SAS Foundation Course

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
  • Anyone starting to write SAS programs
  • SAS Programming 1: 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: Enter the exciting work of data analytics and business intelligence by learning to
  • Create an accumulating column and process data in groups
  • Manipulate data with functions
  • Convert column type to other formats
  • Create custom formats
  • Concatenate, merge and restructure tables
  • Using loops
Essentails :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration : 3 days
Level : Fundamental
Languages : English

DATA Manipulation Techniques :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration : 3 days
Level : Intermdiate
Languages : English

Advance Programmer

Macro Language 1: Essentials:
  • Introduction
    • Overview of SAS Foundation
    • Program flow
  • Macro Variables
    • Introduction to macro variables
    • Automatic macro variables
    • Macro variable references
    • User-defined 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
SQL 1: Essentials:
  • Introduction to SQL
  • Building Basic Queries using SQL procedures
  • Displaying Query Results
  • Using SQL Joins
  • Performing Subqueries
  • Using Operators
  • Creating Tables and Views
  • Advanced PROC SQL Features
Advanced Techniques and Efficiencies :
  • Introduction
  • How to write Efficient SAS Programs
    • SAS DATA step processing
    • Controlling I/O
    • Reducing the length of numeric variables
    • Compressing SAS data sets
    • Using SAS views
  • Accessing Observations
    • Access methods
    • Accessing observations by number
    • Creating and using an index
  • DATA Step Arrays
    • Introduction to lookup techniques
    • One-dimensional arrays
    • Multidimensional arrays
    • Loading a multidimensional array from a SAS data set
  • DATA Step Hash and Hiter Objects
    • Hash object methods
    • Loading a hash object from a SAS data set
    • DATA step hiter object
  • Combining Data Horizontally
    • DATA step merges and SQL procedure joins
    • Using an index to combine data
    • Combining summary and detail data
    • Combining data conditionally
  • User-Defined Functions and Formats
Macro Language 1: Essentials: Experienced SAS programmers who have a sound understanding of DATA step processing
SQL 1: Essentials: SAS programmers and business analysts
Advanced Techniques and Efficiencies: Experienced SAS programmers
Macro Language 1: Essentials:
  • Participants should have completed the SAS Programming 2: Data Manipulation Techniques course or have equivalent knowledge.
  • Use a DATA step to read from or write to a SAS data set or external fil

SQL 1: Essentials:
  • Execute SAS programs on your operating system
  • create and access SAS data sets
  • use arithmetic, comparison, and logical operators
  • Use SAS procedures
Advanced Techniques and Efficiencies:
  • This course is not appropriate for beginning SAS software users.
  • Before attending this course, you should have at least nine months of SAS programming experience
  • Should have completed the Data Manipulation Techniques course
Essentials :
  • Learn to use the components of the SAS macro facility and how to design, write, and debug macro systems.
  • Perform text substitution in SAS code
  • Automate and customize the production of SAS code
  • Conditionally or iteratively construct SAS code
  • Use macro variables and macro functions.
SQL 1: Essentials :
  • Query, subset, summarize and present data
  • Create and modify table views and indexes
  • Combine tables, including complex joins and merges
  • Replace multiple DATA and PROC steps with one SQL query.
Advanced Techniques and Efficiencies : Learn how to compare various SAS programming techniques that enable you to
  • Benchmark computer resource usage, control memory, I/O, and CPU resources
  • Combine data horizontally
  • Compress SAS data sets
  • Create user-defined functions and informats
Macro Language:
Delivery Method : Classroom Training / Live Web / Self Learning
Duration :14 days
Level : Fundamental
Languages : English

SQL 1: Essentials :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration : 17.5 Hours
Level : Fundamental
Languages : English
. .

Advanced Techniques & Efficiencies :
Delivery Method : Classroom Training / Live Web / Self Learning
Duration : 21 Hours
Level : Intermdiate
Languages : English

R and Python

  • Introduction to Python
    • Installation of Python
    • Packages in Python, Installing Packages
  • Basic Operations in Python
    • Programming Language Basics,
    • Numbers , Strings Lists , Dictionaries , Tuples Files ,
    • Exercise/Case Study
  • Data Processing in Python
    • Conditional Processing,
    • Loops, Iterations and other iterative processing
  • Data manipulation in Python
    • Functions , arguments and modules in Python
    • Transforming Variables
  • Overview of Python Packages/Libraries
    • Popular Python Packages/Libraries
    • Overview of Python application in analytics industry
  • Introduction to R
    • Installation of R-Studio
    • Packages in R, Installing Packages,
    • Setting Directories
  • Basic Operations in R
    • Programming Language Basics
    • Scalars, Vectors, Simple Calculations Data Structure
    • Data Frames, Exercise/Case Study
  • Data manipulation in R
    • Data Acquisition (Import & Export)
    • Sub-setting observations,Subsetting variables,
    • Transforming Variables, Renaming and Recoding Variables
  • Data Processing in R
    • Conditional Processing,
    • Missing Values, Merging and Concatenating Datasets
R and Python: Programmers and Budding programmers
R and Python:
  • Should have basic programming experience preferably in an object-oriented programming language
  • Install Python Software and packages
  • Import external forms of data
  • Data manipulation
  • Do iterative processing and simulate new data
  • Understand Python Functions
R Language :
  • Install R Software and packages
  • Import various forms of data
  • Subset and merge data tables
  • create and enhance plots of all types
  • apply descriptive and inferential procedures including regression, logistic regression, analysis of variance
  • Stepwise model selection
  • Performing Common Machine learning
  • Exploring Advance Machine learning
Module 3. R and Python
Delivery Method : Classroom Training / Live Web
Duration : 20 Hours
Level : Fundamental
Languages : English
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