evolutionary Solver Engines
Understanding the GRG Multistart and Evolutionary Solver Engines
36 Penalties and the Evolutionary Solver
37 The Traveling Salesperson Problem
38 Importing Data from a Text File or Document
39 Importing Data from the Internet
40 Validating Data
41 Summarizing Data by Using Histograms
42 Summarizing Data by Using Descriptive Statistics
43 Using PivotTables and Slicers to Describe Data
44 Sparklines
45 Summarizing Data with Database Statistical Functions
46 Filtering Data and Removing Duplicates
47 Consolidating Data
48 Creating Subtotals
49 Estimating Straight Line Relationships
50 Modeling Exponential Growth
51 The Power Curve
52 Using Correlations to Summarize Relationships
53 Introduction to Multiple Regression
54 Incorporating Qualitative Factors into Multiple Regression
55 Modeling Nonlinearities and Interactions
56 Analysis of Variance: One-Way ANOVA
57 Randomized Blocks and Two-Way ANOVA
58 Using Moving Averages to Understand Time Series
59 Winters’s Method
? Time Series Characteristics
? Parameter Definitions
? Initializing Winters’s Method
? Estimating the Smoothing Constants
60 Ratio-to-Moving-Average Forecast Method
61 Forecasting in the Presence of Special Events
62 An Introduction to Random Variables
63 The Binomial, Hypergeometric, and Negative Binomial Random Variables
64 The Poisson and Exponential Random Variable
65 The Normal Random Variable
66 Weibull and Beta Distributions: Modeling Machine Life and Duration of a Project
67 Making Probability Statements from Forecasts
68 Using the Lognormal Random Variable to Model Stock Prices
69 Introduction to Monte Carlo Simulation
70 Calculating an Optimal Bid
71 Simulating Stock Prices and Asset Allocation Modeling
72 Fun and Games: Simulating Gambling and Sporting Event Probabilities
73 Using Resampling to Analyze Data
74 Pricing Stock Options
75 Determining Customer Value
76 The Economic Order Quantity Inventory Model
77 Inventory Modeling with Uncertain Demand
78 Queuing Theory: The Mathematics of Waiting in Line
79 Estimating a Demand Curve
80 Pricing Products by Using Tie-Ins
81 Pricing Products by Using Subjectively Determined Demand
82 Nonlinear Pricing
83 Array Formulas and Functions
84 PowerPivot
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December 15th, 2011
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Audience: This course is designed for the beginner to intermediate-level Tableau user. It is for
anyone who works with data – regardless of technical or analytical background. This course is
designed to help you understand the important concepts and techniques used in Tableau to
move from simple to complex visualizations and learn how to combine them in interactive
dashboards.
Prerequisites: None
Learning Objectives: At the end of this class, the student will be able to:
* Understand the many options for connecting to data.
* Understand the Tableau interface / paradigm – components, shelves, data elements, and
terminology. The student will be able to use this knowledge to effectively create the most
powerful visualizations.
* Create basic calculations including string manipulation, basic arithmetic calculations, custom
aggregations and ratios, date math, logic statements and quick table calculations.
* Able to represent your data using the following visualization types:
o Cross Map
o Scatter Plots
o Geographic Map
o Pie Charts and Bar Charts
o Page Trails
o Small Multiples
o Heat Map
o Dual Axis and Combo Charts with different mark types
o Density Chart
o Options for drill down and drill across
* Use Trend Lines, Reference Lines and statistical techniques to describe your data.
* Understanding how to use group, bin, hierarchy, sort, set and filter options effectively.
* Work with the many formatting options to fine tune the presentation of your visualizations.
* Understand how and when to Use Measure Name and Measure Value.
* Understand how to deal with data changes in your data source such as field addition, deletion or
name change.
* Understand all of your options for sharing your visualizations with others.
* Combine your visualizations into Interactive Dashboards and publish them to the web.
Course Includes: This course will include extensive hands-on activities to re-enforce the skills and knowledge attained.
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November 21st, 2011
QlikView Designer I Version 10
Duration: 12 hours
Recommended Prerequisites: No special prerequisites
Who Should Attend: Business Analysts, Designers, Project Managers, Developers & End-Users
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Course Objectives
By the end of this course, you will be able to:
* Incorporate best practices for application design and layout in the development of QlikView applications.
* Build a QlikView application using sheets and some of the common QlikView objects such as list boxes and table boxes.
* Search and analyze data in existing QlikView applications.
Course Outline
* Introduction to QlikView
* Layout and design fundamentals and best practices
* What to consider when designing a QlikView application
* Definitions and uses of: sheets, sheet objects, list boxes and table boxes
* How to create charts in QlikView
* Chart properties, how to edit the format and layout of charts
* How to load a simple list of data into QlikView
Course Objectives
By the end of this course, you will be able to:
* Incorporate best practices for application design and layout in the development of QlikView applications.
* Build a QlikView application using sheets and some of the common QlikView objects such as list boxes and table boxes.
* Search and analyze data in existing QlikView applications.
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Tags: Business Analysts, Designers, Developers & End-Users, Project Managers, QlikView training in delhi, QlikView training in delhi chennai, QlikView training in delhi ncr, QlikView training in delhi pune, QlikView training in Gurgaon, QlikView training in hyderabad, QlikView Training in India, QlikView training in mumbai, QlikView training in noida
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November 20th, 2011
One of our client Fortune 100 Best Companies to work for(SAS India,pune) looking for SAS Programmer(4 nos):Permanent Position.Experience: Minimum 2-5 years of experience in BASE SAS,SAS/SQL,SAS/Macros. Banking domain preferred
Pl send your resume to hr@iisastr.com
Detail JD:
SAS Programmer(1): Contract position of 4-6 months.
Experience: Minimum 2+ years of experience in BASE SAS,SAS/SQL,SAS/Macros.
Job Location: Pune. Looking to have this person on board by December’11 first week. Urgent requirement.
2) SAS Programmer(4 nos):Permanent Position.
Experience: Minimum 3-5 years of experience in BASE SAS,SAS/SQL,SAS/Macros.
Job Location: Pune
Pl send your resume to hr@iisastr.com
Banking domain preferred for both positions.
Pl send your resume to hr@iisastr.com
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November 1st, 2011
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SAS project for segmentation cluster analysis
ABC Mobile phone Company project,where you have to prepare Mobile device usages report.You have analyze segmentation of Indian user for smart phone and Tablet PC using Factor and cluster analysis (usage cluster by ABC market segments) for different parameter like Os used by smart phone(ex:Google android,Samsung BADA,RIM Black Berry,Apple iOs),social and fun,application downloaded,App categories used daily,most frequently used app categories,Value added services… .
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October 28th, 2011
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Banking Segmentation
A consumer bank wants to segment its customers based on historic activity patterns. Segmentation is used for improving contact strategies in the marketing department. In this project, you transform variables, create segments, profile segments, and score the results of the segementation analysis.
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October 21st, 2011
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Project name :Credit Risk
A bank wants to use performance on an in-house subprime credit product to create an updated risk model that could be combined with other factors to make future credit decisions. In this project, you partition data, impute missing values, transform variables in using several different functions, run several stepwise regression analysis, and run a neural network analysis. Finally you will compare the models to choose which model is best for final implementation.
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Tags: Credit Risk, Credit Risk modelling using sas enterprise miner, Credit Risk sas project, sas live project, sas live project in delhi ncr, sas live project in gurgaon, sas live project in noida, sas project, SAS project Credit Risk, sas project in credit risk
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October 20th, 2011
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Time series data analysis using SAS broadly will cover following
Topics:
1. Modeling physical systems/processes from operational data
2. Time series models as tools for system analysis
3. Forecasting the system behavior based on system dynamics
4. Continuous stochastic systems
5. Forecasting control to alter a system’s performance
6. Trends and seasonality
7. Multiple series
8. Applications of time series analysis
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