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inteligeneit@gmail.com Tiwary
Last Update May 1, 2024
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Learn SAS, R, and Python with InteliGenes Classroom and Online SAS + R + Python Training And Certification Courses In Delhi.
Analytics is the most demanding career and SAS is the most widely used tool. With this, we combine the power of Data Science with R and Machine Learning on Python for an evolving volatile market.SAS helps you in the entry while R & Python helps you grow adapt and evolve successfully.
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 require 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, it’s just your passion for data and hard work that is the only requirement, the 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 Concepts
 Introduction to SAS tool
 SAS Libraries /Temporary Library/ Permanent Library
 Creating Libraries
 Start with a Basic SAS programs
 Data Step / Proc Step / Statements/ Global statements
 Variables / Datatypes / properties of Variables
Access Data
 INFILE statement options to read raw data files
 Creating a file refrence with filename statement
 DATALINES statement with an INPUT statement
Starting With Raw Data(Basics)
 Styles of Input
 Reading Unaligned Data / Understanding List Input
 Understanding Column Input / Reading Data Aligned in Columns
Formats and Informatics
 Standard Data/NonStandard Data
 How Informatics and Format works
 Working with Date/Time/DateTime informal
 How and when to use Yearcutoff
Starting With Raw Data( Beyond Basics)
 Formatted Input style
 Using Modifiers
Mixing Styles of Input
 Testing a Condition before Creating an Observation
 Creating Multiple Observations from a Single Record
 Reading Multiple Records to Create a Single Observation
PDV: How the DATA Step Works
 Writing Basic Data Step
 How SAS Processes Programs
 Compilation phase
 Execution Phase
 Debugging a Data Step
 Testing SAS Programs
Manipulating SAS Datasets
 Creating & Modifying Variables
 Assigning Values Conditionally
 Specifying Lengths for Variables
 Subsetting Data
 Assigning Permanent Labels and Formats
Grouping Statements Using DO Groups
 Assigning Values Conditionally Using SELECT Groups
 Reading a Single Data Set
 Manipulating Data
 Using BYGroup Processing
 Reading Observations Using Direct Access (Point= option)
 Detecting the End of a Data Set(end= option)
 Understanding How Data Sets Are Read through PDV
 Renaming Variables
 Selecting Variables
Combining SAS Data Sets
 OnetoOne Reading
 Concatenating
 Interleaving
 MatchMerging
 MatchMerge Processing
 Excluding Unmatched Observations
Transforming Data with SAS Functions
 General Form of SAS Functions
 Converting Data with Functions
 Restriction for WHERE Expressions
 Manipulating SAS Date Values with Functions
 SAS Date and Time Values
 SAS Date Functions
 Modifying Character Values with Functions
 Modifying Numeric Values with Functions
 Nesting SAS Functions
Relevant base SAS procedures
 Append procedure
 Sort procedure
 Datasets procedure
 Print to procedure
 Format procedure
 Transpose procedure
 Import procedure
 Export procedure
 Print procedure
 Tabulate procedure
 Report procedure
 Means procedure
 Summary procedure
 Freq procedure
Generating Data with DO Loops
 Constructing DO Loops
 Introduction to Constructing DO Loops
 DO Loop Execution
 Counting Iterations of DO Loops
 Decrementing DO Loops
 Nesting DO Loops
 Iteratively Processing Data That Is Read from a Data Set
 Conditionally Executing DO Loops
 Using Conditional Clauses with the Iterative DO Statement
 Creating Samples
Processing Variables with Arrays
 Creating OneDimensional Arrays
 Understanding SAS Arrays
 Defining an Array
 Variable Lists as Array Elements
 Referencing Elements of an Array
 Compilation and Execution
 Using the DIM Function in an Iterative DO Statement
 Creating Variables in an ARRAY Statement
 Creating Temporary Array Elements
AnalyticsAn Introduction
 What is Business Analytics
 Difference between Analytics and Analysis
 Importance of Analytics in Industry
 Application of Analytics in Industry
 Learning and the growth curve of Analytics
 Puzzle
 Interview Preparation
Data and VariablesAn introduction
 What is Statististics
 What is an Average Mean Median Mode
 Different types of Data and variables
 Basic Statistical Measures
 Puzzle
 Quiz
 CodingAn Introduction
 Dependent and Independent Variable
Population and SampleSampling techniques
 Why do we need sample over a population
 Difference between population and sample
 Sampling Techniques
 Simple Random Sampling
 Stratified Random Sampling
 Sampling with and without replacement
 Puzzle
 Assignment
Normal Distribution & Central Limit Theorem
 Introduction to Bell Curve
 Deviation Vs Standard Deviation
 Variance and Standard deviation
 Outliers and their effects on basic statistical measures
 Symmetric and Asymmetric curve
 Bell CurveEmperical Rule–Grading process
 Quiz
Exploratory data Analysis
 Univariate anlysis vs Bivariate analysis Vs Multivariate analysis
 Decile, Percentile,Vintile,Quartile,Quantile
 Proc Univarite Details
 Moments , Basis Statistical Measures, Test for Location, Quantiles , Extreme Observation
 Assignment
 Puzzle
Data Visualization
 Box and whiskers plot
 Ranking AlgorithmProc Rank in detail
 Outlier Detection Removal Treatment
 Missing values How critical are they to a data
 Missing value Removal and imputation
 Proc for missing value Treatment
Hypothesis Testing
 Hypothests testing – What does that mean
 What is Hypothesis and what we do we test
 Null Hypothesis and Alternate Hypothesis
 Significanace level and Confidence level P value and α value
 Decision making Reject/Fail to Reject Null Hypothesis
 Confidence Interval
 Accuracy and Error
 Type I Error and Type II Error
Campaign Management
 Test and Control group
 Solicit and Non Solicit
 Responder and Non Responder
 Targets and Non Targets
 Measuring Cost/Revenue/Profit of a campaign
 2*2 profit/revenue contingency matrixActual Vs predicted conflicts
 Accuracy Error Sensitivity Specificity
 Puzzle
 Interview preparation
One Sample TTest
 One Sample Ttest What is the hypothsis for this test
 One Sample T Test using proc ttest
 One Sample T Test using proc univariate
 Sides of a test One sample two sided, Upper tail, lower tail
 How to apply Ttest to the data
 How will this help in making a decision
 Graphical interpretation of T test
 Puzzle
 Assignment
 Assumptions to a 1 sample T Test
Two sample Ttests & Sides of a test
 Difference between a onesample and a twosample TTest
 Hypothesis for a T Test
 Proc TTest for 2 sample T Test
 Understanding the results from the statistical point of view
 Plotting the graph for visualizing the TTest
 Sides of 2 sample T Test2 sided, Lower tail and upper tail
 Application of 2 Sample T Test
 Puzzle
 Quiz
 Assumptions to 2 sample TTest
ANOVAAnalysis of Variance
 Why do we need Anova
 Why can’t we use a 3 Sample T Test
 Hypothesis Testing for Anova
 Assumptions to Anova
 1 way Anova vs N Way Anova
 Performing 2 sample T test using Anova
 Coefficient of determination
 Degree of Freedom
 Levenes Test and F test
 Interaction – Type 1 and Type III SS
 Balanced Vs Unbalanced design
 Proc Anova
 Proc GLM
 Puzzle
 Interview Preparation
AnovaPost Hoc Analysis Test
 Why apply Posthoc Test when applying Anova
 Controlled Design experiment
 Experimental Error Rate
 Multiple Comparison Test
 Referential comparison
 Tukey and Dunnett Test Sides of a test
 Diffogram and Control Plot
 Means Vs LS Means
 Puzzle
 Mock Test
CorrelationDifferent types
 What is Correlation
 Why do need correlation analysis in any other analysis
 How to measure correlation
 Pearson and Spearman Correlation
 CorrelationHypothesis Test
 Proc Corr
 Correlation Matrix
 Correlationgraphical representation and Interpretation
 Puzzle
 Case Study
Regression Exploring the algorithm
 Simple Linear Regression and Multiple Linear Regression
 RegressionHypothesis Testing
 Degree of Freedom
 Anova table for Regression
 What is the Ordinary least square
 Parameter Estimate and Intercept
 Significant and Non Significant Variables
 Removing Redundancy
 Collinearity VIF
 Puzzle
 AssignmentDiscussion
Regression – Model Building
 Regression Model Building
 R square and adjusted R square
 2way honest assessment
 3way honest assessment
 How to split Training Validation Test
 Oversampling Undersampling Overfitting Underfitting
 Model Selection techniques
 variable selection techniques
 Model building Vs Model Fitting
 Model fit statistics
 Puzzle
 Mock Test
Logistic RegressionIntroduction
 Logistic RegressionAn Introduction
 Logistic RegressionNeed and the necessity
 Why cant we apply regression everywhere
 Algorithm to Logistic Regression
 Asssumption to Logistic Regression
 Checking the linearity amongst variables
 Checking for collinearity
 Removing Non Linearity
 Removing Non Linearity
 Logistic Regression Hypothesis testing
Predictive modeling using Logistic Regression
 What are the odds ratio
 Odds ratio vs probability
 Log odds
 log Vs Natural Log
 Complete separation Vs Quasi Complete Separation
 Data Convergence
 Fischer’s Technique
 Creating and identifying the dependent variable
 Data preparation for the model building process
Logistic RegressionModel Building
 Sampling data for Training Validation Test
 Finetuning Assessment and final assessment
 Outofsample validation
 Outoftime validation
 Variable transformation
 Variable reduction techniques
 Variable clusting techniques
 Identifying Collinearity amongst variables
 Interpretation of Results
Logistic RegressionMeasure model performance
 Cumulative Lift Chart
 Cumulative Gain Chart
 Relative operating characteristics
 Area under the curve
 Model fit statistics
 validation statistics
 Variable selection techniques
 Significant and nonsignificant variables
 Identifying the best variables for a model
Logistic RegressionIndustry view and applications
 Model selection techniques
 Parameter estimate and Intercept
 % Concordant, %Discordant, %ties pairs
 Calculatig C value from the statistics
 Puzzle
 Interview Preparation
 Case study
 Comparing training and validation statistics
SAS SQL 1: Essentials
 Introducing the Structured Query Language
 Overview of the SQL procedure
 Specifying columns
 Specifying rows
 Displaying Query Results
 Presenting data
 Summarizing data
SQL Joins
 Introduction to SQL joins
 Inner joins
 Outer joins
 Complex SQL joins
Subqueries
 Noncorrelated subqueries
 Inline views
Set Operators
 Introduction to set operators
 Union operator
 Outer Union operator
 Except operator
 Intersect operator
Creating Tables and Views
 Creating tables with the SQL procedure
 Creating views with the SQL procedure
Advanced PROC SQL Features
 Dictionary tables and views
 Using SQL procedure options
 Interfacing PROC SQL with the macro language
SAS Macro Language
 Introduction
 Getting Familiar to the macro facility
Macro Variables
 Introduction to macro variables
 Automatic macro variables
 Macro variable references
 Userdefined macro variables
 Delimiting macro variable references
 Macro functions
Macro Definitions
 Defining and calling a macro
 Macro parameters
DATA Step and SQL Interfaces
 Creating macro variables in the DATA step
 Indirect references to macro variables
 Creating macro variables in SQL
Macro Programs
 Conditional processing
 Parameter validation
 Iterative processing
 Global and local symbol tables
Advanced SAS Programming Techniques
 Measuring Efficiencies
Controlling I/O Processing and Memory
 SAS Data step processing
 Controlling I/O
 Using SAS views
 Reducing the length of numeric variables
 Compressing SAS data sets
Accessing Observations
 Creating a sample data set
 Creating an index
 Using an index
Using DATA Step Arrays
 Introduction to lookup techniques
 Using onedimensional arrays
 Using multidimensional arrays
 Loading a multidimensional array from a SAS data set
Using DATA Step Hash and Hiter Objects
 Introduction
 Using hash object methods
 Loading a hash object with data from a SAS data set
 Using the DATA step hiter object

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

Expert Programmer TechniquesCreating userdefined functionsThe experts’ FORMAT procedure

Understanding the R environment:Setting 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 dataReading csv files into RReading json files into RReading txt files into RReading sas7bdat files into R

Data structuresUnderstanding homogenous and hetrogenous form atomic vectors in R including Dataframes, List, Vectors, Factors and Matrices in RAtomic vectors in RDataframes, List, VectorsFactors and Matrices in R

Style GuideCoding standardsKnow HowNotation and NamingFilenamesObject namesSyntaxCurly BracesSpacingLine lengthIndentationAssignmentCommenting Guidelines

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

Functions and conditionalsWriting functions in RUnderstanding if…then…else in RUndersanding pure functions in R and understanding the purrr package in R

summarizationUnderstand the meaning of clusteringHierarchial clustering in RK means clustering in R

GLM FamilyLinear 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 TreesDecision Trees in RUnderstanding Ginni IndexKnowing the difference between CART and CHAIDWorking with Random Forest

Optimization in RWhat is optimization?Working with opimiztion packages in R (e.g Optimx)Constrained optimisation ( Lagrange multipliers )

Bonus TopicBasics of GGPLOT2Scatterplots in RHistograms and density plots in RUnderstanding basics of “Grammer of Graphics” concept

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

Getting started with PythonWhat 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 MLBasic process flow of any machine learningTerminologies used in MLEvaluation metrics used in MLComparison of ML and Statistical learning

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

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

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

Decision TreesIntroductionBasics of a treeClassification with treesAdvantages / disadvantages of tree

Random ForestIntroductionHow Random forest worksDifferent parameters in RFFun example and workshop!

Gradient boosting machinesIntroductionHow GBM worksDifferent parameters in GBMFun example and workshop!

ClusteringIntroductionK Means clusteringHierarchical ClusteringPractical issues in clustering

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

Neural NetworkIntroductionImportant concepts in NNBuilding NN for classificationFun example and workshop!

Solving live problems using what we have learnedBuilding a recommender SystemBuilding a sales prediction modelRetrieving twitter feeds and sentiment analysis
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