If you want to get started with Data Science and don’t like learning a new language such as R or Python, then this book is a perfect fit for you. Entertaining, Data Smart: Using Data Science to Transform Information into Insight approaches data science from a unusual angle. John W. Foreman has written a book for those who wants to apply data mining without using advanced programming (R, Python, etc.). It doesn’t mean you don’t need to understand what data science is, and this book is very good at explaining it to non-practitioners.
Foreman’s book is written in a nice and funny stile, which makes it an easy read. Data mining algorithms are described with the minimum equations needed. Foreman has written a practical book and thus decided to use Excel as a tool for data science. The book starts with an introduction to Excel and its most famous functions. For data scientists using SAS, R, Python or Matlab, you may discover how powerful Excel is. But you will also see how clumsy it is to use Excel for data science. Whereas you would need a few lines in R, the book will take you through a dozen pages of step by step actions you need to perform to obtain the same in Excel. Not only is it more time consuming but also more prone to errors.
Don’t get me wrong: Data Smart is excellent at explaining how to perform data science in Excel. I just think Excel is not the right tool for it. The book is also a journey into MailChimp, the author’s company. This is nice and provides plenty of examples related to e-mail marketing. The book thus provides quick and high-level description of the problem, followed by Excel steps to solve it. In conclusion, Data Smart is a must read to get a fresh perspective on data science with a “Data Science using Excel” user manual. And for the experts? You can just skip the Excel parts and get insights into the field, with a focus on MailChimp use cases.
One of the Predictive Analytics projects I am working on at Expedia uses Gradient Boosting Machine (GBM). This is currently one of the state of the art algorithms in Machine Learning. This article provides insights on how to get started and advices for further readings.
Without any suspense, “An Introduction to Statistical Learning” (ISL) by James, Witten, Hastie and Tibshirani is a key book in the Data Science literature. I would summarize it as a book written by statisticians for non-statisticians. Indeed, while the book “The Elements of Statistical Learning” was heavy on theory and equations, ISL is the practical counterpart. The book is very clear… Continue reading...
This is a guest post from William Blears, Founder of Perceptive Digital.
There’s been a lot of talk lately about how data is driving online marketing forward. What you don’t often hear is how this same data is empowering customers to make wiser purchasing decisions. I thought it’d be interesting to highlight five ways in which empowered consumers can benefit from an abundance of available data.
Automate This is a journey into the world of anything that can be automated, from stock picking to medical diagnosis. The author, Christopher Steiner, excels in telling stories and bringing interesting anecdotes to the reader. Although focused on the trading world, the book explores topics such as automated music creation, geopolitical analysis and poker playing.
Automate This is about the… Continue reading...
IoT and Analytics, April 13th, Lausanne. Event organized by the Swiss Association for Analytics. More information and free subscription at http://meetu.ps/e/Bl61H/mgXJj/d… Continue reading... | 6 Comments
This is a guest post by Jeremy Sutter.
Sometimes, technically minded people feel they are not good candidates for leadership positions. Sometimes, they feel like leadership requires more people skills than they have. However, in the era of big companies and big data, that may be changing. It may be the person with the best information and the best… Continue reading... | 4 Comments