SAS Statistical Business Analyst (Combo)

Base Programmer

  • Essentials
    • The SAS programming process
    • Using SAS programming tools
    • Understanding SAS programming syntax
  • Accessing Data
    • Understanding SAS data structures
    • Accessing data through libraries
    • Importing data into SAS
  • Exploring and Validating Data
    • Data Exploration
    • Filtering and formatting data
    • Arranging data
  • Preparing Data
    • Reading and filtering data
    • Computing new columns
    • Conditional processing
  • Analysing and Reporting on Data
    • Enhancing reports with titles, footnotes, and labels
    • Creating frequency reports
    • Creating summary statistics reports
  • Exporting Results
    • Exporting data and reports
  • Using SQL in SAS
    • Using SQL
    • Joining tables using SQL in SAS
Data Manipulation Techniques :
  • Controlling DATA Step Processing
  • Accessing Data
  • Summarizing Data
  • Manipulating Data with Functions
  • Creating Custom Formats
  • Combining Tables
  • Processing loops
  • Restructuring tables
Essentials : Anyone starting to write SAS programs
Data Manipulation Techniques: Business analysts and SAS programmers
Essentials :
  • No prior SAS experience is needed
  • Experience using computer software
  • Understand file structures and system commands on your operating systems
  • Access data files on your operating systems
Data Manipulation Techniques :
  • Ability to use DATA code to subset rows and columns, compute new columns, and process data conditionally
  • Ability to use SORT procedure
  • Knowledge on applying SAS formats
Essentials Enter the exciting work of data analytics and business intelligence by learning to
  • Use SAS to write and submit SAS programs
  • Access SAS, Microsoft Excel, and text data
  • Explore and validate data
  • Prepare data by creating subsets of rows and computing new columns
  • Analyze and report on data
  • Export data and results to Excel, PDF, and other formats
  • Use SQL in SAS to query and join tables.
Data Manipulation Techniques
  • Use SAS to write and submit SAS programs
  • Access SAS, Microsoft Excel, and text data
  • Explore and validate data
  • Prepare data by creating subsets of rows and computing new columns
  • Analyze and report on data
  • Export data and results to Excel, PDF, and other formats
  • Use SQL in SAS to query and join tables.
Essentails :
Delivery Method : Classroom Training / Live Web / Self Learning
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DATA Manipulation Techniques :
Delivery Method : Classroom Training / Live Web / Self Learning
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Macros SQL

Essentials :
  • Introduction
    • Purpose of the macro facility
    • Program flow
  • Working with Macro Variables
    • Introduction to macro variables
    • Automatic macro variables
    • Macro variable references
    • User-defined macro variables
    • Delimiting macro variable references
    • Macro functions
  • Macro Définitions
    • Defining and calling a macro
    • Macro parameter
  • 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
Essentials : Experienced SAS programmers
Essentials : Should have completed the SAS Programming 2: Data Manipulation Techniques course or have equivalent knowledge on how data manipulation is done in SAS
Essentials Learn how to
  • Automate and customize the production of SAS code
  • Conditionally or iteratively construct SAS code
  • Use macro variables and macro functions.
Essentails :
Delivery Method : Classroom Training / Live Web / Self Learning
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DATA Manipulation Techniques :
Delivery Method : Classroom Training / Live Web / Self Learning
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Statistical Business Analyst

Introduction to ANOVA, Regression, and Logistic Regression :
  • Introduction to Statistics
    • Examining data distributions
    • Obtaining and interpreting sample statistics using the UNIVARIATE and MEANS procedures
    • Render graphically in the UNIVARIATE and SGPLOT procedures
    • Constructing confidence intervals
    • Performing simple tests of hypothesis
  • Tests and Analysis of Variance
    • Tests of differences between two group means using PROC TTEST
    • One-way ANOVA with the GLM procedure
    • Multiple comparisons tests in PROC GLM
    • Two-way ANOVA with and without interactions
  • Linear Regression
    • Working with correlations
    • fitting a simple linear regression model
    • Understanding the concepts of multiple regression
    • Working with multiple models
    • Interpreting models
  • Linear Regression Diagnostics
    • Examining residuals
    • Investigating influential observations
    • Assessing collinearity
  • Categorical Data Analysis
    • Producing frequency tables
    • Examining tests for general and linear association
    • Understanding logistic regression
    • Fitting univariate and multivariate logistic regression models
Predictive Modeling Using Logistic Regression:
  • Introduction to Predictive Modeling
    • Business applications
    • Analytical challenges
  • Fitting the Model
    • Parameter estimation
    • Adjustments for oversampling
  • Input Data Preparation
    • Missing values
    • Categorical inputs
    • Variable clustering
    • Variable screening
    • Subset selection
  • Classifier Performance
    • ROC curves and Lift charts
    • K-S statistic
    • c statistic
    • Evaluating a series of models
Introduction to ANOVA, Regression, and Logistic Regression : Statisticians, researchers, and business analysts who use SAS programming to generate analyses
Predictive Modeling Using Logistic Regression : Modelers, analysts and statisticians who need to build predictive models
Introduction to ANOVA, Regression, and Logistic Regression
  • Should have completed the equivalent of an undergraduate course in statistics
  • Should be able to execute SAS programs and create SAS data sets
Predictive Modeling Using Logistic Regression
  • Must have completed statistics course on regression
  • Experience in executing SAS programs and creating SAS data sets
  • Experience building statistical models using SAS software
Introduction to ANOVA, Regression, and Logistic Regression Learn to
  • Understand describe data using graphical techniques
  • Use Analysis of Varience (ANOVA)
  • Perform linear regression and assess the assumptions
  • Use regression model selection techniques to aid in the choice of predictor variables in multiple regression
  • Use diagnostic statistics to assess statistical assumptions and identify potential outliers in multiple regression
  • Use chi-square statistics to detect associations among categorical variables
  • Fit a multiple logistic regression model.
Predictive Modeling Using Logistic Regression Learn how to
  • Use logistic regression to model as a function of known inputs
  • Create visualizations
  • Handle missing data values
  • Tackle multicollinearity
  • Assess model performance and compare models.
Essentails :
Delivery Method : Classroom Training / Live Web / Self Learning
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DATA Manipulation Techniques :
Delivery Method : Classroom Training / Live Web / Self Learning
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