Free Data Science eBooks - October 2017
The leaves are browning and falling off the trees, the remnants of hurricanes Maria and Lee have just rattled through the UK and there's a chill in the air. Yup, autumn has definitely arrived!
So there's no better time to kick back, get comfy in your favourite armchair with a hot cup of coffee in one hand and some good reading material in the other.
Continuing our Back To School series, here are three free eBooks to help you on your educational journey as the nights get longer, cooler, wetter and windier.
I hope these books prove to be a valuable resource to you and that you will visit regularly (and invite your friends too).
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Disclosure: as well as links to the free ebooks we may also include links to non-free versions of the same books, and we may earn an affiliate commission for purchases you make when using those links
You can find further details in our TCs
This month, we have Machine Learning, Neural and Statistical Classification, Report writing for Data Science in R and An Introduction to Statistical Learning with Applications in R. They're all FREE, so help yourselves.
by D. Michie, D.J. Spiegelhalter, C.C. Taylor (eds)
The aim of this book is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems.
As the book's title suggests, a wide variety of approaches has been taken towards this task. Three main historical strands of research can be identified: statistical, machine learning and neural network.
NOTE: This book is out of print and is now being offered for free as a pdf only. If you would rather have a hardback copy for your office or department bookshelf you might be lucky enough to find a paid copy from Amazon ← *big fat scary affiliate link!*
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by Roger D. Peng
This book teaches the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducibility is the idea that data analyses should be published or made available with their data and software code so that others may verify the findings and build upon them. The need for reproducible report writing is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations.
Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available.
This book will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.
NOTE: This book is offered for free as a pdf only. If you would rather have a hardback copy for your office or department bookshelf you can get a paid copy from Amazon ← *big fat scary affiliate link!*
by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
This book provides an introduction to statistical learning methods.
It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences.
The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.
NOTE: This book is offered for free as a pdf only. If you would rather have a hardback copy or a Kindle version you can get a paid copy from Amazon ← *big fat scary affiliate link!*
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