A time ago the Western Power’s System Forecasting team, where I now belong in, initiated an upgrade of the software used for data analysis and forecasting. Several options were considered, including SPSS Modeller and a few industry specific options, but the final decision was to use SAS.
One reason for this desirable shift (R was not considered a suitable option) was to make use of advanced data techniques and models to quickly build reliable and accurate forecasts to improve business decisions. SAS Institute claims their current technologies can convert “transactional meter and operational systems data into a forecast-ready format”, which is exactly what the team required. However, for a corporation as big as Western Power, it may take another few years before all SAS components required to achieve this goal can be properly integrated and optimised. This time can be used to learn how to use SAS most efficiently and effectively.
There are many on-line resources with plenty of code available to help one learn SAS for free, e.g. UCLA’s SAS learning modules and Robert Allison’s SAS/Graph examples, but books published by SAS Press may remain the best learning option for beginners. It is the purpose of this post to list the books that are likely to be useful for the type of tasks the team performs now and will be expected to perform in the near future.
The list is not meant to be exhaustive, and in particular it does not include some older books on time series forecasting. More recent books on time series analysis, forecasting and predictive modelling are in as most relevant. The general programming books (functions, macros) are useful for developing a general understanding of and intuition about the particularities of the SAS programming language. The book on statistical programming with SAS/IML (Interactive Matrix Language) contains many examples of using R from within SAS environment. The two books on SQL will help learn how to interact with the company’s databases. There is also a book devoted solely to reports, which I think should not be ignored, and a book on techniques useful for cleaning data.
The list includes 12 items, and the first few have already been purchased: