SAS Data AnalystTraining Program

21,347+ Participants | 10,000+ Brands | 1200+ Trainings | 45+ Countries

[Since 2009]

In exclusive association with

Training partner for

[Since 2009]

In exclusive association with

26,500+ Participants | 10,000+ Brands | 1900+ Trainings | 55+ Countries

Training Partner for

Salient Features

Programmers

Course Highlights

Govt. of India(Vskills Certified Course)

3 Hrs/Week Live Instructor-Led Online Sessions

Lifetime Access toUpdated Content and

Videos

Industry andAcademia Faculty

3 Weeks of Project Work

Active Q/A Forum

Placement Support

Class Labs/Home Assignment (10 hours/Week Learning Time)

Individual Attention to Each Learner

Industry’s TopSAS Advisors

Top Data AnalyticsTools Covered

Industry Relevant Curriculum Hands-on Approach Money Back GuaranteeCareer Mentoring

This Course is for

Non-Programmers

Internal Competitions with Prizes

Course Advisors and Instructors

Course Advisors

Shweta GuptaVice President, Tech.

Shweta Gupta has 19+ years of Technology Leadership experience. She holds a patent and number of publications in ACM, IEEE and IBM journals like Redbook and developerWorks.

Manas Garg heads the Analytics for Marketing at Paypal. He takes Data Driven Decisions for Marketing Success.

Vishal is a Technology Influencer and CEO of Right Relevance. (A platform used by millions for content & influencer discovery)

Manas GargArchitect

Vishal MishraCEO & Co-Founder

Course Advisors and Instructors

Course Instructors

Shweta GuptaVice President, Tech.

Shweta Gupta has 19+ years of Technology Leadership experience. She holds a patent and number of publications in ACM, IEEE and IBM journals like Redbook and developerWorks.

Manas Garg heads the Analytics for Marketing at Paypal. He takes Data Driven Decisions for Marketing Success.

Vishal is a Technology Influencer and CEO of Right Relevance. (A platform used by millions for content & influencer discovery)

Manas GargArchitect

Vishal MishraCEO & Co-Founder

Rohit Kumar is a Big Data Researcher with publications in many prestigious international conferences. He has 6 plus years experience in industry and expertise in various pro-gramming languages including Java, Scala, C++, Python, and Haskel. He works in variety of different database systems such as MySQL, Microsoft SQL, and Oracle Coher-ence and in many Big Data systems like Hadoop, Apache Spark, Apache Storm, Kafka, MongoDB.

Shweta GuptaVice President, Tech.Pritesh SrivastavaData Analyst (Contractor)

Shweta GuptaVice President, Tech.

Dilnoor SinghConsulting CTO, Venera.io

Dilnoor Singh is SAS Base and SAS Advanced Certified Professional

having a total of 6 years of IT industry experience. The first 3 years were

with pharmaceutical and Insurance Consulting firms and in the later 3,

he has built 2 startups in web and mobile domains.As an Analyst in the

pharmaceutical industry, he designed SAS Solutions for Measuring the

Comparative effectiveness of drugs in US population and Creating

pricing models for drug launches in the European Markets. Post that in

the auto insurance industry his work involved creating SAS Predictive

models on insuree’s risk factors and telematics data.

Rohit Kumar is a Big Data Researcher with publications in many prestigious international conferences. He has 6 plus years experience in industry and expertise in various pro-gramming languages including Java, Scala, C++, Python, and Haskel. He works in variety of different database systems such as MySQL, Microsoft SQL, and Oracle Coher-ence and in many Big Data systems like Hadoop, Apache Spark, Apache Storm, Kafka, MongoDB.

Shweta GuptaVice President, Tech.

Aakash Gupta

Aakash Gupta is an experienced SAS Certified Data Integration Developer and Base Programmer with 3.5 years of experience in Analysis, Design, Development and Testing of complex distributed systems. This experience spans from working in data development, solution designing and SAS platform administration in Windows and Unix based SAS 9.3 multi-tier Architecture in Insurance domain.

Introduction to Data Analytics

An introduction topic to understand the drivers to data analytics field and its ecosystem.

This SAS course is thoughtfully designed to allow learners with both technical as well as non-technical background to make a transition into the analytics industry with the correct skillsets. It is designed in a way that post completion of the program, learners be prepared to devise solutions for real-time problems in the industry through SAS.

The course covers all the topics from SAS Base Programming Certification major coverage from the SAS Advanced Programming and essential procedures and techniques from SAS Statistical Business Analysis: Regression and Modeling

Course Curriculum

Introduction to Data Analytics with SAS

SAS Base Programming

This section lays the foundation to the SAS Base Programming, starting with the basics about SAS and its architecture. These topics will help connecting with the technology for a holistic understanding. It will cover the underlying function of data step; manipulation of data through various functions and methods available in SAS. Learners will learn manipulation of data through various functions and methods available in SAS, exploring the limits of SAS and think of challenges. The important topics of SAS Procedures and error handling will be covered in depth.

Functions in SASTransposing DataCharacter and Numeric FunctionsConverting Variable TypeReading Formatted InputDo Loop ProcessingConditional Do Loop ProcessingUsing SAS ArraysSAS Array Processing

Managing DataIntroduction to SAS Procedures

SAS Procedures to Probe, Analyse and ReportThe Anatomy of a Proc

The Proc StatementGeneral Purpose Proc: Proc SortProbing Datasets: Proc ContentsProbing Datasets: Proc Datasets

Horizontally Merging the DataData Merge

Understanding SAS Procedures

Proc FreqProc MeansProc Tabulate

SAS Procedures used for Analysing Data

Proc PrintProc Transpose

Proc CompareProc AppendProc Options

Important Procs, Error Control and ODS

What is SAS? Architecture of SAS and ServersUnderstanding SAS StatementsSubmitting a SAS ProgramSAS Program SyntaxAccessing SAS LibrariesUsing SAS Formats

SAS ArchitectureUnderstanding PDV and IB

Options and StatementsWriting Observations

Writing to Multiple DatasetsReading Excel Data, Raw Files, Database Data

Reading from other SAS datasets

Reading and Accessing Data

Introduction to Macro LanguageAutomatic Macro VariablesDefining User Defined MacrosAutomatic vs User Defined Macro VariablesHow SAS Processes Macro VariablesDisplaying Macro Variable Values in the SAS LogMasking Special CharactersUsing SAS Functions with Macro Variables

SAS Macro Processing - I

Using Macro Variables and Macro ProgramsCreating Macro Variables During DATA Step ExecutionCreating Macro Variables During DATA Step ExecutionObtaining Macro Variable Values Creating Macro ProgramsUsing Macro ParametersUnderstanding Symbol TablesProcessing Statements ConditionallyProcessing Statements Iteratively

SAS Macro Processing - II

IntroductionUnderstanding how the SAS Supervisor checks a jobUnderstanding how SAS Processes ErrorsDistinguishing types of errors .SAS recognizes four kinds of Errors:Errors: Syntax Errors | Execution-time Errors | Data Errors | Semantic ErrorsDiagnosing ErrorsDiagnosing Syntax ErrorsDiagnosing Data ErrorsUsing a Quality Control Checklist

Handling ErrorsIntroduction

HTML, Pdf and Postscript, Rtf Files, Microsoft Excel

The Output Delivery System (ODS)

This section will cover accessing data using SQL and SAS Macro Processing, which is an extension of advance SAS where learners will learn about macros and macro variables which is an integral part of SAS Programming.

Introduction to SAS's Version of SQLIntegrity ContraintsPerforming CRUD Operations on DataCreating New Datasets and Inserting DataReading from DatasetsPerforming Queries on DatasetsExecuting Advanced Queries on DatasetsCombining Datasets Horizontally and VerticallyUpdating Data in DatasetsDeleting Data and Deleting DatasetsDictionary Tables

Accessing Data Using SQL

SAS Advanced Programming

Statistics in SASVariable TypesVariable TransformationsMeasurement ScalesMeasures of Central TendencyMeasures of DispersionShape: SkewnessShape: KurtosisSamplingCorrelation and CausationMulticollinearityHypothesis Testing

Introduction to Statistics

Explolatory Data AnalysisScatterplotHistogram

BoxPlot

Proc sgPlotBar Chart

Line ChartsScatter Plot

Stacked ColumnBubble Charts

Cycle Plots

SAS Visualization

Regression TechniquesLogistic RegressionLinear RegressionEssential DifferencesMathematical foundation

SAS Modelling

Data PreparationData CollectionModeling and Validation SplitEDD

Data Prep: Outlier TreatmentCapping and Flooring TechniqueSmoothing TechniquesSigma ApproachRobust Regression TechniqueMahalanobis Distance Technique

Data Prep: Missing Value TreatmentAssign Missing Values with ZEROAssign Missing Values with MEDIANAssign Missing Values with MEANAssign Missing Values with MODE

Post Outlier TreatmentIdentify Unrequired VariablesReformating Variables

Model DemostrationsProc RegProc Logistic

SAS Modelling Continued

SAS Statistical Analysis: Regression and Modeling , SAS Visualization

This section will introduce to the topics of statistics and application of statistical analysis in regression and modelling, with Visualization techniques.

The Capstone project is the culminating assignment that will allow you to have an integrated experience of the program. The approach to this project is to think, define, design, code, test and tune your solution, in such a way that you apply all aspects of the data analytics process.

PaySim Dataset simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.

Our Course Participants Work at

The Placement Process

Tools Covered

Placement ServicesWe partner with 10+ organizations who directly source their Data Analytics manpower needs from us. From resume creation to helping you crack the final interview, our dedicated place-ment team is always on toes to connect talent with the right opportunity.

The Candidates resume is refined and polished as per Market Standards to help them be search-able.

The Candidates are prepared for an initial quiz and a coding test.

Finally, the candidates are prepared for the final round of interview.

The Resume is shared with relevant organisations by our

placement team.

Capstone Projects (3 Weeks)

Duration Fee Batch Options

Rs. 34,900+GST Weekend17 Weeks

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- Naresh Mehta AVP – Data Science & Analytics ,

-Ajay Ohri Data Scientist,

“ ”Good to see Digital Vidya becoming increasingly more involved in covering data science vertical, look forward to collaborate with DV to help shape this industry.

“ ”Yes, I like the huge investment Digital Vidya is doing to create the next generation of talent. Initial feedback suggests Digital Vidya produces high-quality Data Analysts.

Industry Experts Speak

-Madhu Vadlamani Lead Analytics,

“ ”I can see a good course structure and well-designed syllabus for those who are passionate enough to enter into the analytics world. The platform helps people grow professionally and in very less time.

rthis Speak

What Makes us Proud?

-Vani Ananthamurthy(Business Operations Senior Analyst, Accenture)

“ ”I was looking for customized content and I found the same in Digital Vidya. Content is structured and well planned. Classes were very interactive and trainer’s presentation skills were very good. People who are new to the subject can also understand clearly. Thank you so much!

-Nanddeep Nasnodkar (Sr. Software Developer - Remote Software Solutions)

“ ”This course gets you started from very basics, makes you think and solve the assignments, and suddenly you find yourself doing Data Analytics all by yourself!