** New to SAS**
Do you want to learn how to use SAS programming from beginners to validating machine learning algorithms assumptions?
Are you starting your new SAS journey?
Are you looking to know how to well interpret SAS output?
If you are that person, then you are about to enroll in the best course to guide you!
Your Instructor has more than 3 years of SAS experience.
Why learn SAS?
SAS jobs!
Try to search for “SAS Jobs” online. Your search is sure to turn up many current job listings that require a variety of SAS expertise. Since SAS emerges as a key research data analysis tool, it is in demand in the market. Every company is looking for SAS resources.
SAS is fun!
It is fun learning SAS. It provides an easy way to access multiple applications. It relies on user-written scripts or “programs” that are processed when requested to know what to do. Because it is a script-based application, the key to being successful using SAS is learning the rules and tricks of writing scripts. It works with large data and generates graphs and reports.
Data Analysis
SAS is versatile and powerful enough for data analysis. SAS is flexible, with a variety of input and output formats. It has numerous procedures for descriptive, inferential, and forecasting types of statistical analyses. Because the SAS System is an integrated system with similar architecture shared by modules or products, once you master one module, you can easily transfer the knowledge to other modules.
By the end of this course you will be able to :
Why wait when you can learn how to well write SAS programs from scratch?
Don't miss this opportunity for continuous learning.
Enroll to start your SAS journey today.
Use numbered range list to name SAS variables
Understand SAS libraries & how to access data in SAS using a library
Import unstructured data into SAS
Use SAS operators
Use SAS IF statements
IF - THEN/ELSE statements
IF-THEN/DO statements
Understand DO Loops
Use DO WHEN & DO UNTIL statements
Use the missing() function to deal with missing values
Use noduprecs & SORT procedure to remove duplicates
Write a neat sas syntax and be able to interpret the SAS output
How to detect Multicollinearity or Collinearity Diagnostics
Use Variance Inflation Factor (VIF) to detect multicollinearity
Use Condition Index (Condition numbers) to detect Multicollinearity
Perform and Interpret Shapiro Wikis Test Normality Test
Validate Linearity Assumption
Carry out Pearson Correlation Test and Interpret the results using p - values
Carry out RESIDUAL DIAGNOSTICS test and Interpret the results
Detect Outliers & Influential Observations
Interpret DFFITS & DFBETAS plots
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Feedbacks (4)
Good. So far it seems doable.