Data mining, data analysis, these are the two terms that very often make the impressions of being very hard to understand – complex – and that you’re required to have the highest grade education in order to understand them.
I can only disagree, and as with anything in this wonderful life of ours, we only need to spend a certain amount of time learning something, practicing it, before we realize that it’s not really all that hard.
It’s difficult to see what is behind a closed door, and unless we go up to that door and open it, to see what is behind it, we’re never going to know. Though this applies to most things in life, but I can definitely feel the ‘fear’ that people have of such complex studies as data science itself.
No doubt that there are very smart people in this world, working for large corporations such as Google, Apple, Microsoft and plenty more, but if we continue to look up to them; we will always think it’s hard because we have never given ourselves the chance to look at real examples and facts.
By learning from these books, you will quickly uncover the ‘secrets’ of data mining and data analysis, and hopefully be able to make better judgment of what they do, and how they can help you in your working projects, both now and in the future.
I just want to say that, in order to learn these complex subjects, you need to have a completely open mind, be open to every possibility, because that is usually where all the learning happens, and no doubt your brain is going to set itself on fire; multiple times.
This course is a combination of video instruction and tutorials, skill-building worksheets and templates, step-by-step guides, and an interactive forum for personalized responses and feedback to help you with your data analysis. It is a paid course, but the value you’re going to get from it is quite invaluable, compared to taking an expensive course from an agency.
It’s a few hours of content, and it will keep you busy for at least a month; granted that you complete all the quizzes, and actually take it to the next level. I’m recommending this as I find the community support for this particular course to be very appealing to beginners.
DJ Patil gives us a brief introduction to the complexity of data problems, how to look at them from a better perspective, and whether we should bother trying to solve the impossible. He gives perfectly good and understandable examples and is a nice little data book to add to your collection, it’s quality knowledge free of charge.
You can grab a copy of this book by filling out the fields on the right-hand side. (I think filling them blank also works)
This Wikibook aims to fill this gap by integrating three pieces of information for each technique: description and rationale, implementation details, and use cases.
The description and rationale of each technique provide the necessary background for understanding the implementation and applying it to real scenarios. The implementation details not only expose the algorithm design but also explain its parameters, in the light of the rationale provided previously.
Finally, the use cases provide an experience of the algorithms use on synthetic and real datasets.
This book is exactly what I was talking about at the beginning of this post, it features plenty of real-life experiences, that are aimed at beginners to help you better understand the whole process of data manipulation, and how algorithms work.
It’s apparently a work in progress, but there are plenty of chapters already available, though it seems that the last one is a few months overdue right now. Nonetheless, the first few chapters are essential to grasp the basics and highly recommended.
This is a very high-quality book that has more advanced techniques and ways of doing things included, it’s still being edited/written and is set to be released at some point, later this year.
It’s perfect for those learners who like to learn from illustrations and plenty of real-life examples.
I mentioned some large companies like Google and Apple, and the reason for that is very simple: we see data mining and analysis everywhere, not just specific sciences and subjects.
In reality, platforms such as Google Analytics heavily depend on algorithms that have been built on top of high quality data science knowledge, and the same goes for advertising companies, which is the main topic of discussion in this white-paper / eBook.
Jeffrey M. Stanton briefs of us on data science, and how it essentially is more than just a set of tasks related to data mining. In his own words, it’s more of an art form that, interacts with more industries than some may believe.
In addition, data science is much more than simply analyzing data. There are many people who enjoy analyzing data and who could happily spend all day looking at histograms and averages, but for those who prefer other activities, data science offers a range of roles and requires a range of skills. Let’s consider this idea by thinking about some of the data involved in buying a box of cereal.
In a couple of short words, this book is perfect for those who want to learn more about data mining on the web, and it discusses the most common set of problems when designing for the web and working with data that the web is giving us.
It will provide you with plenty of examples and tasks to do at the end of each section, and is also a fairly beginner-friendly book; requiring you to have some previous experience with data algorithms, some math, and database experience wouldn’t hurt either.
School of Data is a great place to be, they offer a wide variety of courses targeted at all levels of expertise, and this Handbook is perfect alongside their course material. What I really love about this handbook is that it gives you plenty of follow-up links on the web, to make project creation easier.
A good example is linked to websites that have previously built data sets, essential to those who want to learn more about data and how it works!
We are going to conclude our list of free books for learning data mining and data analysis, with a book that has been put together in nine chapters, and pretty much each chapter is written by someone else; but it all makes perfect sense together.
The main focus of this book is text mining, and the evolution of web technology and how that is making an impact on data science and overall analysis. A great book to have!
Learn Data Science from Free Books
There is no better way to learn than from books, and then going out in the world and putting that new-found knowledge to the test, or otherwise, we’re bound to forget what we actually had learned. This is a beautiful list of books that every aspiring data scientist should take note of, and add to his list of learning materials.
What books have you read in order to help you begin your own journey in data mining and analysis? I’m sure that the community would love to hear more, and I’m eager to see what I potentially let slip through my fingers myself.