Practicing SAS (Statistical Analysis System) involves hands-on experience with SAS software for data analysis, statistical modeling, and business intelligence. Regular practice, hands-on projects, and continuous learning through resources and communities are essential to improving your proficiency in SAS. This will enable you to effectively utilize SAS for data analysis, statistical modeling, and deriving insights from data.
Statistical Analysis Systems (SAS) is a powerful programming language. The components of the language are called procedures. The procedures perform various things like different types of analysis, data management functions and produce different types of text-based and graphical presentation output.
Experienced in Installing SAS Grid client component includes SAS platform LSF7.0, SAS Management Console, SAS Data Integration Studio, SAS E Miner, SAS E Guide, SAS Metadata Server. And, other component in Grid Control Server such as Base SAS, SAS Connect, SAS Workspace and spawner.
Create user, group, role, grant them permission.
Perform back up the metadata repositories. Maintain system data integrity.
Start,stop, pause, resume metadata server and other servers.
Create,maintain definition for SAS library on Metadata server; register,publish new table, lock/clear existing file for data protection.
View, modify infrastructure configuration; attribute for SAS application.
Perform hotfix installation, generate help guideline and tips for developers.
Two levels security: (1) O.S. level. Do 2FA authentication integrated PIV/Smart card and PAM, LDAP/AD authentication. FACL. (2) Application level: using SSL for establishing an encrypted link between a web server and a browser. Configure omaconfig.xml, ACT.
Shell scripting: invoking /Submitting SAS in batch mode. Use X command to execute Perl script.
Testing, troubleshooting SAS program, find their syntax and logical errors.
Optimization SAS program, save resource (CPU, memory, I/O) consumption.
Coding for ETL, macro, such as using X command through DDE read in Excel raw file, clean dirty data, meet CDISC format and through ODS output file.
Supporting analyst in data distribution; statistics; categorical; correlation; regression and variance (ANOVA).
Using RFM criteria to divide customer into segments for CRM such as which program were most welcomed by what kind shopping behavior customer.