1.Visualising Data
Enormous quantities of data go unused or underused today, simply because people can't visualise the quantities and relationships in it. Using a downloadable programming environment developed by the author, Visualising Data demonstrates methods for representing data accurately on the Web and elsewhere, complete with user interaction, animation, and more.
2.target="_blank">Implementing Analytics
Implementing Analytics demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possible for widespread adoption across all functions of an organisation. Implementing Analytics simplifies and helps democratise a very specialised discipline to foster business efficiency and innovation without investing in multi-million dollar technology and manpower.
3.How Algorithms Came to Rule Our World
In this fascinating, frightening book, Christopher Steiner tells the story of how algorithms took over—and shows why the “bot revolution” is about to spill into every aspect of our lives, often silently, without our knowledge.
4.Delivering Business Analytics: Practical Guidelines for Best Practice
This groundbreaking resource provides comprehensive coverage across all aspects of business analytics, presenting proven management guidelines to drive sustainable differentiation. Through a rich set of case studies, author Evan Stubbs reviews solutions and examples to over twenty common problems spanning managing analytics assets and information, leveraging technology, nurturing skills, and defining processes.
5.Advanced Database Marketing: Innovative Methodologies and Applications for Managing Customer Relationships
Advanced Database Marketing provides a state-of-the-art guide to the methods and applications that define this new era in database marketing, including advances in areas such as text mining, recommendation systems, internet marketing, and dynamic customer management.
6.Practical Data Science with R
Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyse the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
7.Analysing the Analysers: An Introspective Survey of Data Scientists and Their Work
Despite the excitement around "data science," "big data," and "analytics," the ambiguity of these terms has led to poor communication between data scientists and organisations seeking their help. In this report, authors Harlan Harris, Sean Murphy, and Marck Vaisman examine their survey of several hundred data science practitioners in mid-2012, when they asked respondents how they viewed their skills, careers, and experiences with prospective employers. The results are striking.
8.The Big Data Revolution
Want to learn how companies like Amazon, Target, and IBM use data to gain competitive advantages? Or how Obama used Big Data tools to better utilise his resources? The Big Data Revolution was written for the non-or-only-slightly-technical business person in mind-but in a way that gives you enough meat behind the ideas so that you have a road map that tells you how to get where you want to go. It uses real-world examples and case studies to illustrates the concepts and explore the technology that makes them happen.
9.Naked Statistics: Stripping the Dread from the Data
For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
10.Business Analytics: A Practitioner's Guide
This book provides a guide to businesses on how to use analytics to help drive from ideas to execution. Analytics used in this way provides “full lifecycle support” for business and helps during all stages of management decision-making and execution.
11.Mining of Massive Datasets
The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets.
12.Hadoop in Practice
Hadoop in Practice collects 85 Hadoop examples and presents them in a problem/solution format. Each technique addresses a specific task you'll face, like querying big data using Pig or writing a log file loader. You'll explore each problem step by step, learning both how to build and deploy that specific solution along with the thinking that went into its design.
13.Machine Learning in Action
Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarisation and simplification.
14.Data Mining: Concepts and Techniques
Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data.
15.Data Analysis with Open Source Tools
Author Philipp Janert teaches you how to think about data: how to effectively approach data analysis problems, and how to extract all of the available information from your data. Janert covers univariate data, data in multiple dimensions, time series data, graphical techniques, data mining, machine learning, and many other topics.
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