Statistical Analysis System
Statistical Analysis System Description
SAS (Statistical Analysis System) is a software suite developed by SAS Institute Inc. for advanced analytics, business intelligence, data management, and predictive analytics. It is widely used by organizations across various industries to analyze and derive insights from data, make data-driven decisions, and solve complex business problems.
Key features of Statistical Analysis System(SAS) include:
- Data Management:
SAS provides a comprehensive set of tools for data management, including data integration, data quality, and data governance. Users can efficiently manage and prepare data for analysis, ensuring data accuracy and consistency. - Analytics:
SAS offers a wide range of advanced analytics capabilities, including statistical analysis, machine learning, predictive modeling, and optimization. Users can perform complex analyses to uncover patterns, trends, and relationships within their data and make informed decisions. - Business Intelligence (BI):
SAS provides robust business intelligence tools for data visualization, reporting, and dashboarding. Users can create interactive visualizations, generate reports, and monitor key performance indicators (KPIs) to gain insights into business operations. - Programming Language:
SAS programming language, known as SAS Programming, provides a powerful and versatile environment for data manipulation, analysis, and reporting. It offers a wide range of functions, procedures, and data step programming capabilities for data processing and statistical analysis. - Data Visualization:
SAS Visual Analytics and SAS Visual Statistics are tools within the SAS suite that enable users to create interactive visualizations, explore data visually, and perform advanced statistical analysis through a user-friendly interface.
- Introduction of SAS software.
- Industries using SAS
- Components of SAS System.
- Architecture of SAS system.
- Functionality of SAS System.
- Introduction of SAS windows
- Functionality of SAS Windows.
- Creating and managing SAS Libraries.
- Overview of SAS Data states.
- Types of Libraries.
- Storing files temporarily and permanently.
- Referencing SAS files.
- Steps to create a SAS dataset.
- Creating SAS dataset using text file.
- Creating SAS dataset using text file with delimiters.
- Creating SAS dataset using structured text file.
- Creating SAS dataset using unstructured text file.
- Creating SAS dataset using Excel file.
- Creating SAS dataset using Access file.
- Creating SAS dataset using values inside the code.
- Concepts of output delivery system.
- How ODS works and viewing output of ODS in different format.
- HTML, RTF, PDF etc..
- One-o-one reading
- One to many
- Many to many
- Concatenation
- Interleaving,
- Match merge
- Character function
- Numerical function
- Arithmetical function
- Mathematical function
- Date Function
- Do Loop
- Do While
- Do Until
- Definition of array
- Example of array
- Procedure Format.
- Procedure Contents.
- Procedure Options.
- Procedure Append.
- Procedure Compare.
- Procedure Transpose.
- Procedure Print.
- Procedure Import.
- Procedure Export.
- Procedure Datasets.
- Procedure Tabulate.
- Procedure Chart, Gchart, Gplot.
- Procedure Report.
- Introduction to graphics.
- Introduction to graphics.
- Types of Graphics (with latest models)
- Defining procedure Graphics
- Generate detail reports by working with a single table, joining tables, or using set operators in the SQL procedure.
- Generate summary reports by working with a single table, joining tables, or using set operators in the SQL procedure.
- Construct sub-queries and in-line views within an SQL procedure step.
- Compare solving a problem using the SQL procedure versus using traditional SAS programming techniques.
- Create and use user-defined and automatic macro variables within the SAS Macro Language.
- Automate programs by defining and calling macros using the SAS Macro Language.
- Understand the use of macro functions.
- SAS role in Clinical Research.
- What is Clinical trial?
- What is Protocol and role of Protocol in Clinical Research?
- Which is playing main role in Clinical Research?
- Identify the classes of clinical trials data (demographic, lab, baseline, concomitant medication, etc.).
- Identify key CDISC principals and terms.
- Describe the structure and purpose of the CDISC SDTM data model.
- Describe the structure and purpose of the CDISC ADaM data model.
- Describe the contents and purpose of define.xml.
- Apply categorization and windowing techniques to clinical trials data.
- Transpose SAS data sets.
- Apply ‘observation carry forward’ techniques to clinical trials data (LOCF, BOCF, WOCF).
- Calculate ‘change from baseline’ results.
- You will never miss a class at Makeinternship! You can choose either of the two options:
- You can go through the recorded session of the missed class and the class presentation that are available for online viewing through the LMS.
- You can attend the missed session, in any other live batch. Please note, access to the course material will be available for a lifetime once you have enrolled in the course.
- Makeinternship is committed to providing you with an awesome learning experience through world-class content and best-in-class instructors.
- We will create an ecosystem through this training, which will enable you to convert opportunities into job offers by presenting your skills at the time of an interview. We can assist you in resume building and also share important interview questions once you are done with the training. However, please understand that we are not into job placements.
- We have a limited number of participants in a live session to maintain Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording, and it would give you a clear insight into how the classes are conducted, the quality of instructors, and the level of interaction in the class.
- Trainer has 15+ years of experience in IT industry.