orangetree global bi and ba program march and april 2013

Authorized Business Analytics Training Partners of

Program Brochure Business Analytics & Business Intelligence February to April 2013

SAS (48 Hours)

This Module comprises of Base SAS (30 Hours) and Advanced SAS (18 Hours)

The program initiates with Core Analytics (16 Hours) followed by Advanced Analytics (40 Hours)

Analytics (56 Hours)

Advanced Excel -VBA (20 Hours)

20 Hours of VBA Macros an understanding of which is crucial in Analytics

For more information Please mail us at [email protected] or call us on +91 9051563222

Business Analytics and Business Intelligence Module

For professionals with or without work experience aiming at careers in the Analytics Industry 140 Hours Rs 32000 + Tax

+ Career Aid

SPSS Predictive Analytics (16 Hours)

A 12 Hour Module focusing on Resume Building, Interview Skills and Technical Round Questions

A 16 Hour Module focusing on Predictive Analytic Techniques on SPSS

“..OrangeTree Global is currently one of the country’s leading organizations in the area of Business Analytics and Intelligence Training…. A majority of the professionals enrolling for the program are looking for work that would be intellectually stimulating, provide clear career growth along with an attractive pay-package…

OrangeTree Global provides a tailor made program relevant to the market requirements. Companies from Bangalore and Kolkata regularly approach the organization for the hiring of professionals …”

– Economic Times 7th August 2012

2 Authorized Business Analytics Training Partners of

SAS Analytics ensures bright careers for bright people

1

Analytics has Concrete Advantages for India

Although analytics has been around for a long while, it wasn’t until the last 5 to 10 years that its importance in the business field has been realized. It was in the last 10 years that technology has been revolutionized and we now produce about 2.5 quintillion bytes of data every day. This is more data than that was collected in two years, previously. What has also changed in the last decade is that we now have the means to sift through these 2.5 quintillion bytes of data in a reasonable amount of time. All these changes have major implications for organizations today.

In organizations, analytics enables professionals to convert extensive data and statistical and quantitative analysis into powerful insights that can drive efficient decisions.

Therefore with analytics organizations can now base their decisions and strategies on data rather than on gut feelings. Moreover with the rate at which this data can be analyzed, organizations are able to keep tabs on the customer trends in

2

near real time. As a result effectiveness of a strategy can be determined almost immediately. Thus with powerful insights, analytics promises reduced costs and increased profits.

The analytics Industry is one of the fastest growing in modern times with it poised to become a $50 billion market by 2017. With this sudden surge in the analytics industry there is a tremendous increase in the demand for analytics expertise across all domains, throughout all major organizations across the globe. It has been predicted that by 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions. (McKinsey Global Institute).


3

Train with one of the most trusted Analytics

Training Organization in India.

It has been the most exhilarating year. We have successfully placed 85% of all aspiring Analytics professionals who have come to us looking for a rewarding career in this field. Our students are now working in companies such as HSBC Analytics, Genpact, Mu Sigma, Capgemini, TCS, Cognizant, PwC, Deloitte, IBM, Mahindra Satyam and Infosys. © Orangetree Business Solutions Pvt Ltd.

We have tied up with some of the country’s top university’s in delivering workshops and awareness programs and are grateful to leading educational institutes such as IIM Calcutta, Calcutta University, Jadavpur University, Presidency College, St. Xavier’s College, Rabindra Bharati University.

We are the official training partners of some of India’s top financial and Analytics organizations.

We are proud to be associated with IBM as their Business Analytics Training Partners. We are the only organization in Eastern India to specialize in SAS Analytics.

Orangetree Global welcomes you to one of India’s foremost programs in Analytics and Business Intelligence

Facts & Figures

IBM's recent study revealed that “83% of Business Leaders listed Business Analytics as the top priority in their business priority list.”

Deloitte has mentioned in its study that Decision makers who can leverage everyday data & information into actionable insights for the growth of their organization by taking reliable decisions, will find themselves in a much better position to achieve strategic growth in their Career

“There is an information overload in today’s world and data analytics helps to cut out the clutter to help businesses make safe and smart choices,” said the Deloitte global MD

A recent report by Nucleus Research found that companies realize a return of USD10.66 for every dollar they invest in analytics. www.ibm.com

In the developed economies of Europe, government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone by using big data, not including using big data to reduce fraud and errors and boost the collection of tax revenues. McKinsey Global Institute

A retailer using big data to the full has the potential to increase its operating margin by more than 60 percent. McKinsey Global Institute

4

Placements

We have successfully placed Professionals in HSBC Analytics, RBI, Cisco, HDFC, McKinsey, PwC, TCS, Accenture, Vodafone, Genpact, Nokia, Glaxo, HP, Citi, Airtel, Mu Sigma, Deloitte, Novartis, Infosys, Wipro, Standard Chartered, Dell, Tesco, Symphony, Cognizant, Dell, Capgemini and IBM.

Advantages of Training with OrangeTree ü Authorized Partners of IBM ü Updated course programs in tune with

field requirements ü Case Study Centric Methodology of

delivery ü Highly recommended by the country’s

leading Analytics Companies. ü Weekday and Weekend Batches for

working professionals ü Exclusive Resume Building and Interview

Skills Modules under ‘Career Aid’ ü Post Program Support for all students ü Exhaustive Alumni Network across the


` Base SAS

30 Hours

3

• Generate listing reports using Print and Report procedures.

• Descriptor portion and data portion of a data set

• Creating descriptor portion using Contents procedure and use of keywords like nods, _all_, position, short.

• Generate Summary reports and frequency Distribution using FREQ procedure.

• Descriptive statistics of numeric variables using MEANS, SUMMARY, UNIVARIATE and TABULATE procedure.

• Creating formats using Format Procedure. • Use of Transpose procedure to

manipulate SAS data sets. • Enhance the quality of the reports

through the use of labels, SAS Formats, titles, footnotes and various default reporting options.

SAS Functions to manipulate SAS Data

• SAS date Functions to manipulate date variables in a data set.

• Use of SAS functions to manipulate Character and Numeric data.

• SAS functions to convert character data to numeric data and vice versa.

2

• Use of Where, If and When conditional

statements to create subsets from a data set.

• Use of And, Or, In, Contains logical operators and the sign of inequalities.

• Separate out data sets based on suitable conditions using SAS conditional statements © Orangetree Business Solutions Pvt Ltd.

Import and Export data sets using SAS

• Import various type of data sets like Text files (.txt, .csv), Excel files (.xls) Etc. using SAS codes.

• Import data sets using the SAS Import Wizard.

• Export SAS data sets into different type of file like Text files (.txt, .csv), Excel files (.xls) Etc.

• Generate HTML, RTF and PDF reports outside SAS environment using ODS statement.

1

• What is SAS Enterprise Guide? • SAS Enterprise Guide Projects • User interface • Process flow • Active Data sets • Introduction to SAS Libraries and SAS

Syntax © Orangetree Business Solutions Pvt Ltd.

• Types of variables in SAS • Creating temporary and permanent SAS

data sets • Introduction to INFORMATS, FORMATS,

LENGTH, LABEL, RENAME statements • Copying SAS data sets from one library to

another library • Creating subsets of a data set using

FIRSTOBS, OBS, KEEP, DROP.

Introduction to SAS Enterprise Guide

Introduction to key concepts on SAS Data Sets and Data Comprehension

Creating Data Sets and Variables based on conditionality.

Import and Export data sets using SAS

Generating Different Reports Using SAS Proc

SAS Functions to manipulate SAS Data Sets and Variables


4

• Appending two or more SAS data sets using data step and Append procedure.

• Sort observations in SAS data sets in the specified order of magnitude, Nodupkey and Nodup

• Merging two or more data sets in data step.

•

Reading Raw Data Sets

• Use of INFILE statement options when reading raw data file

• Use of various components like line pointer controls, trailing @ controls for reading raw data files.

• Reading missing values by using MISSOVER, DSD option and PAD option while reading raw files.

Continued… Base SAS

30 Hours

Combining SAS Data Sets

5

• Do loop statements in SAS data step • Use of Nested do loops • Use of Do while and Do until loop • Concept of SAS Array and use of Array in

SAS • Dimension of an Array, Array elements,

introduction to temporary array and the use of it. © Orangetree Business Solutions Pvt Ltd.

Error handling and debugging

• Identify and resolve programming and logic errors

• Recognizing and correcting SAS syntax errors

Reading Raw Data Sets

Process SAS data sets using Do loops and Array

Error handling and debugging

SAS is the largest independent vendor in the business intelligence market and has 2,100 employ-ees. SAS is ploughing back 24% of the $2.7 billion revenue it made in 2011 to stay ahead in the analytics space. As for Wipro, business ana-lytics and information manage-ment contributes 6.6% to overall revenues and has around 8,000 em-ployees under this practice. It re-cently snapped up Australian based analytics firm, Promax which specializes in trade promotion analyt-ics for the food and beverage sector for $36 million. According to Wipro, consumer focused companies spend up to 15-20% of their annual sales on trade promotions and they are increasingly using analytics to im-prove effectiveness. Wipro plans to extend Promax's product offering to clients in other industries like re-tail, banking and insurance.


Advanced SAS

18 Hours

Structured Query Language (SQL) in SAS using Proc SQL

SAS Macros

1

• Introduction to Structured Query Language in SAS.

• Advantages of using Proc SQL over Traditional SAS Codes

• Introduction to Relational Data bases. • Creating new variables using Proc SQL. • Use of select statement to display

Column headings from a table. • Creating outputs and new tables using

Proc SQL statement. • Selecting Duplicate/unique values. • Use of Calculated option, label, format

option in Proc SQL. • Writing query for sorting a report and

Data sets in a specified order of magnitude.

• Remerging, remerging for totals. • Compare solving a problem using the

SQL procedure versus using traditional SAS programming techniques.

• Use of Customized formula for Calculations and creating subsets of the data sets.

• Using other conditional operators like

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Any, All, Between – And, Contains, Exists, In, Is Null or is Missing, Like, =*(tests for a value that sounds like a specified value).

• Use of Case expression on Select statement.

• Application of Where clause, Having Clause.

• Construct sub queries within a PROC SQL step Combining Queries with Set Operators.

• Introduction to SQL Joins and a comparison between SAS merge and SQL join.

• Use of Inner Join, Left Join, Right Join, Full join.

• Creating and updating tables using Proc SQL.

• Editing observations and Data table management like Updating Data Values, deleting rows, Altering columns, deleting a table, in a Table using Proc SQL.

• Creating, Describing, Updating and Deleting view using Proc SQL

• Creating macro variables with Proc SQL. © Orangetree Business Solutions Pvt Ltd.

• Getting started with Macro facility • Introduction to SAS programs and

Macro Processing • Generating SAS Codes with Macro

Language • Defining and Calling Macros • Introduction to Macro parameters and

the concept of Positional and Keyword parameters.

• Introduction to Macro Variables and the Concept of Global and Local Macro variables.

• Defining Arithmetic and Logical expressions in SAS Macro.

• Evaluation of Arithmetic and Logical expressions in SAS Macro.

• Macro functions. • Interfaces with the SQL Procedure. • Introduction to Storing and Reusing

Macros • Writing Efficient Macro


Core ANALYTICS on SAS

16 Hours

1

• What is Data Mining? • Direct and Indirect Data Mining. • Models in Data Mining. • Basic introduction to Prediction and

Profiling.

Developing a basic understanding of the data: Introduction to data types and data dictionary.

Qualitative data

• Tabular summary: One – way and two – way frequency distribution.

• Graphical summary using SAS/GRAPH: Introduction to Bar graph, Pie graph, and Line graph.

2

Quantitative data

• Tabular summary: One – way and two – way frequency distribution, relative frequency, percent frequency, cumulative frequency.

• Graphical summary using SAS/GRAPH introduction to Histogram, Box plots, Line graph, Stem and leaf diagram, Quantile Plots or Probability Plots Scatter diagram.

• Introduction to various measures of Central tendencies: Mean, Median, Mode.

• Introduction to the measures of dispersion: Range, Mean deviation, Semi inter - quartile range, Standard deviation.

• Various moment measures: Skewness and Kurtosis.

• Missing value and Outlier treatment: Various methods and application using SAS Codes.

• Concept of classical theory of probability.

• Concepts of probability Mass Functions

Data Mining

Exploratory Data Analysis

Data Summarization techniques

Descriptive Statistics of the variables using Proc Univariate Procedure

Discrete Probability distribution

Continuous Probability distribution

3

and Probability Density Functions • Concepts of Expectations • Use of Discrete and Continuous Random

variables and examples and introduction

• Binomial Distribution and Binomial plot using PROC GPLOT procedure in SAS.

• Poisson distribution and Poisson plot using Proc GPLOT procedure in SAS.

• Application of Binomial and Poisson distribution in Analytics with real life examples.

• Normal Distribution and Standard Normal Distribution Normal plot using Proc GPLOT procedure in SAS.

• Application of Normal distribution in Analytics with real life examples. © Orangetree Business Solutions Pvt Ltd.


4

• Concept of Population and Sample • Use of PROC SURVEYSELECT procedure in

SAS. Introduction to Some important terminologies

• Parameter and Statistic. • Properties of a good estimator • Standard Deviation and Standard Error. • Point and Interval Estimation. • Confidence level and level of

Significance. • Constructing Confidence Intervals. • Formulation of Null and Alternative

hypothesis and performing simple test of Hypothesis.

5

• Performing test of one sample mean, difference between two group means (independent sample) and difference between two group means (Paired sample) using Proc TTEST in SAS.

• Performing Chi - square tests: Test of Independence and Test of Goodness – of – Fit using PROQFREQ procedure in SAS.

• Performing one-way ANOVA with PROC ANNOVA and PROC GLM procedure

• Performing post-hoc multiple comparisons tests in PROC GLM using Tukey’s mean test.

• Performing two-way ANOVA with and without interactions. © Orangetree Business Solutions Pvt Ltd.

Introduction to Sampling and Sampling Distribution

T-Tests, Chi–Square Tests & Analysis of Variance

Problem of Estimation and Testing of Hypothesis

Continued. ..Core ANALYTICS on SAS

16 Hours

Hungry for more!

IBM and Cognizant have already snapped up Analytics enterprises to increase their presence in the space. IBM has spent $16 billion on acquiring more than 25 analytics companies since 2005, and it's projecting $16 billion in an-alytics revenue by 2015. Cognizant bought Market Rx in 2007 for $135 million, its largest acquisition till date, to get a head start in the Analytics segment. MarketRx's propri-etary analytics software platform was helping pharma companies im-prove their marketing performance among other things, and came with revenues of around $40 million at the time of the acquisition. The acquisition gave Cognizant access to the top 20 pharma companies in the US at that time, and enterprise analytics continues to be a focus area. "Enterprise analytics will be one of the key growth drivers in the next few years," says R Chandrasekhar, group chief executive, technology and operations, Cognizant.


6

• Translating a business problem in to a

Statistical problem. • Importance of building robust model:

model stability issue. • Choosing the set of right explanatory

variables. • Splitting the sample to create Training,

Validation and hold out test set towards creating a balanced model set.

• Data manipulation and preparing the data for the model.

• Assessing the model fit by means of the model fit statistics.

Correlation and Linear Regression

• Introduction to Pearson’s Correlation coefficient using PROC CORR procedure.

• Correlation and Causation • Fitting a simple linear regression model

with the Proc REG procedure. • Understanding the concepts of Multiple

Regression. • Using automated model selection

techniques in PROC REG to choose the

7

best model from among several competing models.

• Interpretation of the model: overall fit of the model and fining out the influential variables.

Linear Regression diagnostics • Examining Residuals • Assessing Collinearity, Heteroskedasticity

and Auto – Correlation. © Orangetree Business Solutions Pvt Ltd.

• Comparison between Liner Regression and Logistic Regression

• Performing Logistic regression using Proc Logistic Procedure in SAS.

• Performing Goodness of fit of the model: Introduction to Percent Concordant, AIC, SC, and Hosmer – Lemeshow, Receiver Operating Characteristics (ROC) Curve and Area under Curve (AUC).

• Interpretation of the model: overall fit of the model and fining out the influential variables using Odds ratio criteria.

8

• Using automated model selection techniques in PROC Logistic to choose the best model from among several competing models using AIC criterion.

• Introduction to Cluster Analysis and various techniques: Hierarchical and Non – Hierarchical Clustering techniques.

• Using Hierarchical Clustering by Proc Tree procedure in SAS.

• Performing K – means Clustering in SAS – Divisive Clustering, Agglomerative Clustering.

• Application Cluster Analysis in Analytics with Examples with profiling of the clusters and interpretation of the clusters.

Advanced ANALYTICS on SAS

40 Hours

Data Mining Methodology

Correlation and Linear Regression

Introduction to categorical data analysis and Logistic Regression

Introduction to Segmentation Techniques: Cluster Analaysis


9

• Introduction to Factor Analysis and various techniques: Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA).

• Application of Factor Analysis using Proc Factor procedure: KMO MSA test, Bartlett’s Test Sphericity, The Mineigen Criterion, Scree plot.

• Introduction to Factor Loading Matrix and various rotation techniques like Varimax and Promax.

Orangetree Business Solutions Pvt Ltd.

10

• What is Time series Analysis? • Objectives and Assumptions of Time

Series. • Identifying pattern in Time series data:

Decomposition of the time series data and general aspect of the analysis

• Introduction to Various Smoothing techniques: Simple Moving Average, Weighted Moving Average, Exponential Smoothing, Holt’s Linear Exponential Smoothing

• Examples of Seasonality and detecting Seasonality in Time series data.

• Introduction to Proc Forecast to generate forecast for time series data.

• Autoregressive models and Stepwise Autoregression (STEPAR) procedure.

• Autoregressive and Moving Average models and Introduction to Box Jenkins Methodology.

• Introduction to Autoregressive Moving Average (ARMA) model and Autoregressive Integrated Moving Average (ARIMA) model.

• Building an ARIMA Model.

Contd..Advanced ANALYTICS on SAS

40 Hours

Introduction to Time Series Analysis

Introduction to Segmentation Techniques: Factor Analysis

How is Advanced Analytics helping?

If you are a bank trying to find out which of your customers is most likely to default on a loan, predictive analysis finds patterns and correlations between data sets using statistics and complex mathematical models, customers who are always late on their credit card payment and don't have a stable savings patterns are most likely to default and so forth. "But prediction is not a pure science," says N Veeraraghavan, Senior Vice President and Global Practice Head, Data warehousing, BI and performance management, at Cognizant. "It gives you a proba-bilistic view of things. Sometimes, there could be 5-6 iterations (the process of repeating a sequence of operations, each building on the one preceding it, to reach the de-sired degree of accuracy) before we arrive at the solution." "Companies have put a lot of work into systems and process engineer-ing," says KR Sanjiv, Senior Vice President-Analytics and Information Management services, Wipro Technologies. "Analytics is the next lever with which companies can prop up their key performance indicators,


1

• Introduction to Microsoft Excel. • GUI of Excel 2007 • Understanding Excel Data Format • Current Region Property • Simple Spreadsheet calculations • Simple Worksheet Functions • User Defined Ranges • Customizing Spreadsheets • Freeze Pane

Concept of Object

• Object as an element in programming • Examples of objects • Collection of objects • Object properties • Methods related to objects

Introduction to Excel Visual Basic Editor

• Excel VBA programming environment • Project window, properties window

and code window

• Concept of Sub Routines

2

• Types of Errors • Excel Macro Recording facility • Modifying the existing Macro in VB

editor window • Understanding the Macro Security

and saving a workbook with Macro contents

• Adding new module, removing module, Importing and Exporting objects

Commonly used objects

• Worksheet Object • Range Object • Row Object • Column Object • Cell Object

Some Useful range methods

• The Select method • Copy and Paste method • Clear method • Delete method.

Multiple tasks on same object

• Use of ‘With ... End With Structure’

Concept of Variables

• Variable Naming Conventions • Numeric and Non-numeric variables

3

Decision Statements

• ‘If … then’ structure • ‘Select ... Case’ structure

Loops

• ‘For … Next’ structure • ‘For… Step… Next’ structure • Nested for loop • Exiting a for loop • For Each Loop • Calling a macro • Practice of good programming • Concept of Functions • Structure of a function • Uses of writing a function • Difference between a macro and a

function • Some Examples

© Orangetree Business Solutions Pvt Ltd.

Visual Basic for Application

20 Hours

Introduction to VBA

Programming Basics Excel Basics


4

Applications

• Filtering data using Auto Filter and Advanced Filter

• Advanced VBA data processing techniques for multi worksheets and multi workbooks

• Advanced techniques for going into a folder for workbooks and then opening them using VBA

• Advanced VBA programming techniques for creating multiple data sheets from a single sheet base on some pre specified conditions

• Automation of Slicing and Dicing of large data sets into multiple copies using VBA Macro codes with the help of suitable case studies © Orangetree Business Solutions Pvt Ltd.

5

• Uses of Dashboard in Business Intelligence

• How to ideate a dashboard • Relation between User defined Ranges

and Dashboards

Introduction to User Forms

• Working with user forms • Using active X controls like, text box,

combo box, check box, command buttons, etc.

• Checks in application • Creating Excel Database • Use of Offset Property • Using Active X controls on the

worksheet

Continued… Visual Basic for Applications

20 Hours

Why Use Excel VBA

Microsoft Excel is an extremely powerful tool that you can use to manipulate, analyze, and present data. It has a rich set of features in the standard Excel user interface. But you might want to find an easier way to perform a mundane, repetitive task, or to perform some task that the user interface does not seem to address. Visual Basic for Applications (VBA) is a programming language that gives you the ability to extend those applications. Learning to program might seem intimidating, but once you have learned some VBA, it becomes much easier to learn a whole lot more—so the possibilities here are limitless.

By far the most common reason to use VBA in Excel is to automate repetitive tasks. VBA is not just for repetitive tasks though. You can also use VBA to build new capabilities into Excel. For example, you could develop new algorithms to analyze your data, and then use the charting capabilities in Excel to display the results. You can perform tasks that integrate Excel with other Office applications such as Microsoft PowerPoint. In addition to all the obvious tasks that involve lists and accounting, developers use Excel in a

Dashboards on Excel


SPSS – Predictive Analytics 16 Hours

SPSS

SPSS is a good first statistical package for people wanting to perform quantitative research in social science, because it is easy to use, and because it can be a good starting point to learn more advanced statistical packages. SPSS has been around since the late 1960s. Of the major packages, it seems to be the easiest to use for the most widely used statistical techniques.

It has been continuously successful because the software does such a good job of making predictions, and the SPSS people could always figure out what they should do next. So the practical application of the software has always been to attempt to predict the future. Predictive models are used on business data to identify both risk and opportunities. Relationships among many factors are analyzed to guide decision makers in selecting from a number of possible actions.

1

In

• What is SPSS • SPSS and Data Analysis • Versions of SPSS • SPSS Environment

• Entering Data into the Data Editor • Creating a Variable • Saving Files • Retrieving Files

• The ‘Variable View’ • Attributes of a Variable • Missing Values • Changing the Column Format

• Creating Coding Variables • Recode into Same Variables • Recode into Different variables • Compute Variables

2

• Bar Chart • Pie Chart • Histogram

• Tree Diagram of Output • Related Analysis

• Numeric format • Date-Time format • Identifying Duplicates • Restructuring Data • Aggregating Data

• Merging Files Analyzing Multiple Response • Defining Multiple Response Data • Analyzing Frequency • Analyzing Cross Tabs • Relationship Between Variables

Introducing IBM SPSS Statistics

Familiarization with the SPSS Data Editor

Summarizing Individual Variables

Modifying Data Values

Graphical Presentation of Data

Understanding Output in the Viewer

Helpful Data Management Features

Analyzing Multiple Response Questions


Continued… SPSS and Predictive Analytics

SPSS needs to be complimented by...

Your understanding of Statistical procedures…

3

• Chi Square test of Independence • Independent Samples T • Paired Sample T Test • Z Test for Column proportion • One way Anova • Two Way Anova

Bivariate Plots and Correlation for Scale Variables • Using Scatter Plots to understand

the association between two variables

• Matrix Scatterplot • Correlation Coefficients • Partial Correlation • Doing Simple Regression on SPSS • Interpreting a Simple Regression

on SPSS • Example of a Multiple Regression

Model • Methods of Regression • Assessing the Regression Model • Multiple Regression using SPSS • Interpreting Multiple Regression

4

• Assessing the Model: The Log-Likelihood Statistic, R and R2

• Assessing the Contribution of Predictors: The Wald Statistic

• Methods of Logistic Regression: Forced Entry Method, Stepwise Methods

• Running the Analysis • Interpreting Logistic Regression Introduction to Time Series Analysis

• Time Series Model building using

SPSS

• What is a Decision Tree • Understanding the Theoretical

background of CHAID • Running CHAID on SPSS • Interpreting the output

Statistical Tests on SPSS

Bivariate Plots & Correlation for Scale Variables

Introduction to Regression Analysis

Introduction to Logistic Regression

Introduction to Time Series Analysis

“…OrangeTree Global presents a state of the art Program in Business Analytics and Business Intelligence. So much so that it is currently the only one of its kind in Eastern India. They are IBM Partners in the delivery of Business Analytics Training Modules…

The impressive tie-ups with academic organizations and corporates, assures one of the kind of quality and professionalism one should and can expect from such training organizations…

-Economic Times 7th August 2012

Introduction to CHAID Decision Tree


Your resume is your first entry point.

Learn how to catch the attention of the

Analytics Industry. Post this session we assure

you of a whole new profile of companies

approaching you.

Learn how to tilt the interview in your

favor. Aligning your needs with the

Organization.

Analytics is all about guiding a client

towards better and profitable business.

Frequent Client meets require you to

have good communication

skills.

Throughout the duration of an

interview, there is a series of nonverbal indicators that are

closely paid attention too. This sometimes reveal

much more about a prospective

candidate that his or her resume.

The Tipping Point. Your ability to apply Analytics on SAS will

ensure your entry into this domain. Our

question bank from students who have

secured jobs in some of the country’s top

companies will show guide you.

OrangeTree Global presents Is your CV designed to win over a recruiter? Are you designed to win over a recruiter? We at OrangeTree Global would like to give you a little more guidance and support! Welcome to Career-Aid!

Program Highlights

Communication Skills

+ CAREER-AID

A Career Support Initiative

Resume Architecture

Interview Skills Communication Techniques

Non-Verbal Communication

Guiding Technical

Interview Rounds

`

Our Offices OrangeTree Business Solutions Pvt Ltd HB 206 Sector III Salt Lake Kolkata 7000106 (Closed on Tuesday) OrangeTree Business Solutions Pvt Ltd Office 1 &15 2nd Floor Bhakti Plaza Braman Chowk Aundh Pune 411 027 (Closed on Thursday) 033 40041432 033 40041497 91 90515 63222 Kolkata 91 90284 63222 Pune [email protected] www.orangetreeglobal.com

Forthcoming Batches Kolkata

- 9th March 2013 - 28th March 2013 - 6th April 2013 Pune

- 16th February 2013 - 2nd March 2013 - 23rd March 2013 - 7th April 2013 Program Fees The Cost of the Program is Rs32000 + 12.36% ST = Rs35955/- Payment Option 1 A one-time payment of entitles the candidate to RS1000/- discount Payment Option 2 (Installment) Rs 8000/- Registration Rs 16,000/- 1st Day of the First Class Rs 11,955/- PDC 1st of next month

Some useful links to help you get started! • What is Analytics? • What is SAS? • What is the duration of the Program?

What are the Batch Timings? • 17,000 people are associated with

OrangeTree Global. Find out Why! • What is VBA? • I would like to know more about

OrangeTree Global and IBM? • When is the next available Batch? • How do I get to know about latest

Jobs and Updates in the Analytics Industry?

orangetree global bi and ba program march and april 2013

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