Our regular newsletter subscribers know that every month we scour the internet seeking out free eBooks to help you on your educational journey. Well, it has been so popular that rather than just send the links in the newsletters we decided to create a resources section here on the website.
I hope these books prove to be a valuable resource to you and that you will visit regularly (and invite your friends too).
If you haven't subscribed to our newsletter yet, why not subscribe using the form on the right - you'll be the very first to know when new resources are published.
This month, we have a couple of Data Science eBooks and one about programming in Python. They're all FREE, so there's no excuse not to get stuck in and improve your employability index...
by Jeffrey Stanton
In this Introduction to Data Science eBook, a series of data problems of increasing complexity is used to illustrate the skills and capabilities needed by data scientists.
The open source data analysis program known as "R" and its graphical user interface companion "R-Studio" are used to work with real data examples to illustrate both the challenges of data science and some of the techniques used to address those challenges.
To the greatest extent possible, real datasets reflecting important contemporary issues are used as the basis of the discussions.
by Annalyn Ng and Kenneth Soo
Numsense! is lovingly put together by two data science enthusiasts, Annalyn Ng (University of Cambridge) and Kenneth Soo (Stanford University), who wrote:
"We noticed that while data science is increasingly used to improve workplace decisions, many people know little about the field. Hence, we wrote these tutorials so that everyone and anyone can learn - be it an aspiring student or enterprising business professional".
Each tutorial covers the important functions and assumptions of a data science technique, without any maths or jargon. The book also illustrates these techniques with real-world data and examples.
by Zed A. Shaw
In Learn Python the Hard Way, Third Edition, you'll learn Python by working through 52 brilliantly crafted exercises.
Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run.
As you do, you'll learn how software works; what good programs look like; how to read, write, and think about code; and how to find and fix your mistakes using tricks professional programmers use.
Most importantly, you'll learn how to install a complete Python environment, organise and write code, basic coding constructs, game development and more.