
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
- 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
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
- 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.
- 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
Duration :
Level :
Languages :
DATA Manipulation Techniques :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration :
Level :
Languages :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration :
Level :
Languages :
DATA Manipulation Techniques :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration :
Level :
Languages :
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
Duration :
Level :
Languages :
DATA Manipulation Techniques :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration :
Level :
Languages :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration :
Level :
Languages :
DATA Manipulation Techniques :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration :
Level :
Languages :
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
- 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
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
- 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.
- 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
Duration :
Level :
Languages :
DATA Manipulation Techniques :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration :
Level :
Languages :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration :
Level :
Languages :
DATA Manipulation Techniques :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration :
Level :
Languages :
Related Courses

SAS Advanced Programmer

SAS Base Programmer

SAS Certified AI & Machine Learning

SAS Certified Big Data Professional

SAS Certified Data Scientist

SAS Clinical Trials Programmer

SAS Data Manager (Analytics Programmers)

SAS Enterprise Miner

SAS Foundation Course

SAS Predictive Business Analyst (Combo)

SAS Predictive Modeling

SAS Statistical Business Analyst

SAS Statistical Business Analyst (Combo)
