Got your Christmas shopping done yet?
Nope, me neither - I usually don't start until about 24th December...
In which case, you've got plenty of time to devour the latest set of FREE Data Science books on offer.
This month, our first offering is not actually a Data Science book, but is still very relevant to Data Scientists. It's all about being a modern scientist and everything that entails, using social media and other online tools to maximise the exposure of your research.
The other two books are about data analysis and deep learning, and I hope you find them useful.
This month we highlight 3 books:
- How to be a Modern Scientist
- R Deep Learning Essentials
- Putting it all Together: Essays on Data Analysis
They're all FREE, so help yourselves...
The face of academia is changing. It is no longer sufficient to just publish or perish. We are now in an era where Twitter, Github, Figshare, and Alt Metrics are regular parts of the scientific workflow. Here I give high level advice about which tools to use, how to use them, and what to look out for. This book is appropriate for scientists at all levels who want to stay on top of the current technological developments affecting modern scientific careers.
The book is probably most suited to graduate students and postdocs in the sciences, but may be of interest to others who want to adapt their scientific process to use modern tools.
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning.
This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples.
What is a data analysis? What makes for a successful data analysis? These are difficult questions that even long-time practitioners have difficulty answering. The way that we have thought about data analysis to date has been focused on the data and the statistical tools that we employ to produce results. But data analysis is about more than those things, and developing an understanding of the things "outside" the data is critical to characterizing the actual process of data analysis, the process that data analysts go through every day.
This book attempts to draw a more complete picture of the data analysis process and presents a new view about what makes for a successful data analysis. It is presented in a completely non-technical and highly readable style that should be of interest to practitioners and managers in data analysis.