Data Preparation for Analytics Using SAS

1. Data Preparation for AnalyticsUsing SAS Gerhard Svolba, Ph.D. Reviewed by Madera Ebby, Ph.D.

2. What is the purpose of this book? • Introduces the reader to data preparation • Why data preparation is not only important but a must prior to data analysis • From data preparation process to data analytics

3. The Analysis Path: From raw data to results that can be implemented

4. The Analysis Path: From raw data to results that can be implemented Good Results ` Clever Modeling Adequate Preparation Data availability

5. Four Dimensionsfor Analytic Data Preparation Business and Process Knowledge Analytical Knowledge Analytic Data Preparation Efficient SAS coding Documentation and Maintenance

6. Business question: How did students who met the provincial standard in grade 3 perform in grade 6? • Generates many other questions • Work with people in other departments such as IT to carry out a data analytic process

7. Why is this author qualified or not qualified to address this topic? • He is an experienced SAS user as exemplified in the many Macros • He addresses issues by presenting examples from different background

8. What are the strengths or weaknesses of this book? • The book is written clearly and is easy to read • Provides the reader with a lot of examples of codes, input and outputs

9. Would you recommend this book?  If so, who would you recommend it to and for what purpose?  • Those who prepare data marts for statistics or data mining or time series analyses • Those who provide data used in creating data marts IT and data warehousing • Both new and experienced SAS users who perform data analyses using data marts • Those who prepare data in relational databases with SQL

10. Does the book achieve its purpose? Absolutely! It enables one to: • Understand the business environment in which data preparation occurs • Extract and structure your data • Create derived variables from different tables • Program SAS in an efficient way

11. What is the best tip or technique addressed in this book? • There are many new techniques that I learnt from this book. For example: • Examine the mean scores for math by board mident

12. Continued… • Procmeansdata=datalib.boards noprintnway; • class board_mident; • var Math_score; • outputout=datalib.aggr_static(drop=_type_ _freq_) • Mean= Sum= N= STD= MIN= MAX= /Autoname; • run;

13. Continued… • To run analysis by board_mident, we use a CLASS statement. A BY statement could also be used but data would have to be sorted by board_mident • NWAY suppresses grand total mean and all other totals so that output data contains only rows for 5 boards which are the analysis subjects • The NOPRINT in order to suppress the printed output from the log, which can be thousands of descriptive measures even for a small sample of 5 observations • In the OUTPUT statement we specify the statistics that will be calculated . The AUTONAME option creates the new variable names in the form of VARIABLENAME_ STATISTIC • If we want to calculate different statistics for different input variables we can specify it on the OUTPUT statement: e.g SUM(VARIABLE)=sum_variable • In the OUTPUT statement we drop the _TYPE_ and _FREQ_vaiables, although we could keep the _FREQ_ and omit N from the statistics list. • Chapter 18, Multiple Interval-Scaled Observations per subject, page 183.

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15. Are there other books (or sources of information) available with similar content?  • Yes, but tend to present bits and pieces of information • E.g. Resources on the internet • The Little SAS Book by Delwiche and Slaughter • If so, how does this book compare? • Comprehensive, well illustrated presentation of material

16. What will your SAS log look like?

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