I love reading great books about statistics and Christoper Chatfield is probably one of the greatest educators in statistics. Statistics for technology is a great introduction in statistics which isn’t too theoretical. I won’t provide a summary because these topics are rather rudimentary. However, I found some interesting things in this book, e.g.
A scientific experiment has some or all of the following characteristics.
- The physical laws governing the experiment are not entirely understood
- The experiment may not have been done before, at least successfully
- There are strong incentives to run the smallest number of the cheapest tests as quickly as possible
- The experimenter may not be objective, as for example when an investor tests his own invention or when a company tests competitive products
- Experimental results are unexpected or disappointing
- Although experimental uncertainty may be present, many industrial situations require decisions to be made without additional testing or theoretical study
It is often equally important to know how spread out the data is. For example suppose that a study of people affected by a certain disease revealed that most people affected were under two years old or over seventy years old; then it would be very misleading to summarize the data by saying ‘average age of persons affected is thirty-five years’.
There are some great examples in this book which make statistics for students more interesting in my opinion. The examples are rather technical which is obvious reading the title.
All in all, I can recommend this book if you are want to learn a bit about (technical) statistics. Great book!!
This is probably one of the standard intro texts into machine learning. Tom Mitchell covers most of the basic techniques in machine learning (ToC) but doesn’t cover all of them, e.g. SVMs. I got a bit of background in statistics so it was rather easy to dive into machine learning although their terminology is a mostly different from statistics.
If you don’t have a background in statistics but solid basics in calculus then it should be rather easy to understand the contents of this book. There are lots of exercises which help you to strengthen your understanding. I think it’s an ideal theoretical basis for Programming Collective Intelligence. All in all, a really nice book if you are interested in machine learning.
So, I read a few more technical books and asked myself how I should present them. I came to the conclusion that writing a summary and stuff isn’t really practical. Therefore I will only post a small entry with some words about the book. However, if you want to know more about a specific book and don’t find enough stuff about it online, you can ask me about it.
I looked for a nice intro to R because the official documentations is a bit too meta. What I found is this book which is a great reference and introduction to R. Joseph Adler covers the whole bandwidth of topics from data cleansing to analysis and graphics. He even covers 3th party packages for bioinformatics. However, if you don’t have a clue about statistics you should firstly read some books about it. This book doesn’t cover any proofs or derivations of statistical methods. All in all a pretty nice book which doesn’t have any serious flaws.
What is it about?
Robin Williams explains four simple rules to create better and clearer designs. She explains each rule with examples and uses these examples in other categories like color or typography.
Contrast, Repetition, Alignment and Proximity and don’t be a wimp. Proximity means that you should group relevant information together. If you use contrast it is important that you aren’t a wimp. Use hard contrast like black and white or big font size and very small font size.
The Non-Designer’s Design Book is a pretty cool introduction book in design. It shows the main patterns of good design. Surprisingly you are going to see each of this pattern everywhere. I tell you, I’m dammed!