Over the years, a world full of supermarkets and shopping malls has desensitized our relationship with the environment and each other. We no longer know where our food or clothes come from, and the cashier will only make eye contact to take our payment. By contrast, when shopping online, it seems that almost every website knows all of our preferences in a personalized experience. FiveStars is a loyalty and shopping analytics platform for small brick-and-mortar retailers that aims to restore the balance. FiveStars has logged more than 35 million visits from 10 million consumers who have used the loyalty app through over 10,000 merchants.

If you have used (or attempted to use) Giraph you will know that it's not the easiest system to pick up and start getting your hands dirty. However, it is an extremely powerful tool that can be used to solve many interesting problems at scale. This book does a great job of taking you from the very beginning with simple, straightforward algorithms to more complex real-world applications and finally more advanced topics (e.g., data partitioning, out-of-core algorithms, running on the cloud, etc.). With this book you can start without knowing anything about Giraph or distributed graph processing systems in general (or even about graphs!) and learn how to solve many graph problems at scale. There are several things that I find so appealing about this book. The main things are: (1) the _numerous_ examples with step-by-step illustrations that help you understand what exactly is happening during an algorithm, (2) the fact that the example applications are motivated by real-world problems (e.g., recommendation systems, pagerank, etc.) whose solutions can then serve as a building block for your personal applications, and (3) the example code that will allow you to quickly get your hands dirty and start playing with the tool.

This is an excellent book both in depth and breadth of the topics covered. It gives descriptions, analyses, and insights about the most popular algorithms on various topics, and it covers many more areas than most books. The book is well integrated across the broad diversity of topics that are covered, and connections between methods and topics are pointed out throughout the book. I wouldn't agree with an earlier review that the descriptions are short or introductory. For most of the important topics, a lot of detail is provided in terms of algorithm description and pseudo-code.

An Amazon warehouse is a flurry of activity. Towering hydraulic arms lift heavy boxes toward the rafters. And an army of stubby orange robots slide along the floor like giant, sentient hockey pucks, piled high with towers of consumer gratification ranging from bestsellers to kitchenware. Those are Kiva robots, once the marvel of warehouses everywhere. Amazon whipped out its wallet and threw down 775 million to purchase these robot legions in 2012.

Scala is a programming language that makes it possible to write terse but efficient code. In today's fast paced world, learning languages like Scala pay great dividends when solving complex problems like building recommendation engines to optimize the customer experience. The big players like Google, Amazon, Linkedin and Facebook all employ recommendation engines to keep the customer coming back for more. The author of this book assumes no prior experience with Scala and starts from the beginning, explaining how to install Scala, the Scala Build Tool and Apache Spark. The author combines these with Apache Kafka and MongoDB to build a data processing pipeline able to glean upto the minute insights into customer data.

Infor, a leading provider of business applications, has announced the acquisition of Predictix, a provider of machine-learning solutions for retailers. Predictix will become part of Infor CloudSuite Retail, a new suite of enterprise applications delivered in the cloud and designed for today's retailing landscape. The acquisition comes six months after Infor announced an investment in Predictix. "The synergies between Infor and Predictix were greater than we could have hoped, and we've come to appreciate a great cultural alignment where both teams have passionate people who work hard and want to make a difference in retail and beyond," said Charles Phillips, CEO of Infor. "Buying out the other Predictix investors makes sense to bring the teams together and provide the scale and resources needed to accelerate the retail revolution."

As databases keep growing unabatedly, so too has the need for smart data mining. For a competitive edge in business, it helps to be able to analyse your data in unique ways. This text gives you a thorough education in state of the art data mining. The extensive problem sets are well suited for the student. These often expand on concepts in the narrative, and are worth tackling.

Madhavan's book has proven useful for some of the projects I'm working on. The first chapter includes a brief primer on Numpy and Pandas--useful for someone that is new to the Python ecosystem, but assuming you are already familiar with those packages, it should be okay to skip to the second chapter. The second chapter includes some Python statistical examples that I have not seen in other texts, but are important when looking at different types of distributions. These distribution examples and explanations are a must-have in my collection of Python recipes. There are also data visualization tweaks that I've not seen in other Data Science Python texts.

The way we are used to do things and draw decisions is about to change with the oncoming digital evolution. Software and hardware are converging in the Internet of Things and devices driven by machine learning will bring in machine-like accuracy and speed to support data driven human decisions and actions. In today's world, we need to designs solutions for customer engagement in the digital age, and regularly rework our industry scenarios to stay ahead of the game. There is a call for design of data driven, machine learning enabled enterprise software to run on state-of-the-art connected devices. As the amount of data continues to grow, it is essential to utilize the power of deep learning to urge better decisions throughout the customer engagement process, and to run on the next generation of connected devices.

There are a few key points and reasons here. The first is that the book starts all the way from installing the tools and performing all of the basic coding that will be needed later in the book. This fundamentals in the first portion is something that many other books I have purchased did not cover. The book actually exposed me to a few new libraries that I found personally useful including a hands on approach with numl and accord.net,