Learning Analytics on SAS (120 HRS )

This course also prepares the learners for Global  SAS Certification Exam Base SAS(A00 -211) Advance SAS(A00-212) Regression & Modeling (A00-240), Mock Tests are also available at the end of the this Analytics SAS Training. Pass SAS Institute A00-211 ,A000-212 and A00-240 Exam at Your First Attempt with our Real Exam Practice classes

 

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 Informats

  • Standard Data/ Non Standard Data
  • How Informats and Format works
  • Working with Date/Time/Datetime informat
  • 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 BY-Group 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

  • One-to-One Reading
  • Concatenating
  • Interleaving
  • Match-Merging
  • Match-Merge 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
  • PRINTTO 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 One-Dimensional 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

 

SAS SQL 1: Essentials

1. Introduction

  • Introducing the Structured Query Language

2. Basic Queries

  • Overview of the SQL procedure
  • Specifying columns
  • Specifying rows

3. Displaying Query Results

  • Presenting data
  • Summarizing data

4. SQL Joins

  • Introduction to SQL joins
  • Inner joins
  • Outer joins
  • Complex SQL joins

5. Subqueries

  • Noncorrelated subqueries
  • In-line views

6. Set Operators

  • Introduction to set operators
  • UNION operator
  • OUTER UNION operator
  • EXCEPT operator
  • INTERSECT operator

7. Creating Tables and Views

  • Creating tables with the SQL procedure
  • Creating views with the SQL procedure

8. Advanced PROC SQL Features

  • Dictionary tables and views
  • Using SQL procedure options
  • Interfacing PROC SQL with the macro language

SAS Macro Language

 

1. Introduction

  • Getting Familiar to the macro facility

2. Macro Variables

  • Introduction to macro variables
  • Automatic macro variables
  • Macro variable references
  • User-defined macro variables
  • Delimiting macro variable references
  • Macro functions

3. Macro Definitions

  • Defining and calling a macro
  • Macro parameters

4. DATA Step and SQL Interfaces

  • Creating macro variables in the DATA step
  • Indirect references to macro variables
  • Creating macro variables in SQL

5. Macro Programs

  • Conditional processing
  • Parameter validation
  • Iterative processing
  • Global and local symbol tables

 

Advanced SAS Programming Techniques

  • 1. Measuring Efficiencies
    • Measuring efficiency

    2. 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

    3. Accessing Observations

    • Creating a sample data set
    • Creating an index
    • Using an index

    4. Using DATA Step Arrays

    • Introduction to lookup techniques
    • Using one-dimensional arrays
    • Using multidimensional arrays
    • Loading a multidimensional array from a SAS data set

    5. 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

    6. Combining Data Horizontally

    • DATA step merges and SQL procedure joins
    • Using an index to combine data
    • Combining summary and detail data
    • Combining data conditionally

    7. Expert Programmer Techniques

    • Creating user-defined functions
    • The experts’ FORMAT procedure

      Introduction to Statistics

      Basic Statistical Concepts
      Descriptive and Inferential Statistics, Populations and Samples, Parameters and Statistics, Use of variables dependent and Independent, Types of Variables Quantitative and Categorical, Scales of measurement Nominal ordinal interval ratio, Statistical Methods, Exploring your data

      Descriptive Statistics
      Describing your data, Measures of Location, Percentiles, Measures of Variability, Using descriptive statistics to answer data questions, Means procedure, Using Proc means to generate statistics

      Picturing your data
      Histogram, Normal Distribution, Assessing Normality, Measures of Shape Skewness, Measures of Shape Kurtosis, Normal probability plots, ,Box Plots, Comparing Distributions Summary, Assessing Normality with examples, Univariate Procedure, Statistical Graphics procedure in SAS, The SG Plot procedure in SAS, ODS Graphics output, Using SAS to picture your data

      Confidence Intervals
      Point Estimators,Variability and Standard Error, Distribution of Sample Means, Interval Estimators, Confidence Intervals, Normality and Central Limit Theorem, Calculating confidence interval for the Mean, Using Proc Means to generate confidence interval, Calculating 95 percent confidence interval

      Hypothesis Testing
      Decision Making Process, Steps in hypothesis testing, Types of Errors and Power, The P value Effect Size Sample Size, Statistical Hypothesis Test, The T Statistic and the T Distribution, Comparing Sample and Hypothesized Means, Using Proc univariate to generate statistics, Using Proc Univariate to perform a hypothesis test

      Analysis of Variance – Anova

      Two sample T test
      Assumptions for Two sample T Test, F test for Equality of Variance, Comparing Group Means, Identifying your data, The T test procedure, Running Proc T test in SAS, Examining the equal variance t- test and p- value, Examining the unequal variance t- test and p value, Interpreting two sample t test result, One Sided Test, Test for difference on one side, The T test procedure and Side option, Performing a one sided test.

      One Way Anova-Introduction
      Anova Overview, The ANOVA hypothesis, The ANOVA Model, Sum of Squares, Assumptions for Anova, Predicted and Residual Values, Comparing group means with one way Anova, Examining the descriptive statistics across means, The GLM procedure, using the GLM procedure.

      Anova with data from a Randomized block design
      Observational Studies Vs Controlled Experiments, Nuisance Factors, Including a blocking variable in the model, More Anova Assumptions, Creating a Randomized block design, Performing Anova with Blocking,

      Anova -Post Hoc Test
      Multiple Comparison Methods, Tuckey’s multiple comparison method, Dunnet’s multiple comparison method, determining which mean is different, Diffogram and Tuckey’s Method, Control Plots and Dunnet’s Method, Proc GLM with LS means, performing post hoc pairwise comparison

      Two Way Anova with Interactions
      N Way Anova, Interactions, Two way Anova Model, Using Two way Anova, Identifying your data, Applying the two way Anova Model, Examining your data with Proc Means, Examining your data with Proc SG plot, Performing two way Anova with Interactions, Performing Post Hoc Pairwise comparison

      Regression

      Exploratory data analysis
      Using scatter plots to describe relationship between continuous variables, Using Correlation to measure relationship between two continuous variables, Hypothesis testing for a correlation, Avoiding common errors in interpreting correlations, Avoiding common errors Causal and Effect, Avoiding common Errors: Types of relationships, Avoiding common Errors: Outliers, Exploring data using correlation and scatter plots, Producing correlation statistics and scatter plots using Proc Corr, Using PROC CORR to produce correlation matrix and scatter plots, Examining correlations between predictor variables.

      Simple Linear Regression
      Objectives of simple linear regression, performing simple linear regression, The simple linear regression model, How SAS performs simple linear regression model, Measuring how well the model fits the data, Comparing regression model to a baseline model, Hypothesis testing for linear regression, Assumption of simple linear regression, The REG procedure: Performing Simple linear regression, Confidence and prediction intervals, Specifying Confidence and Prediction Intervals using SAS, Viewing and printing confidence intervals, The REG procedure producing predicted values, Producing predicted value of the response variable, Scoring predicted values using parameter estimates, Storing parameter estimates using Proc Reg and score using Proc score.

      Multiple Linear Regression
      Advantages and Disadvantages of Multiple Regression, Common Applications for Multiple linear regression, Picturing the model for Multiple Regression, Analysis versus prediction in multiple regression, Hypothesis testing for multiple regression, Assumptions for multiple regression, The Reg Procedure performing multiple linear regression

      Model Building and interpretation
      Approaches to selecting model, SAS and automated approaches to modeling, All possible regressions approach to model building, SAS and all possible regressions approach, Evaluating the model using Mallow’s Cp stat, Viewing Mallow’s Cp stat in Proc Reg, The REG procedure using all techniques, The REG procedure using automatic selection, The REG procedure Estimating and testing coefficients for selected models, The Stepwise selection approach to model building, Specifying Stepwise selection in SAS, The REG procedure performing stepwise regression, Using alternate significance criteria for stepwise models

      Regression Diagnostics

      Examining Residuals
      Assumption for regression, the importance of plotting data and checking Assumptions, Verify Assumptions using residual plots, Detecting outlies using residual plots, The REG procedure producing default diagnostics, The REG procedure specific diagnostics

      Identifying influential observations introduction
      Using diagnostic statistic, Using Diagnostic Statistic STUDENT, Using Diagnostic Statistic COOK, Using Diagnostic Statistic RSTUDENT, Using Diagnostic Statistic DFFITS, Using Diagnostic Statistic DFBETAS, The REG procedure Requesting diagnostic plots, using diagnostic plots to identify influential observation, The REG procedure generating and saving diagnostic statistics, The REG procedure writing diagnostic statistics to an output dataset, Using cut off values for diagnostic criteria, Detecting influential observation programmatically, Handling influential

      Detecting Collinearity
      Understanding Collinearity, The REG procedure detecting Collinearity, Using Diagnostic statistic to detect Collinearity, The REG procedure Calculating diagnostics for Collinearity, The REG Procedure Dealing with Collinearity, Using an effective modeling cycle
      Categorical data analysis

      Describing categorical data
      One Way Frequency tables, Association between categorical Variables, Cross Tabulation tables, Testing for Association and fitting a logistic model, The Tables statement in Proc Freq, Examining the distribution of categorical variables, Ordering the values of an Ordinal variable, Ordering a variable in cross tab table

      Test of Association
      The Pearson Chi Square Test, Cramer’s V Statistics, Odds Ratio, Performing Chi Square Test, The Mentel Haenszel Chi Square Test, The Spearman Correlation Statistic, Performing a Mentel Haenszel test for ordinal Association

      Introduction to Logistic Regression
      Logistic Regression, Modeling a binary response, The Logistic Procedure, Specifying a parameterized method in class statement, Effect Coding, Reference Cell coding, Fitting a binary logistic regression model, Interpreting odds ratio for a categorical predictor, Interpreting odds ratio for a continuous predictor, Comparing pairs to assess the fit of a model.

      Multiple Logistic Regression
      Multiple logistic regression, The backward elimination method for variable selection, Adjusted Odds ratio, Specifying the variable selection method in model statement, The Units Statement, Fitting a multiple logistic regression model, Comparing the binary and the logistic regression model, Specifying the formatted value as a reference, Interaction between variables, The backward elimination method with interactions, Specifying interaction in the model statement, Fitting a multiple logistic regression with interaction, The Odds ratio statement, Fitting a multiple logistic regression with all Odds ratio, Comparing multiple logistic regression models, Interaction plots

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