As Adcadet mentioned, there are often several levels of Biostatistics that people take, depending on their backgrounds and how likely they are to use statistics in their work. At my school, there are three Biostats tracks, and the middle-level one is the most popular among MPH students in Epidemiology, International Health, etc.
There is a "recommended" textbook (Rosner - Fundamentals of Biostatistics), but it is really only for reference and for those who like learning by reading textbooks; it's not explicitly used in class. It is also quite expensive.
Another "recommended" source of information is the online introduction at
http://davidmlane.com/hyperstat/index.html which covers about the first 8-12 weeks of class. There are a lot of ads on the page, though, and thus it can be a bit visually distracting.
I will list for you the topics covered in my year-long introductory Biostatistics sequence; any book that covers these subjects in some detail and seems to explain things well should give you a pretty good foundation in Biostats. (Others, please let me know if I've left out anything vital.) This is excerpted from the course descriptions:
- basic concepts and methods of statistics
- methods of exploring, organizing, and presenting data
- fundamentals of probability, including probability distributions and conditional probability, with applications to 2x2 tables
- foundations of statistical inference, including concepts of population, sample parameter, and estimate
- approaches to inferences using the likelihood function, confidence intervals, and hypothesis tests
- use of likelihood functions, confidence intervals, and hypothesis tests to draw scientific inferences from public health data
- null and alternative hypotheses
- Type I and II errors
- power calculations
- parametric and non-parametric statistical methods for comparing multiple groups (ANOVA)
- measures of association
- simple linear regression
- methods for planning a study, including stratification, balance, sampling strategies, and sample size
- use of generalized linear models for quantitative analysis of data encountered in public health and medicine
- creation, evaluation, and application of different models
- analysis of variance (ANOVA)
- analysis of covariance (ANCOVA)
- multiple linear regression
- logistic regression
- Cox regression
- conduct and reporting of the results of a valid statistical analysis of quantitative public health information
- advanced skills in multiple regression models
- log-linear models
- techniques for the evaluation of survival and longitudinal data
- methods for the measurement of agreement, validity, and reliability
Note that these are general topic headings, and under each one, there are more specific methods, names of tests, and so forth.
A lot of what is emphasized in class, aside from how to perform various analyses and make models, is judgment and discretion in the use of models and analyses. (Just because you can use a particular analysis or model doesn't mean you should.) I don't know how well this could be learned from a book, unless it's a very good one.
Also, if you plan to use any of this, you'll probably want/need to learn to use a statistical computing package, such as SAS, R, SPSS, or Stata. SAS and R tend to be the most widely used in public health circles.
Hope this helps!
