Quick Stats
Analytics is now a gamechanger and every organization is investing in Big data for better growth and sustainability
SAS will help us enter into Analytics and build a bright future. R and Python help us adapt to new advancements towards evolution
Be it experienced professional or fresher every one of us must grow with a better vision. Adapt to Analytics R and Python for a healthy growth
Benefits
Analytics on SAS Complete programme
Knowledge of data science, predictive modeling, machine learning and Statistical Techniques algorithms with the datadriven approach
Real time data driven projects
Development of analytical and decisionmaking abilities by examining reallife case studies and coming up with strategies and decisions
Certification
Certification on SAS AnalyticsRegression and Modeling is a great valueadd for the student’s resume, which can help an individual pursue a promising career or a career growth
SAS Analytics
Industry recognized SAS AnalyticsRegression and modeling certification from SAS after completion of the program.
Career Rise
Looking for the switch, career growth, enhancement, upskill. This is the perfect module designed for the same. Take a step forward and be a part of the future drive
Who Should Attend
 Engineering and IT Students – BTech / BE, BCA, MCA, BScIT, MScIT
 Commerce & Finance Students  BCom / MCom, Economics Graduates, MBA or BBA
 Highly recommended for people aspiring for jobs that required data handling – Research, Marketing, IT Services, Big Data & more
 People who are already employed, but want to upskill themselves in the domain of Analytics
 As a prerequisite, its just your passion towards data and hard work that is the only requirement,rest is just a cakewalk.
Course Outcome
 Understanding of basic concepts and types of data
 Understanding of sampling techniques
 Data Science and Machine Learning concepts with application
 Understanding of frequency distributions and measures of central tendency, dispersion and shape
 InDepth Knowledge of the Hypothesis testing TTest ANOVA
 Indepth knowledge of Correlation and Regression
 Indepth Knowledge of Predictive modeling using Logistic Regression
 Two live projects which are full handson realtime industrial data
Curriculum

Basic ConceptsEnroll NowIntroduction to SAS toolSAS Libraries /Temporary Library/ Permanent LibraryCreating LibrariesStart with a Basic SAS programsData Step / Proc Step / Statements/ Global statementsVariables / Datatypes / properties of Variables

Access DataEnroll NowINFILE statement options to read raw data filesCreating a file reference with filename statementDATALINES statement with an INPUT statement

Starting With Raw Data(Basics)Enroll NowStyles of InputReading Unaligned Data / Understanding List InputUnderstanding Column Input / Reading Data Aligned in Columns

Formats and InformatsEnroll NowStandard Data/ Non Standard DataHow Informats and Format worksWorking with Date/Time/Datetime InformatHow and when to use Yearcutoff

Starting With Raw Data( Beyond Basics)Enroll NowFormatted Input styleUsing Modifiers

Mixing Styles of InputEnroll NowTesting a Condition before Creating an ObservationCreating Multiple Observations from a Single RecordReading Multiple Records to Create a Single Observation

PDV: How the DATA Step WorksEnroll NowWriting Basic Data StepHow SAS Processes ProgramsCompilation phaseExecution PhaseDebugging a Data StepTesting SAS Programs

Manipulating SAS DatasetsEnroll NowCreating & Modifying VariablesAssigning Values ConditionallySpecifying Lengths for VariablesSubsetting DataAssigning Permanent Labels and Formats

Grouping Statements Using DO GroupsEnroll NowAssigning Values Conditionally Using SELECT GroupsReading a Single Data SetManipulating DataUsing BYGroup ProcessingReading Observations Using Direct Access (Point= option)Detecting the End of a Data Set(end= option)Understanding How Data Sets Are Read through PDVRenaming VariablesSelecting Variables

Combining SAS Data SetsEnroll NowOnetoOne ReadingConcatenatingInterleavingMatchMergingMatchMerge ProcessingExcluding Unmatched Observations

Transforming Data with SAS FunctionsEnroll NowGeneral Form of SAS FunctionsConverting Data with FunctionsRestriction for WHERE ExpressionsManipulating SAS Date Values with FunctionsSAS Date and Time ValuesSAS Date FunctionsModifying Character Values with FunctionsModifying Numeric Values with FunctionsNesting SAS Functions

Relevant base SAS proceduresEnroll NowAppend procedureSort procedureDatasets procedurePrintto procedureFormat procedureTranspose procedureImport procedureExport procedurePrint procedureTabulate procedureReport procedureMeans procedureSummary procedureFreq procedure

Generating Data with DO LoopsEnroll NowConstructing DO LoopsIntroduction to Constructing DO LoopsDO Loop ExecutionCounting Iterations of DO LoopsDecrementing DO LoopsNesting DO LoopsIteratively Processing Data That Is Read from a Data SetConditionally Executing DO LoopsUsing Conditional Clauses with the Iterative DO StatementCreating Samples

Processing Variables with ArraysEnroll NowCreating OneDimensional ArraysUnderstanding SAS ArraysDefining an ArrayVariable Lists as Array ElementsReferencing Elements of an ArrayCompilation and ExecutionUsing the DIM Function in an Iterative DO StatementCreating Variables in an ARRAY StatementCreating Temporary Array Elements

AnalyticsAn IntroductionEnroll NowWhat is Business AnalyticsDifference between Analytics and AnalysisImportance of Analytics in IndustryApplication of Analytics in IndustryLearning and the growth curve of AnalyticsPuzzleInterview Preparation

Data and VariablesAn introductionEnroll NowWhat is StatisticsWhat is an Average Mean Median ModeDifferent types of Data and variablesBasic Statistical MeasuresPuzzleQuizCodingAn IntroductionDependent and Independent Variable

Population and SampleSampling techniquesEnroll NowWhy do we need sample over a populationDifference between population and sampleSampling TechniquesSimple Random SamplingStratified Random SamplingSampling with and without replacementPuzzleAssignment

Normal Distribution & Central Limit TheoremEnroll NowIntroduction to Bell CurveDeviation Vs Standard DeviationVariance and Standard deviationOutliers and their effects on basic statistical measuresSymmetric and Asymmetric curveBell CurveEmperical RuleGrading processQuiz

Exploratory data AnalysisEnroll NowUnivariate analysis vs Bivariate analysis Vs Multivariate analysisDecile, Percentile, Vintile, Quartile, QuantileProc Univariate DetailsMoments , Basis Statistical Measures, Test for Location, Quantiles , Extreme ObservationAssignmentPuzzle

Data VisualizationEnroll NowBox and whiskers plotRanking AlgorithmProc Rank in detailOutlier Detection Removal TreatmentMissing values How critical are they to a dataMissing value Removal and imputationProc for missing value Treatment

Hypothesis TestingEnroll NowHypothesis testing  What does that meanWhat is Hypothesis and what we do we testNull Hypothesis and Alternate HypothesisSignificance level and Confidence level P value and α valueDecision making Reject/Fail to Reject Null HypothesisConfidence IntervalAccuracy and ErrorType I Error and Type II Error

Campaign ManagementEnroll NowTest and Control groupSolicit and Non SolicitResponder and Non ResponderTargets and Non TargetsMeasuring Cost/Revenue/Profit of a campaign2*2 profit/revenue contingency matrixActual Vs predicted conflictsAccuracy Error Sensitivity SpecificityPuzzleInterview preparation

One Sample TTestEnroll NowOne Sample Ttest What is the hypothesis for this testOne Sample T Test using proc ttestOne Sample T Test using proc univariateSides of a test One sample two sided, Upper tail, lower tailHow to apply Ttest to the dataHow will this help in making a decisionGraphical interpretation of T testPuzzleAssignmentAssumptions to a 1 sample T Test

Two sample Ttest & Sides of a testEnroll NowDifference between a one sample and a two sample TTestHypothesis for a T TestProc T Test for 2 sample T TestUnderstanding the results from the statistical point of viewPlotting the graph for visualizing TTestSides of 2 sample T Test2 sided, Lower tail and upper tailApplication of 2 Sample T TestPuzzleQuizAssumptions to 2 sample TTEst

AnovaAnalysis of VarianceEnroll NowWhy do we need AnovaWhy cant we use a 3 Sample TTEstHypothesis Testing for AnovaAssumptions to Anova1 way Anova vs N Way AnovaPerforming 2 sample T test using AnovaCoefficient of determinationDegree of FreedomLevenes Test and F testInteraction  Type 1 and Type III SSBalanced Vs Unbalanced designProc AnovaProc GLMPuzzleInterview Preparation

AnovaPost Hoc Analysis TestEnroll NowWhy apply Posthoc Test when applying AnovaControlled Design experimentExperimental Error RateMultiple Comparison TestReferential comparisonTukey and Dunnett Test Sides of a testDiffogram and Control PlotMeans Vs LS MeansPuzzleMock Test

CorrelationDifferent typesEnroll NowWhat is CorrelationWhy do need correlation analysis in any other analysisHow to measure correlationPearson and Spearman CorrelationCorrelationHypothesis TestProc CorrCorrelation MatrixCorrelationgraphical representation and InterpretationPuzzleCase Study

Regression Exploring the algorithmEnroll NowSimple Linear Regression and Multiple Linear RegressionRegressionHypothesis TestingDegree of FreedomAnova table for RegressionWhat is Ordinary least squareParameter Estimate and InterceptSignificant and Non Significant VariablesRemoving RedundancyCollinearity VIFPuzzleAssignmentDiscussion

Regression  Model BuildingEnroll NowRegression Model BuildingR square and adjusted R square2 way honest assessment3 way honest assessmentHow to split Training validation TestOversampling Undersampling Overfitting UnderfittingModel Selection techniquesvariable selection techniquesModel building Vs Model FittingModel fit statisticsPuzzleMock Test

Logistic RegressionIntroductionEnroll NowLogistic RegressionAn IntroductionLogistic RegressionNeed and the necessityWhy cant we apply regression everywhereAlgorithm to Logistic RegressionAssumption to Logistic RegressionChecking the linearity amongst variablesChecking for collinearityRemoving Non LinearityRemoving Non LinearityLogistic Regression Hypothesis testing

What are odds ratioOdds ratio vs probabilityLog oddslog Vs natural LogComplete separation Vs Quasi Complete SeparationData ConvergenceFischers TechniqueCreating and identifying the dependent variableData preparation for model building process

Logistic RegressionModel BuildingEnroll NowSampling data for Training Validation TestFine tuning Assessment and final assessmentOut of sample validationOut of time validationVariable transformationVariable reduction techniquesVariable clustering techniquesIdentifying Collinearity amongst variablesInterpretation of Results

Cumulative Lift ChartCumulative Gain ChartRelative operating characteristicsArea under the curveModel fit statisticsvalidation statisticsVariable selection techniquesSignificant and non significant variablesIdentifying the best variables for a model

Model selection techniquesParameter estimate and Intercept% Concordant, %Discordant, %ties pairsCalculating C value from the statisticsPuzzleInterview PreparationCase studyComparing training and validation statistics

SAS SQL 1: EssentialsEnroll NowIntroducing the Structured Query LanguageOverview of the SQL procedureSpecifying columnsSpecifying rows

Displaying Query ResultsEnroll NowPresenting dataSummarizing data

SQL JoinsEnroll NowIntroduction to SQL joinsInner joinsOuter joinsComplex SQL joins

SubqueriesEnroll NowNoncorrelated subqueriesInline views

Set OperatorsEnroll NowIntroduction to set operatorsUnion operatorOuter Union operatorExcept operatorIntersect operator

Creating Tables and ViewsEnroll NowCreating tables with the SQL procedureCreating views with the SQL procedure

Advanced PROC SQL FeaturesEnroll NowDictionary tables and viewsUsing SQL procedure optionsInterfacing PROC SQL with the macro language

SAS Macro LanguageEnroll NowIntroductionGetting Familiar to the macro facility

Macro VariablesEnroll NowIntroduction to macro variablesAutomatic macro variablesMacro variable referencesUserdefined macro variablesDelimiting macro variable referencesMacro functions

Macro DefinitionsEnroll NowDefining and calling a macroMacro parameters

DATA Step and SQL InterfacesEnroll NowCreating macro variables in the DATA stepIndirect references to macro variablesCreating macro variables in SQL

Macro ProgramsEnroll NowConditional processingParameter validationIterative processingGlobal and local symbol tables

Advanced SAS Programming TechniquesEnroll NowMeasuring Efficiencies

Controlling I/O Processing and MemoryEnroll NowSAS Data step processingControlling I/OUsing SAS viewsReducing the length of numeric variablesCompressing SAS data sets

Accessing ObservationsEnroll NowCreating a sample data setCreating an indexUsing an index

Using DATA Step ArraysEnroll NowIntroduction to lookup techniquesUsing onedimensional arraysUsing multidimensional arraysLoading a multidimensional array from a SAS data set

Using DATA Step Hash and Hiter ObjectsEnroll NowIntroductionUsing hash object methodsLoading a hash object with data from a SAS data setUsing the DATA step hiter object

Combining Data HorizontallyEnroll NowDATA step merges and SQL procedure joinsUsing an index to combine dataCombining summary and detail dataCombining data conditionally

Expert Programmer TechniquesEnroll NowCreating userdefined functionsThe experts’ FORMAT procedure

Understanding the R environment:Enroll NowSetting up the machine and installing RSetting up the R environment for the smooth usage of RUnderstanding the various IDEs for R developmentInstallation/Removal of packages

Importing raw dataEnroll NowReading csv files into RReading json files into RReading txt files into RReading sas7bdat files into R

Data structuresEnroll NowUnderstanding homogeneous and heterogeneous form atomic vectors in R including Dataframes, List, Vectors, Factors and Matrices in RAtomic vectors in RDataframes, List, VectorsFactors and Matrices in R

Style GuideEnroll NowCoding standardsKnow HowNotation and NamingFilenamesObject namesSyntaxCurly BracesSpacingLine lengthIndentationAssignmentCommenting Guidelines

Loops and vectorizationEnroll NowWriting For and while loops in RUnderstanding if loops are really a necessary in RUnderstand the apply family in R

Functions and conditionalsEnroll NowWriting functions in RUnderstanding if...then...else in RUnderstanding pure functions in R and understanding the purrr package in R

summarizationEnroll NowUnderstand the meaning of clusteringHierarchical clustering in RK means clustering in R

GLM FamilyEnroll NowLinear regression in RMulticollinearity and calcualtion of VIFRoot mean squared error, t statistic, pvalue and confidence intervalLogistic regression in RRoC, TPR, Lift, Gain and KS statistic in RInterpreting the Linear and logistic ModelUnderstanding ridge and lasso in linear and logistic model

Decision TreesEnroll NowDecision Trees in RUnderstanding Ginni IndexKnowing the difference between CART and CHAIDWorking with Random Forest

Optimization in REnroll NowWhat is optimization?Working with optimization packages in R (e.g Optimx)Constrained optimization ( Lagrange multipliers )

Bonus TopicEnroll NowBasics of GGPLOT2Scatterplots in RHistograms and density plots in RUnderstanding basics of "Grammar of Graphics" concept

Introduction to MLEnroll NowWhat is LearningComponents of learningA simple learning modelTypes of LearningWhat is machine learningApplication of ML

Getting started with PythonEnroll NowWhat is PythonOrigins and versions of PythonWays to run a Python programSetting up Python environmentBasic file operations with PythonBasic data operations with PythonBasic data visualization with PythonHands on with Python

Process in any MLEnroll NowBasic process flow of any machine learningTerminologies used in MLEvaluation metrics used in MLComparison of ML and Statistical learning

RegressionEnroll NowSimple Linear RegressionMultiple Linear RegressionConsideration in RegressionAccessing the accuracy of Coefficient estimatesAccessing the accuracy of ModelFun example and workshop!

ClassificationEnroll NowAn overview of classificationWhy not linear regressionLogistic ModelLinear discriminant AnalysisComparing classificationFun example and workshop!

Support Vector machinesEnroll NowIntroductionGeneral conceptsComponents of SVMRelationship with Logistic regressionFun example and workshop!

Decision TreesEnroll NowIntroductionBasics of a treeClassification with treesAdvantages / disadvantages of tree

Random ForestEnroll NowIntroductionHow Random forest worksDifferent parameters in RFFun example and workshop!

Gradient boosting machinesEnroll NowIntroductionHow GBM worksDifferent parameters in GBMFun example and workshop!

ClusteringEnroll NowIntroductionK Means clusteringHierarchical ClusteringPractical issues in clustering

Natural Language processingEnroll NowGeneral text processingTokenizingStop wordStemmingPOS taggingChunking/chinkingLemmatizingCorporaBuilding text only modelsFun example and workshop!

Neural NetworkEnroll NowIntroductionImportant concepts in NNBuilding NN for classificationFun example and workshop!

Building a recommender SystemBuilding a sales prediction modelRetrieving twitter feeds and sentiment analysis
FAQS
Is this a module or a complete course?
This is a complete course. Technically the course is Analytics on SAS for which we cover five modules SAS Base, SAS Analytics, SAS Advance, R and Python
Is this a distance or online program?
This is an instructorled workshop program, available in 2 modes – InstructorLed and selfpaced.
What is the difference between instructor led and self paced program?
In the self paced programme learners have to learn about the course on their own using the study materials provided to them after registration. There would be recorded videos which will be throughout this programme.
In the Instructorled version, learners will be provided assistance of faculty who will take them through the course as a part of classroom/ live webinar mode.
In case if I missed a class?
Every class is recorded and In case you miss a class you can go through the recordings and let us know in case of doubts. You can even sit in other regular classes for the sessions that you missed
Is there any option of facetoface classes?
Yes, this is a classroom programme. In case you are not able to make up for the class you can use the link to connect online through a webex link and can attend the same LIVE class without even coming to the class.
What is the duration of Analytics program?
Its a weekend programme comprising 40 Hours. It would take 10 weeks or 2 months to cover the module in full.
You just need to spend not more than 30 minutes a day to become a successful Analyst.
What are the speciality of this module?
Our Analytics module is aligned to current industry requirements, uses the latest tools and techniques and the curriculum of the course has been developed in consultation with industry practitioners.
Which certification will I be eligible for?
You would be eligible for the SAS certification exam conducted by SAS with global recognition.
We will conduct exam preparation classes for the same and this is included as a part of the module
Does the fees also includes the SAS certification fees
The preparation classes are complimentary and the exam needs to be booked. The exam fees are not included as a part of the programme
How is this SAS certification different from the certificates provided by other institute
SAS certification is conducted by SAS and recognized globally. Upon successful completion, your name appears on the website and anyone can check that. The certificates provided by institutes are not recognized by any organization and hence not valid
What about the projects
The projects are industrial projects with realtime data and not the mocked one. This is not a capstone project where you actually do not get a pretty Idea of how things work in Industry. It will be handson and live industrial data decisionmaking projects