I have recently graduated with my masters degree on medical and biological modeling, accompanied with engineering mathematics as a background. Even though my education program included a significant amount of courses on mathematical statistics (see below for a list), which I managed with pretty high grades, I frequently end up completely lost staring down on both theory and applications of statistics. I have to say, compared to "pure" mathematics, statistics really makes little sense to me. Especially the notations and language used by most statisticians (including my past lecturers) is annoyingly convoluted and almost none of the resources I have seen so far (including wikipedia) had simple examples that one could easily relate to, and associate to the theory given…
This being the background; I also realize the bitter reality that I cannot have career as a researcher/engineer without a firm grip on statistics, especially within the field of bioinformatics.
I was hoping that I could get some tips from more experienced statisticians/mathematicians. How can I overcome this problem I have mentioned above? DO you know of any good resources; such as books, e-books, open courses (via iTunes or OpenCourseware for ex) etc..
EDIT: As I have mentioned I am quite biased (negatively) towards a majority of the literature under general title of statistics, and since I can't buy a number of large (and expensive) coursebooks per branch of statistics, what I would need in terms of a book is something similar to what Tipler & Mosca
is for Physics, but instead for statistics.
For those who don't know about Tipler; it's a large textbook that covers a wide majority of the subjects that one might encounter during higher studies, and presents them each from basic introduction to slightly deeper in detail. Basically a perfect reference book, bought it during my first year in uni, still use it every once in a while.
The courses I have taken on statistics:
- a large introduction course,
- stationary stochastic processes,
- Markov processes,
- Monte Carlo methods
- Survival analysis
Best Answer
I can completely understand your situation. Even though I am PhD student, I find it hard sometimes to related theory and application. If you are willing to immerse yourself in understanding theory, it is definitely rewarding when you think about real world problems. But the process may be frustrating.
One of the many references that I like is Gelman and Hill's Data Analysis Using Hierarchical/Multilevel Models. They avoid the theory where they can express the underlying concept using simulations. It will definitely benefit you as you have experience in MCMC etc. As you say, you are working in bioinformatics, probably Harrell's Regression Modeling Strategies is a great reference too.
I will make this a community wiki and let others add to it.