SAS Training

• SAS fundamental concepts their importance 
• SAS Enterprise Guide
• SAS Environment 
• operators, functions and logic statements 
• Base SAS and Advanced SAS
• Summary reports and SAS graphs 
• SAS certification preparation 

Following are the professionals who get benefited from learning this course:

• Architects,
• Data Warehousing
• Business Intelligence Professionals 
• IT and Software Developers
• Project Managers
• Big Data and Analytical Professionals

There are no prerequisites to enrol into this SAS training course. If you have a basic knowledge of SQL then it would be an added advantage for you.

• SAS is one of the widely used Analytics tools in the IT world and the demand for the Analytics tools is expected to grow exponentially in the near future. 
• There is a huge demand for the certified SAS developers across the globe. 
• Indeed.com, statistics shows that the average salary received by a SAS programmer is $102,000

Yes, upon the successful completion of the SAS training you will receive the course completion certificate from SK Trainings. This certificate is valid across all the top MNCs and simplifies your job search process.

Yes, you will get all assistance from the SAS experts to clear your certification exam. Moreover you will be provided with the experts designed SAS course material to clear your certification exam.

• Managing windows in SAS window environment
• Why using the SAS in different sectors.
• How to use the data step to read and manipulate complex forms of  data
• Write Data and Proc steps.
• Data step compile and execution
• Read any type of external raw data into SAS
• Reading raw data SAS environment into DATA SET using Input statement & advance INLIFE  statement options
• Working with Data Storage in SAS libraries creation for user defined libraries and multi-engine architecture
• Reading data from data set to another data set.
• To manage the SAS window environment used with global options.
• To manage existing data with controlling statements and expressions
• Creating Summary Information, SAS Functions, Transforming Data
• Changing variable types using the PUT and INPUT functions summarizing data files
• To expert data from data sets to delimiter files using with data set block
• Understand error messages in the SAS Log and debug your program

• Using Do loops for repetitive calculations and processing
• Using Arrays to process across an observations and processing

• Using the update statement to update data in existing dataset.
• Using the MODIFY statements to update and modify data in place
• Merging concepts
• Concatenation concept in merging
• Interleaving concept and merging
• Different kind of match merging using MERGE statement using the contribution (IN=) option in merge  concept
• Using ODS concept to generate reports
• Role of ODS concepts to reporting SAS output
• To generate SAS output in different panels like RTF, HTML,PDF and XML Using ODS

• IMPORT PROCEDURE
• EXPORT PROCEDURE
• DBLOAD PROCEDURE
• COPY PROCEDURE
• CPORT PROCEDURE
• CIMPORT PROCEDURE
• DELTE PROCEDURE
• SETININT PROCEDURE
• OPTIONS PROCEDURE
• TEMPLATE PROCEDURE
• APPEND PROCEDURE
• COMPARE PROCEDURE
• SORT PROCEDURE
• DATASETS PROCEDURE
• PRINTTO PROCEDURE
• FORMAT PROCEDURE
• TRANSPOSE PROCEDURE
• ACCESS PROCEDURE

• PRINT PROCEDURE
• TABULATE PROCEDURE
• REPORT PROCEDURE
• CHART PROCEDURE
• GCHART PROCEDURE
• PLOT PROCEDURE
• GPLOT PROCEDURE

• MEANS PROCEDURE
• SUMMARY PROCEDURE
• UNIVARIATE PROCEDURE
• CORR PROCEDURE
• FREQ PROCEDURE
• ZTEST PROCEDURE
• FREQ PROCEDURE
• RANK
• TTEST PROCEDURE
• ANOVA PROCEDURE
• REG PROCEDURE
• GLM PROCEDURE

• Introduction to SQL Concepts
• Create new tables, indexes , views and reports

• What are Cartesian Products , what is join
• Innner , FULL,OUTER,LEFT and RIGHT Joins
• Union join,Cross joins
• Set Operator us such as union and intersection joining multiple tables

• HOW TO USE SAS MACROS IN SQL
• How to retrieve Raw data different from DATABASES to SAS environment using SQL Statements
• To create table in different databases using SAS sql statement
• To manage in different databases using SAS Sql Statements

• Uses of pass through facility
• How to communicate with other database like Access, Oracle, DB2
• To control and manage other databases from the SAS.

• Macro concepts&Advantages
• SAS Macro Language
• Macros And Macro Variables
• Creating Macro Variables & Using Macro Variables
• Creating Modular Code With Macros
• Invoking A Macro
• Adding Parameters to Macros
• Writing Macros With Conditional Logic
• Using Various Procedures in Macro Variables
• Nesting of Macros
• Multiple And Multi-Level Macros
• Macro Functions

The following are the four capabilities in SAS Framework

1.  Access: As we can learn from the figure, SAS allows us to access data from multiple sources like an Excel file, raw database, Oracle database and SAS Datasets.
2.  Manage: We can then manage this data to subset data, create variables, validate and clean data.
3.  Analyze: Further, analysis happens on this data. We can perform simple analyses like frequency and averages and complex analyses including regression and forecasting. SAS is the gold standard for statistical analyses.
4.  Present: Finally we can present our analysis in the form of list, summary and graphic reports. We can either print these reports, write them to data file or publish them online.

Stop statement causes SAS to stop processing the current data step immediately and resume processing statement after the end of current data step.

There are two data types in SAS. Character and Numeric. Apart from this, dates are also considered as characters although there are implicit functions to work upon dates.

SUM function returns the sum of non-missing arguments whereas “+” operator returns a missing value if any of the arguments are missing.

Suppose value of a variable PayRate begins with a dollar sign ($). When SAS tries to automatically convert the values of PayRate to numeric values, the dollar sign blocks the process. The values cannot be converted to numeric values. Therefore, it is always best to include INPUT and PUT functions in your programs when conversions occur.

PROC SQL is a simultaneous process for all the observations. The following steps happen when PROC SQL is executed:

1.  SAS scans each statement in the SQL procedure and check syntax errors, such as missing semicolons and invalid statements.
2.  SQL optimizer scans the query inside the statement. The SQL Optimizer decides how the SQL query should be executed in order to minimize run time.
3.  Any tables in the FROM statement are loaded into the data engine where they can then be accessed in memory.
4.  Code and Calculations are executed.
5.  Final Table is created in memory.
6.  Final Table is sent to the output table described in the SQL statement.

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TRANWRD function replaces or removes all occurrences of a pattern of characters within a character string.

Data is central to every data set. In SAS, data is available in tabular form where variables occupy the column space and observations occupy the row space.

• SAS treats numbers as numeric data and everything else falls under character data. Hence SAS has two data types numeric and character.
• Apart from these, dates in SAS are represented in a special way compared to other languages.
• A SAS date is a numeric value equal to the number of days since January 1, 1960.
• Apart from Date Values, there are many tools to work on dates such as informats for reading dates, functions for manipulating dates and formats for printing dates.

We can use ‘do until’ or ‘do while’ to specify the condition.

Yes, it depends on how you use the variable. There are some numbers we will want to use as a categorical value rather than a quantity. An example of this can be a variable called “Foreigner” where the observations have the value “0” or “1” representing not a foreigner and foreigner respectively. Similarly, the ID of a particular table can be in number but does not specifically represent any quantity. Phone numbers is another popular example

The number of observations is limited only by computer’s capacity to handle and store them.
Prior to SAS 9.1, SAS data sets could contain up to 32,767 variables. In SAS 9.1, the maximum number of variables in a SAS data set is limited by the resources available on your computer.

• Variables/Columns in position order.
• Rows ordered as they appear in data set.

• If the variable is used as group variable, rows that have the same values are collapsed.
• Group variables produce list report whereas order variable produces summary report.

You can use the OUTPUT statement to save summary statistics in a SAS data set. This information can then be used to create customized reports or to save historical information about a process.
You can use options in the OUTPUT statement to

1.  Specify the statistics to save in the output data set,
2.  Specify the name of the output data set, and
3.  Compute and save percentiles not automatically computed by the CAPABILITY procedure.

If you don’t want to process certain variables and you do not want them to appear in the new data set, then specify drop = data set option in the set statement.

Whereas If want to process certain variables and do not want them to appear in the new data set, then specify drop = data set option in the data statement.

The main difference is that while reading an existing data set with the SET statement, SAS retains the values of the variables from one observation to the next. Whereas when reading the data from an external file, only the observations are read. The variables will have to re-declared if they need to be used.

PROC MEANS produces subgroup statistics only when a BY statement is used and the input data has been previously sorted (using PROC SORT) by the BY variables.

PROC SUMMARY automatically produces statistics for all subgroups, giving you all the information in one run that you would get by repeatedly sorting a data set by the variables that define each subgroup and running PROC MEANS. PROC SUMMARY does not produce any information in your output. So you will need to use the OUTPUT statement to create a new DATA SET and use PROC PRINT to see the computed statistics.

An important difference between the DO UNTIL and DO WHILE statements is that the DO WHILE expression is evaluated at the top of the DO loop. If the expression is false the first time it is evaluated, then the DO loop never executes. Whereas DO UNTIL executes at least once.