October 1

Free Data Science eBooks – October 2017

Blog, Ebooks, Resources

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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.

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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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3 free Data Science Ebooks for October

This month, we have Machine Learning, Neural and Statistical ClassificationReport writing for Data Science in R and An Introduction to Statistical Learning with Applications in R. They're all FREE, so help yourselves.

Enjoy!

Disclosure: The FREE ebooks were free to download at the time of posting but other links in this post may contain affiliate links. As Amazon Associates we may earn from qualifying purchases.

​​You can find further details in our TCs

Machine Learning, Neural and Statistical Classification

Machine Learning, Neural and Statistical Classification

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.

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Report Writing for Data Science in R

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.

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An Introduction to Statistical Learning with Applications in R

An Introduction to Statistical Learning with Applications in R

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.

Related Books


Related Udemy Video Courses

Below are a few related Udemy courses that we recommend. They are by no means the only ones - there are LOADS more, and if these don't tickle your fancy, just click through. I'm sure you'll find something that does.

*NOTE - the prices listed below are the full price, and are not automatically updated when a sale is on. If you want to find out the sale price (often around 10 £/$/Euro), just click through!

Check out our 3 Free Data Science Ebooks for October #rprogramming #statistics

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free ebooks, r programming, statistics


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Machine Learning Models:

The Big Picture

FREE Ultra HD pdf

Download your FREE mind map to learn about the different types of ML models in Machine Learning.

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