Customer Reviews

Making Sense of Data II: A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications

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Be aware that this book is not meant for the mathematician or statistician. Instead, the authors write for someone outside those fields, who has expertise and data in another topic, and who needs to analyse that data. It is concisely written; in part perhaps as an inducement for you to easily read it cover to cover.

The basic graphical methods are explained. With a good reminder that sometimes a well laid out table is preferable to a graph that makes comparisons difficult. Pie graphs are especially deprecated. The advice is well worth pondering, especially when many users now have Excel or other office software on their computers, that can too easily gin up a colourful graph. A key idea is that you need to put some thought into what you want to graph, instead of quickly grabbing the first available method in your software package.

The mathematics in the text is mostly confined to definitions of terms like correlation cofficient. There is little in the way of actual derivations. Again, this is to expand the readership to those not overly familiar with maths. As one example, the F test is informally defined, in such a way that you can easily apply it. But it is presented at a black box level. If you need more information, a full statistics text should be consulted.

Chapter 5 goes lightly into data mining in bioinformatics and for financial contexts. Enough to give a good introduction, from which you can seek books devoted to each topic.