Cam's book, mentioned also in the comments, is also wonderful.
I grinded through textbooks during my graduate studies, but I had to, in order to complete the HW and pass the courses.
But since joining industry I've not been able to actually work through a textbook - when I try to attempt the problems, I'll find a couple have passed and only one or two
problems have been completed - I simply find it a challenge to find the time to work through book exercises.
I usually don't retain a ton, but the big benefit is that I know where to find the relevant sections when I need them in the future, and have some sort of big-picture view of how they fit together.
I believe minds do need reread, retry in order to retain it as something worth recalling. I guess, some brains prefer not to waste energy on forming the long-lasting synapses on something that was casually encountered.
One. Simple. Thing. Is. Enough.
Freedom from the pressure to fully understand everything from a book (or even from a single chapter) has allowed me to learn a lot more in recent years, and in a far more enjoyable manner.
So for me, I read the book quickly or academic paper almost as if it is a fiction. Generally not going back or pausing. Sometimes even faster. (I have found this gives me a better overview of the entire content, comapred to meticulously starting slowly at the first chapter and eventually getting stuck.)
I do this until, a particular section stands out and really piques my interest. This is typically either because:
(i) it is coincidentally relevant to something I have been recently working on, or
(ii) the author's description of a topic is written in just the right way that things 'just click' and I have a newer or deeper understanding of the topic.
Often these scenarios give me the intrinsic motivation to spend a longer time on that topic.
In this way, I often read the same book/paper many times over, during a period of a few months, and each time I learn something new. In some ways, this strategy is similar to what some people call the wedge approach which is a balance between the debate of being a studying wide or deep. That is, study a lot of things broadly, several things moderately, and a few things very deeply.
The corollary to this idea comes from my teaching experience. Teachers know that the best learning comes when the difficulty is just at the edge of a student's ability. Not too easy. Not too hard. This is the power of incremental learning. So it makes sense you only have to find one thing in the text book that is just at the edge of what you already know, and learn about that.
So often when I need to do something for work, I apply this +1 technique. I learn what I need to do for my project and then just 'one bit more'. All those 'one bit more' explorations add up to quite a lot over time.
Hope that helps. ;)
literally just front to back. I did maths in university and when I started to work for a few years I didn't get too do much of it so I just got into the habit to put half an hour a day aside to work through whatever books I find interesting.
I actually enjoy it much more now than I did in uni given that I can do it at my own pace now and for fun.
About exercise, I do not think you are meant to solve them all. The most important thing is to get the main idea from chapter to chapter.
I feel like I won't be able to answer with satisfaction till I have a good foundation...
I won’t claim Bayesian is the only conceptual framework, but I found it particularly intuitive and straightforward — and gives you a lot of flexibility. Refer an earlier discussion on Bayesian approaches a few days ago.
Why would someone want to paint but try and dodge the foundations of art theory?
Why would someone want to learn a new language but try and dodge learning language theory?
Why would someone want to learn to program but try and dodge learning category theory?
The book tries to explain a few things very well. Also the author is well aware when things are getting complicated, and he acknowledges it and holds the hand of the reader. Here is an excerpt from the book where he was explaining how to write a statistical model and ends with a reassuring note saying if it doesn't make sense it means they are holding the right book.
I wish more books would do that. Breaking the 4th wall.
Why would somebody want to become an airline pilot without doing a graduate degree in fluid dynamics?
Not everybody needs to drill down to the most fundamental levels of a discipline.
A job of a data analysis also requires one to adapt to the new situations, variations etc. The job is closer to that of a car mechanic than that of a car driver. Its not really a brick laying kind of a job.
Knowing the math is not a all or nothing deal, one need not get into its most sophisticated avatar right from scratch. However, if one one thinks that one can become something more than a mouse clicker, the person is just deluding oneself.
> "I ... read this book ... I like it!" - Andrew Gelman
Uh... sure if you know measure theory.
The later chapter especially in Dirichlet chapters assume you know measure theory.
I've always heard that it's a bit on the dry side of things, but haven't actually read it myself.
If you want something less technical then read Gelman and Hill 'Data Analysis Using Regression and Multilevel/Hierarchical Models', which is also great. More for scientists than statisticians, I'd say.
No. I would not recommend it unless you have a strong foundation in statistic.
If you want an introduction, I like
"Doing Bayesian Data Analysis: A Tutorial with R and BUGS" by John K. Kruschke. It's basically the Bayesian intro statistic version of Wackerly's statistic book (frequentist).
I was hoping he updated the other book for Hierarchical modeling with rStan.