Introducing you to data science through an easy to understand conceptual framework and immediate practice using RapidMiner
Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions.
A lot has happened since the first edition of this book was published in 2014. There is hardly a day where there is no news on data science, machine learning, or artificial intelligence in the media.
It is interesting that many of those news articles have a skeptical, if not an even negative tone. All this underlines two things: data science and machine learning are finally becoming mainstream. And people know shockingly little about it.
Readers of this book will certainly do better in this regard, as it is a valuable resource to not only educate about how to use data science in practice, but also how the fundamental concepts work.
What you’ll learn in this edition
This second edition includes additional chapters on deep learning and recommendation systems. Another focus area is using text analytics and natural language processing. It became clear in the past years that the most successful predictive models have been using unstructured input data in addition to the more traditional tabular formats. Finally, expansion of time series forecasting should get you started on one of the most widely applied data science techniques in the business.
More algorithms could mean that there is a risk of increased complexity. But thanks to the simplicity of the RapidMiner platform and the many practical examples throughout the book this is not the case here. We continue our journey towards the democratization of data science and machine learning. This journey continues until data science and machine learning are as ubiquitous as data visualization or Excel.
“This book is an important work to help analytics teams rapidly bridge the data science skills gap. The hands-on application of advanced analytics and machine learning will enable readers to competently solve high value use cases.”
Of course, we cannot magically transform everybody into a data scientist overnight, but we can give people the tools to help them on their personal path of development. This book is the only tour guide you need on this journey.
