data science using sas & r - analytixlabs · data science using sas and r is a comprehensive...

Data Science usingSAS & R

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A comprehensive, job-orientedtraining program crafted by experts

About AnalytixLabs

• Seasoned analytics professionals

• Together we have 30 + years ofexperience with prestigious firms,like McKinsey, KPMG, Deloitteand AOL

• Regular sessions by industryexperts

• Job-oriented training

• Lucrative job prospects in highgrowth domain

• Support for relevantcertifications and diplomas

• Career counseling and planning

• Value for money with high returnon investment

Faculty

Bottom line

AnalytixLabs is a capability building and training solutions firm led by McKinsey, IIM, ISB and IIT alumni with deep industry experience and aflair for coaching. We are focused at helping our clients develop skills in basic and advanced analytics to enable them to emerge as“Industry Ready” professionals and enhance career opportunities. AnalytixLabs has been also featured as top institutes by prestigiouspublications like Analytics India Magazine and Higher Education Review, since 2013.

• 80-20 focus on practical & theory

• Personal attention and Individualcounselling

• Industry best practices

• World class course structure

• Surpasses industry requirements

• Cater to Standard certifications

• High quality course material andreal life case studies

• Seasoned analytics professionals

• Together we have 30 + years ofexperience with prestigious firms,like McKinsey, KPMG, Deloitteand AOL

• Regular sessions by industryexperts

• Job-oriented training

• Lucrative job prospects in highgrowth domain

• Support for relevantcertifications and diplomas

• Career counseling and planning

• Value for money with high returnon investment

Approach

Content

McKinsey Global Institute estimates a shortage of nearly 1.7 million big data talents by2018. This includes a shortage of 140,000 to 190,000 workers with deep technical andanalytical expertise, and a shortage of 1.5 million managers and analysts equipped towork with and use big data outputs

Global Data science and Big Data skill gap

Candidates trained by us are working in leading companies acrossindustries…

Data Science using SAS and R is a comprehensive program with followingmodules, weekly assignments and case studies

• MS Excel – 6 hours + Practice exercises• Basic data handling to advanced mathematical and logical functions

• SAS Base & Advanced – 24 hours + Practice• Hands-on training on SAS & SQL for data mining & analytics reporting

Module 1

Module 2

• R Foundation – 12 hours + Practice• Hands-on training on R packages for data mining & analytics reporting

• SAS Base & Advanced – 24 hours + Practice• Hands-on training on SAS & SQL for data mining & analytics reporting

• Business Analytics using SAS & R – 33 hours• Basic and advanced analytics techniques with applicationsModule 4

Crafted by team of experts and maintains a balance between theoretical concepts and practical applications

• R Foundation – 12 hours + Practice• Hands-on training on R packages for data mining & analytics reportingModule 3

MS Excel – Basic and AdvancedTotal Duration: 6 hours

Introduction to Excel• Navigating Worksheets• Formatting and Editing Worksheet Data• Grouping Data, Subtotals and Data

Validation

Working with Formulas and Functions• Referencing Functions• Filter, Sorting, Advance filter• Conditional formatting

Creating Charts and Graphics• Simple charts• Pie charts• Dynamic charts

Using Advanced Excel features• Worksheet protection and security• Data Validation• Text to column, use of delimiter, etc

Logical functions using Excel• And, if, not, or, true, false• Concatenate, left, right, mid, len, trim,• Upper, lower, proper, exact, etc

Analyzing Data with Excel• Sum, Count, Sumifs, Countifs• Consolidating Worksheets (Vlookup, Index,

Match, Offset)• Using Pivot Tables

Data analysis toolpack• Data tables• Goal Seek

Introduction to Excel• Navigating Worksheets• Formatting and Editing Worksheet Data• Grouping Data, Subtotals and Data

Validation

Working with Formulas and Functions• Referencing Functions• Filter, Sorting, Advance filter• Conditional formatting

Creating Charts and Graphics• Simple charts• Pie charts• Dynamic charts

Using Advanced Excel features• Worksheet protection and security• Data Validation• Text to column, use of delimiter, etc

Logical functions using Excel• And, if, not, or, true, false• Concatenate, left, right, mid, len, trim,• Upper, lower, proper, exact, etc

Analyzing Data with Excel• Sum, Count, Sumifs, Countifs• Consolidating Worksheets (Vlookup, Index,

Match, Offset)• Using Pivot Tables

Data analysis toolpack• Data tables• Goal Seek

SAS – Base and AdvancedTotal Duration: 24 hours

SAS +R Edge program also availableseparately - Module 1+2+3

Introduction - Data importing - Understanding• Introduction to SAS, GUI• Concepts of Libraries, PDV, data execution etc• Building blocks of SAS (Data & Proc Steps -

Statements & options)• Debugging SAS Codes• Importing different types of data & connecting

to data bases• Data Understanding(Meta data, variable

attributes(format, informat, length, label, etc))• SAS Procedures for data

import/export/understanding(Proc import/Proccontents/Proc print, etc)

Data Manipulation• Data Manipulation steps(Sorting, filtering,

duplicates, merging, appending, subsetting,derived variables, sampling, Data typeconversions, renaming, formatting, etc)

• Data manipulation tools(Operators, Functions,Procedures, control structures, Loops, arrays,etc)

• SAS Functions (Text, numeric, date, utilityfunctions)

• SAS Procedures for data manipulation(Proc sort,proc format, Proc transpose, etc)

• SAS Options (System Level, procedure level)

Exploratory Data Analysis & Data visualization• Introduction exploratory data analysis• Descriptive statistics, Frequency Tables and

summarization• Univariate Analysis (Distribution of data &

Graphical Analysis)• Bivariate Analysis(Cross Tabs, Distributions &

Relationships, Graphical Analysis)• SAS Procedures for Data Analysis(proc freq/Proc

means/proc summary/proc tabulate/Procunivariate, etc)

• SAS Procedures for Graphical Analysis (ProcSgplot, Proc gplot, etc)

Reporting - Output Exporting• Introduction to Reporting• SAS Reporting Procedures ( Proc print, Proc

Report, Proc Tabulate etc)• Exporting data sets into different formats (Using

proc export(• Concept of ODS(output delivery system)• ODS System - Exporting output into different

formats

Optimizing SAS Codes• Introduction to Advanced SAS - Proc SQL &

Macros• Understanding select statement (From, where,

group by, having, order by etc)• Proc SQL - Data creation/extraction• Proc SQL - Data Manipulation steps• Proc SQL - Summarizing Data• Proc SQL - Concept of sub queries, indexes etc• SAS Macros - Creating/defining macro variables• SAS Macros - Defining/calling macros• SAS Macros- Concept of local/global variables• SAS Macros - Debugging techniques

Introduction - Data importing - Understanding• Introduction to SAS, GUI• Concepts of Libraries, PDV, data execution etc• Building blocks of SAS (Data & Proc Steps -

Statements & options)• Debugging SAS Codes• Importing different types of data & connecting

to data bases• Data Understanding(Meta data, variable

attributes(format, informat, length, label, etc))• SAS Procedures for data

import/export/understanding(Proc import/Proccontents/Proc print, etc)

Data Manipulation• Data Manipulation steps(Sorting, filtering,


• Data manipulation tools(Operators, Functions,Procedures, control structures, Loops, arrays,etc)

• SAS Functions (Text, numeric, date, utilityfunctions)

• SAS Procedures for data manipulation(Proc sort,proc format, Proc transpose, etc)

• SAS Options (System Level, procedure level)

Exploratory Data Analysis & Data visualization• Introduction exploratory data analysis• Descriptive statistics, Frequency Tables and



Relationships, Graphical Analysis)• SAS Procedures for Data Analysis(proc freq/Proc

means/proc summary/proc tabulate/Procunivariate, etc)

• SAS Procedures for Graphical Analysis (ProcSgplot, Proc gplot, etc)

Reporting - Output Exporting• Introduction to Reporting• SAS Reporting Procedures ( Proc print, Proc

Report, Proc Tabulate etc)• Exporting data sets into different formats (Using

proc export(• Concept of ODS(output delivery system)• ODS System - Exporting output into different

formats

Optimizing SAS Codes• Introduction to Advanced SAS - Proc SQL &

Macros• Understanding select statement (From, where,

group by, having, order by etc)• Proc SQL - Data creation/extraction• Proc SQL - Data Manipulation steps• Proc SQL - Summarizing Data• Proc SQL - Concept of sub queries, indexes etc• SAS Macros - Creating/defining macro variables• SAS Macros - Defining/calling macros• SAS Macros- Concept of local/global variables• SAS Macros - Debugging techniques

R – FoundationTotal Duration: 12 hours

Introduction - Data Importing/Exporting• Introduction R/R-Studio - GUI• Concept of Packages - Useful Packages (Base &

other packages) in R• Data Structure & Data Types (Vectors, Matrices,

factors, Data frames, and Lists)• Importing Data from various sources• Database Input (Connecting to database)• Exporting Data to various formats)• Viewing Data (Viewing partial data and full data)• Variable & Value Labels – Date Values

Data Manipulation• Data Manipulation steps (Sorting, filtering,


• Data manipulation tools(Operators, Functions,Packages, control structures, Loops, arrays, etc)

• R Built-in Functions (Text, Numeric, Date, utility)

Data Manipulation• R User Defined Functions• R Packages for data manipulation(base, dplyr,

plyr, reshape, car, sqldf, etc)

Data Analysis - Visualization• Introduction exploratory data analysis• Descriptive statistics, Frequency Tables and



Relationships, Graphical Analysis)• Creating Graphs- Bar/pie/line

chart/histogram/boxplot/scatter/density etc)• R Packages for Exploratory Data Analysis(dplyr,

plyr, gmodes, car, vcd, Hmisc, psych, doby etc)• R Packages for Graphical Analysis (base, ggplot,

lattice,etc)

SAS +R Edge program also availableseparately - Module 1+2+3

Introduction - Data Importing/Exporting• Introduction R/R-Studio - GUI• Concept of Packages - Useful Packages (Base &

other packages) in R• Data Structure & Data Types (Vectors, Matrices,

factors, Data frames, and Lists)• Importing Data from various sources• Database Input (Connecting to database)• Exporting Data to various formats)• Viewing Data (Viewing partial data and full data)• Variable & Value Labels – Date Values

Data Manipulation• Data Manipulation steps (Sorting, filtering,


• Data manipulation tools(Operators, Functions,Packages, control structures, Loops, arrays, etc)

• R Built-in Functions (Text, Numeric, Date, utility)

Data Manipulation• R User Defined Functions• R Packages for data manipulation(base, dplyr,

plyr, reshape, car, sqldf, etc)

Data Analysis - Visualization• Introduction exploratory data analysis• Descriptive statistics, Frequency Tables and



Relationships, Graphical Analysis)• Creating Graphs- Bar/pie/line

chart/histogram/boxplot/scatter/density etc)• R Packages for Exploratory Data Analysis(dplyr,

plyr, gmodes, car, vcd, Hmisc, psych, doby etc)• R Packages for Graphical Analysis (base, ggplot,

lattice,etc)

Business Analytics using SAS and RTotal Duration: 33 hours

Business Analytics program alsoavailable separately - Module 1+4

Introduction to Statistics• Basic Statistics - Measures of Central Tendencies and

Variance• Building blocks - Probability Distributions - Normal

distribution - Central Limit Theorem• Inferential Statistics -Sampling - Concept of

Hypothesis Testing• Statistical Methods - Z/t-tests( One sample,

independent, paired), Anova, Correlations and Chi-square

Introduction to Predictive Modeling• Introduction to Predictive Modeling• Types of Business problems - Mapping of Techniques• Different Phases of Predictive Modeling

Data Preparation• Need of Data preparation• Data Audit Report and Its importance• Consolidation/Aggregation - Outlier treatment - Flat

Liners - Missing values- Dummy creation - VariableReduction

• Variable Reduction Techniques - Factor & PCAAnalysis

Segmentation• Introduction to Segmentation• Types of Segmentation (Subjective Vs Objective,

Heuristic Vs. Statistical)

Segmentation• Heuristic Segmentation Techniques (Value Based, RFM

Segmentation and Life Stage Segmentation)• Behavioural Segmentation Techniques (K-Means Cluster

Analysis)• Cluster evaluation and profiling• Interpretation of results - Implementation on new data

Decision Trees• Decision Trees - Introduction - Applications• Types of Decision Tree Algorithms• CHAID Vs. CART• Decision Trees - Validation• Overfitting - Best Practices to avoid• Implementation of Solution

Linear Regression• Introduction - Applications• Assumptions of Linear Regression• Building Linear Regression Model• Understanding standard metrics (Variable significance, R-

square/Adjusted R-square, Global hypothesis ,etc)• Validation of Models (Re running Vs. Scoring)• Standard Business Outputs (Decile Analysis, Error

distribution (histogram), Model equation, drivers etc.)• Interpretation of Results - Business Validation -

Implementation on new data

Logistic Regression• Introduction - Applications• Linear Regression Vs. Logistic Regression Vs. Generalized

Linear Models• Building Logistic Regression Model• Understanding standard model metrics (Concordance,

Variable significance, Hosmer Lemeshov Test, Gini, KS,Misclassification, etc)

• Validation of Logistic Regression Models (Re running Vs.Scoring)

• Standard Business Outputs (Decile Analysis, ROC Curve,• Probability Cut-offs, Lift charts, Model equation, Drivers,

etc)• Interpretation of Results - Business Validation -


Time Series Forecasting• Introduction - Applications• Time Series Components( Trend, Seasonality, Cyclicity

and Level) and Decomposition• Classification of Techniques(Pattern based - Pattern less)• Basic Techniques - Averages, Smoothening, etc• Advanced Techniques - AR Models, ARIMA, etc• Understanding Forecasting Accuracy - MAPE, MAD, MSE,

etc

Introduction to Statistics• Basic Statistics - Measures of Central Tendencies and

Variance• Building blocks - Probability Distributions - Normal

distribution - Central Limit Theorem• Inferential Statistics -Sampling - Concept of

Hypothesis Testing• Statistical Methods - Z/t-tests( One sample,

independent, paired), Anova, Correlations and Chi-square

Introduction to Predictive Modeling• Introduction to Predictive Modeling• Types of Business problems - Mapping of Techniques• Different Phases of Predictive Modeling

Data Preparation• Need of Data preparation• Data Audit Report and Its importance• Consolidation/Aggregation - Outlier treatment - Flat

Liners - Missing values- Dummy creation - VariableReduction

• Variable Reduction Techniques - Factor & PCAAnalysis

Segmentation• Introduction to Segmentation• Types of Segmentation (Subjective Vs Objective,

Heuristic Vs. Statistical)

Segmentation• Heuristic Segmentation Techniques (Value Based, RFM

Segmentation and Life Stage Segmentation)• Behavioural Segmentation Techniques (K-Means Cluster

Analysis)• Cluster evaluation and profiling• Interpretation of results - Implementation on new data

Decision Trees• Decision Trees - Introduction - Applications• Types of Decision Tree Algorithms• CHAID Vs. CART• Decision Trees - Validation• Overfitting - Best Practices to avoid• Implementation of Solution

Linear Regression• Introduction - Applications• Assumptions of Linear Regression• Building Linear Regression Model• Understanding standard metrics (Variable significance, R-

square/Adjusted R-square, Global hypothesis ,etc)• Validation of Models (Re running Vs. Scoring)• Standard Business Outputs (Decile Analysis, Error

distribution (histogram), Model equation, drivers etc.)• Interpretation of Results - Business Validation -


Logistic Regression• Introduction - Applications• Linear Regression Vs. Logistic Regression Vs. Generalized

Linear Models• Building Logistic Regression Model• Understanding standard model metrics (Concordance,

Variable significance, Hosmer Lemeshov Test, Gini, KS,Misclassification, etc)

• Validation of Logistic Regression Models (Re running Vs.Scoring)

• Standard Business Outputs (Decile Analysis, ROC Curve,• Probability Cut-offs, Lift charts, Model equation, Drivers,

etc)• Interpretation of Results - Business Validation -


Time Series Forecasting• Introduction - Applications• Time Series Components( Trend, Seasonality, Cyclicity

and Level) and Decomposition• Classification of Techniques(Pattern based - Pattern less)• Basic Techniques - Averages, Smoothening, etc• Advanced Techniques - AR Models, ARIMA, etc• Understanding Forecasting Accuracy - MAPE, MAD, MSE,

etc

Course completion and career assistance

What is included in career assistance?

• Post successful course completion, candidates can seekassistance from AnalytixLabs for profile building. A teamof seasoned professionals will help you based on youroverall education background and work experience. Thiswill be followed by interview preparation along withmock interviews (if required)

• Job referrals are based on the requirements we get fromvarious organizations, HR consultants and large pool ofAnalytixLabs’ ex-students working in various companies.

• No one can truthfully provide job guarantee, particularlyfor good quality job profiles in Analytics. However, mostof our students do get multiple interview calls and goodcareer options based on the skills they learn duringtraining. For this there will be continuous support fromour side for as long as required.

Course completion & Certification criteria

• You shall be awarded an AnalytixLabs certificate onlypost the submission and evaluation of mandatory courseproject work. These will be provided as a part of thetraining.

• There is no pass/fail for these assignments and projects .Our objective is to ensure that trainees get strong hands-on experience so that they are well-prepared for jobinterviews along with performance at their jobs.

• Incase the assignments and projects are not up-to-the-mark, trainees are welcome to take help and support forimprovisation.

• While weekly schedule is shared with trainees for regularassignments, candidates get 3 months, post coursecompletion, to submit their final assignment andprojects.

• Post successful course completion, candidates can seekassistance from AnalytixLabs for profile building. A teamof seasoned professionals will help you based on youroverall education background and work experience. Thiswill be followed by interview preparation along withmock interviews (if required)

• Job referrals are based on the requirements we get fromvarious organizations, HR consultants and large pool ofAnalytixLabs’ ex-students working in various companies.

• No one can truthfully provide job guarantee, particularlyfor good quality job profiles in Analytics. However, mostof our students do get multiple interview calls and goodcareer options based on the skills they learn duringtraining. For this there will be continuous support fromour side for as long as required.

• You shall be awarded an AnalytixLabs certificate onlypost the submission and evaluation of mandatory courseproject work. These will be provided as a part of thetraining.

• There is no pass/fail for these assignments and projects .Our objective is to ensure that trainees get strong hands-on experience so that they are well-prepared for jobinterviews along with performance at their jobs.

• Incase the assignments and projects are not up-to-the-mark, trainees are welcome to take help and support forimprovisation.

• While weekly schedule is shared with trainees for regularassignments, candidates get 3 months, post coursecompletion, to submit their final assignment andprojects.

Time and investment

SAS + R + Business Analytics: 80 hours + Practice, INR 30,000 + 15% ST / $1200 (foreign nationals)

SAS + R + Business Analytics (self-paced): 80 hours + Practice, INR 25,000 + 15% ST / $900 (foreign nationals)

R + Business Analytics: 48 hours + Practice, INR 25,000 + 15% ST / $750 (foreign nationals)

Business Analytics: 33 hours + Practice, INR 20,000 + 15% ST / $600 (foreign nationals)

Timing: 6 hours per weekend live training (Saturday & Sunday 3 hours each) + Practice

Training mode: Fully interactive live online class /Class room (In Gurgaon center only)(In addition to the above, you will also get access to the recordings for future reference and self study)

Components: Learning Management System access for courseware like class recordings - study material, Industry-relevant project work

Certification: Participants will be awarded a certificate on successful completion of the stipulated requirementsincluding an evaluationBase SAS Global Certification: $180 (optional)SAS Business Analyst Global Certification: $180 (optional)

SAS + R + Business Analytics: 80 hours + Practice, INR 30,000 + 15% ST / $1200 (foreign nationals)

SAS + R + Business Analytics (self-paced): 80 hours + Practice, INR 25,000 + 15% ST / $900 (foreign nationals)

R + Business Analytics: 48 hours + Practice, INR 25,000 + 15% ST / $750 (foreign nationals)

Business Analytics: 33 hours + Practice, INR 20,000 + 15% ST / $600 (foreign nationals)

Timing: 6 hours per weekend live training (Saturday & Sunday 3 hours each) + Practice

Training mode: Fully interactive live online class /Class room (In Gurgaon center only)(In addition to the above, you will also get access to the recordings for future reference and self study)

Components: Learning Management System access for courseware like class recordings - study material, Industry-relevant project work

Certification: Participants will be awarded a certificate on successful completion of the stipulated requirementsincluding an evaluationBase SAS Global Certification: $180 (optional)SAS Business Analyst Global Certification: $180 (optional)

We provide trainings both in ‘fully interactive live online’ and classroom*mode

Savescommuting timeand resources intoday’s chaotic

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Fully interactivelive online class

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Contact Us

Visit us on: http://www.analytixlabs.in/

For course registration, please visit: http://www.analytixlabs.co.in/course-registration/

For more information, please contact us: http://www.analytixlabs.co.in/contact-us/

Or email: [email protected]

Call us we would love to speak with you: (+91) 9555219007

Join us on:

Twitter - http://twitter.com/#!/AnalytixLabs

Facebook - http://www.facebook.com/analytixlabs

LinkedIn - http://www.linkedin.com/in/analytixlabs

Blog - http://www.analytixlabs.co.in/category/blog/

Visit us on: http://www.analytixlabs.in/

For course registration, please visit: http://www.analytixlabs.co.in/course-registration/

For more information, please contact us: http://www.analytixlabs.co.in/contact-us/

Or email: [email protected]

Call us we would love to speak with you: (+91) 9555219007

Join us on:

Twitter - http://twitter.com/#!/AnalytixLabs

Facebook - http://www.facebook.com/analytixlabs

LinkedIn - http://www.linkedin.com/in/analytixlabs

Blog - http://www.analytixlabs.co.in/category/blog/

data science using sas & r - analytixlabs · data science using sas and r is a comprehensive...

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