It also offers an overview of big data technologies, explains what is needed to succeed with big data, and gives examples of both successful and failed data practices undertaken by startups, online firms, and large companies. He recognizes big online companies like Google or Facebook as the originators of best big data tools and technologies, as well as data-driven management reporting and best practices. Critics point out that the book offers rather a breezy approach to the subject as it refrains from using technical language, thus it avoids answering some of the rudimentary questions.
Best for: business data analysts, consultants, and graduate students in business analytics. This is a real data analytics manual that would suit readers who already have the basic knowledge of data mining and business intelligence and are looking for structural and technical instructions on how to conduct big data analytics in real-world business management. The author then proceeds with highlighting the most important steps of the process model, such as sampling, treatment of missing values, and variable selection.
The subsequent chapters focus on predictive and descriptive analytics. Additionally, numerous case studies on risk management, fraud detection, customer relationship management, and web analytics are included and described in detail. In the seventh chapter, the author provides us with concrete instructions on which business analytics tools , and practices, to use to put analytics to work.
Topics covered here range from backtesting and benchmarking approaches to data quality issues, software tools, and model documentation practices. Designed to be an accessible resource, this essential big data book does not include an exhaustive coverage of all analytical techniques. Instead, it highlights data analytics techniques that really provide added value in business environments. Best for: someone who has read a few intro books on data science and is ready to challenge themselves and dive deeper.
This is facilitated by the use of technical sections which the reader can choose to skip or devour according to their interest. Best for: Any lay person with no prior background in math or analytics, who wants to work in this field or to manage other data scientists. Data Science for the Layman is a great little book. Not only could it be a fine introduction for someone with little if any knowledge of data science, but it also provides nice summaries of several different areas for those with familiarity.
Five stars for doing what the title says. For big data books geared toward the practical application of digital insights, Numsense! Get BOOK. Big Data represents a new era in data exploration and utilization, and IBM is uniquely positioned to help clients navigate this transformation. This book reveals how IBM is leveraging open source Big Data technology, infused with IBM technologies, to deliver a robust, secure, highly available, enterprise-class Big Data platform.
The three defining characteristics of Big Data--volume, variety, and velocity--are discussed. Industry use cases are also included in this practical guide. Big Data in Practice. The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Overview: In this book, the author explains what Big Data means and why everyone in business should know about it.
The book covers all the bases, such as what Big Data means from a technical, consumer, and management perspective, what are the opportunities and costs involved, where it can have a real business impact and which aspects of this hot topic have been oversold.
This book will also help you understand the reasons behind why big data is important to you and your organization, what technology you need to manage it, how big data could change your job, your company, and your industry, and other such relevant topics.
Overview: This book on Big Data teaches you to build Big Data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Overview: This book fills the knowledge gap by showing how major companies are using Big Data every day, from an up-close, on-the-ground perspective. Here, you can learn how for each company profiled, learn what data was used, what problem it solved and the processes put in place to make it practical, as well as the technical details, challenges, and lessons learned from each unique scenario.
Overview: The goal of this book is to demystify the term Big Data and to give practical ways to leverage this data using data science and machine learning. Part One of this book includes the story of big data, AI and machine learning, use cases for big data analytics. Part Two includes making the big data ecosystem work for an organisation, big data can help guide your strategy, etc.
Overview: This book is written with a strong practitioner focus on the topic of Big data and analytics. It also includes what Big data can do for you, understanding the analytics, obstacles, and importance of Big data and its challenges, etc.
This book reveals how IBM is leveraging open source Big Data technology, infused with IBM technologies, to deliver a robust, secure, highly available, enterprise-class Big Data platform. Download or read it online for free here: Download link 4.
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