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This book looks very close to what my girlfriend is looking for. She's interested in learning bioinformatics and it's been difficult to find a good book that in

by jsnk 11y ago

This book looks very close to what my girlfriend is looking for. She's interested in learning bioinformatics and it's been difficult to find a good book that introduces topics in data science in a digestible manner. If anyone knows the book, can you give a quick overview of how much, math, stats, programming and comp sci. you'd need to read this book? Thank you.

joelgrus 11y ago
I know the book, I wrote it! Most of the math is vector space arithmetic. There are a few sections that use matrix multiplication. The probability and stats is stuff like understanding probability distributions and Bayes's Theorem. (It's all covered in the book, but you'd need to be comfortable picking it up and using it.) In terms of programming, not much. Someone who's never programmed before would probably have a tough time, but the goal is that someone who is bright and hardworking and who can write fairly simple Python programs should not have a problem. Very little CS background required. Maybe basic data structures like list vs dict and so on.
- Goladus 11y ago
  Reading this comment for some reason makes me curious how much time the book spends addressing computer science fundamentals like cpu and memory. My guess is that it's included in bits and pieces along the way but I didn't see anything explicit in the table of contents. I'm thinking about it in terms of running computation in production environments where you may be constrained by available compute resources or budget. Some people have an intuitive grasp of cpu/memory/bandwidth and can do performance tuning as necessary, but those who don't can find themselves in situations where they waste a lot of resources, such as running a million parallel jobs that each have less than 1 second of CPU time, getting stuck after failing to request or provision nodes with sufficient memory, or performing unnecessary reads and writes.
a_bonobo 11y ago
She may also enjoy Vince Buffalo's Bioinformatics Data Skills http://shop.oreilly.com/product/0636920030157.do http://shop.oreilly.com/product/0636920030157.do It's more focused on how to analyze existing biological data with the shell, R, and how to use git. Personally, I've rarely seen advanced machine learning being used outside of genome-wide association studies, and even there most people just use PLINK's logistic regression without understanding what's being done and call it a day. Another really good book on how to understand statistics is Motulsky's Intuitive Biostatistics - it introduces all common "tests" and methodologies people working in the life sciences use, but without the formulas (you use R for that anyway). It's more about the caveats of each test, in which situation you'd use it, what can go wrong, how to interpret the results etc., all written in a very lively style.

by jsnk 11y ago

joelgrus 11y ago

I know the book, I wrote it! Most of the math is vector space arithmetic. There are a few sections that use matrix multiplication. The probability and stats is stuff like understanding probability distributions and Bayes's Theorem. (It's all covered in the book, but you'd need to be comfortable picking it up and using it.) In terms of programming, not much. Someone who's never programmed before would probably have a tough time, but the goal is that someone who is bright and hardworking and who can write fairly simple Python programs should not have a problem. Very little CS background required. Maybe basic data structures like list vs dict and so on.

Goladus 11y ago
Reading this comment for some reason makes me curious how much time the book spends addressing computer science fundamentals like cpu and memory. My guess is that it's included in bits and pieces along the way but I didn't see anything explicit in the table of contents. I'm thinking about it in terms of running computation in production environments where you may be constrained by available compute resources or budget. Some people have an intuitive grasp of cpu/memory/bandwidth and can do performance tuning as necessary, but those who don't can find themselves in situations where they waste a lot of resources, such as running a million parallel jobs that each have less than 1 second of CPU time, getting stuck after failing to request or provision nodes with sufficient memory, or performing unnecessary reads and writes.

a_bonobo 11y ago

She may also enjoy Vince Buffalo's Bioinformatics Data Skills http://shop.oreilly.com/product/0636920030157.do http://shop.oreilly.com/product/0636920030157.do It's more focused on how to analyze existing biological data with the shell, R, and how to use git. Personally, I've rarely seen advanced machine learning being used outside of genome-wide association studies, and even there most people just use PLINK's logistic regression without understanding what's being done and call it a day. Another really good book on how to understand statistics is Motulsky's Intuitive Biostatistics - it introduces all common "tests" and methodologies people working in the life sciences use, but without the formulas (you use R for that anyway). It's more about the caveats of each test, in which situation you'd use it, what can go wrong, how to interpret the results etc., all written in a very lively style.