SAS Tips and Tricks

Summary

SAS Tips and Tricks consists of half days/full days of knowledge transfer in SAS programming language and are organized by an motivated SAS Expert who combines theory and demonstration to helps you refreshing your SAS skills, discover news technics and meet other experts.

Organized for employees, sessions are organized on a monthly basis in our business center (near Brussels-North train station) and, to guaranty an optimal quality, the number of participants is limited to 8 persons. Contact us to assist to a session (scheduled or not) or click the register button in the course details, we will recontact you with more informations.

You cannot find a topic you would like to follow or would like to follow the session individually at your best conveniance? Please Contact us in order to see if we could organize a SAS Coaching that suits perfectly your needs.

Topic list

Level= Intermediate

In this tips and tricks we will have a look to the merge statement of the SAS DataStep in order to combine datasets.

Level= Intermediate

SAS has more than 190 of 'built-in' functions allowing you to perform a variety of programming tasks. It would be a burden to explain them all in an half day, that's the reason why we selected the most useful one in this tips and tricks.

Level= Intermediate

SAS formats are instruction that SAS uses to write data values. You use formats to control the written appearance of data values, or, in some cases, to group data values together for analysis. You can create format manually with hardcoded values or from datasets, you can store them in permanent library, share them and also use build-in format (etc.) every thing you want to know about SAS format should be present in this tips and tricks.

Reading flat files in SAS Data Step gives you a lot of flexibility in terms of data quality and data manipulation. However, problem may arise when files are huge with poor data quality. In this tips and tricks we will have a look to the 'heart' of the Data Step manipulation and learn to do as much as we can do in this important step: applying data quality rules, creating error datasets, selecting good variables and records (etc.)

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