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Data Strategy and the Enterprise Data Executive: Ensuring that Business and IT are in Synch in the Post-Big Data Era (Data Literacy Book 1)
Master a proven approach to create, implement, and sustain a data strategy.Pervasive, data is a unique organizational resource, and this distinction warrants its own strategy. Data, representing your single non-depletable, non-degradable, durable strategic asset, is likely also your most poorly leveraged and underutilized organizational asset. Lack of talent, barriers in organizational thinking, and seven specific data sins prevent most organizations from benefiting fully from their data asset investments. Solving these prerequisites will allow your organization to:
  • Improve your organization's data;
  • Improve the way your people use data; and
  • Improve the way your people use data to achieve your organizational strategy.
This method better focuses data and thinking in direct support of strategic objectives. After eliminating necessary prerequisites, organizations can develop a disciplined and repeatable means of improving their data, literacy, standards, and controls using data governance practices. Once in place, the process (based on the theory of constraints) becomes a variant of lather, rinse, and repeat. Several complementary concepts covered include:
  • An overview of data strategy prerequisites;
  • A repeatable process for identifying and removing data constraints;
  • Why data strategy is necessary for effective data governance;
  • Balancing operational results with capability development;
  • An objective definition of data-centric thinking; and
  • Ways to monetize these efforts.
Published by: Technics Publications | Publication date: 06/06/2017
Kindle book details: Kindle Edition, 250 pages

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data
Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you:
  • Become a contributor on a data science team
  • Deploy a structured lifecycle approach to data analytics problems
  • Apply appropriate analytic techniques and tools to analyzing big data
  • Learn how to tell a compelling story with data to drive business action
  • Prepare for EMC Proven Professional Data Science Certification
Corresponding data sets are available at Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
Published by: Wiley | Publication date: 01/05/2015
Kindle book details: Kindle Edition, 399 pages

B4b: How Technology and Big Data Are Reinventing the Customer-Supplier Relationship
Industry after industry is becoming technology-driven as software rapidly eats the world. As it spreads, so do complexity and opportunity. There are clear signs that the traditional B2B business model designed 125 years ago as a simple “make, sell, ship” approach for early manufacturing companies is no longer capable of delivering the full potential of high-tech and near-tech solutions. B4B seeks to frame what is possible in an age where suppliers are connected to their customers in real time. The traditional world of B2B was designed to sell things to customers, whereas the new B4B model will be about delivering outcomes for customers. It’s a whole new ballgame. Using powerful models and specific examples, B4B envisions a next-generation tech industry where suppliers play an active, ongoing role in helping business customers achieve unparalleled value from their technology investments.
Published by: Point B Inc/TSIA | Publication date: 10/14/2013
Kindle book details: Kindle Edition, 240 pages

The Art of Invisibility: The World's Most Famous Hacker Teaches You How to Be Safe in the Age of Big Brother and Big Data
Be online without leaving a trace.Your every step online is being tracked and stored, and your identity literally stolen. Big companies and big governments want to know and exploit what you do, and privacy is a luxury few can afford or understand.In this explosive yet practical book, Kevin Mitnick uses true-life stories to show exactly what is happening without your knowledge, teaching you "the art of invisibility"--online and real-world tactics to protect you and your family, using easy step-by-step instructions. Reading this book, you will learn everything from password protection and smart Wi-Fi usage to advanced techniques designed to maximize your anonymity. Kevin Mitnick knows exactly how vulnerabilities can be exploited and just what to do to prevent that from happening. The world's most famous--and formerly the US government's most wanted--computer hacker, he has hacked into some of the country's most powerful and seemingly impenetrable agencies and companies, and at one point was on a three-year run from the FBI. Now Mitnick is reformed and widely regarded as the expert on the subject of computer security. Invisibility isn't just for superheroes--privacy is a power you deserve and need in the age of Big Brother and Big Data.
Published by: Little, Brown and Company | Publication date: 02/14/2017
Kindle book details: Kindle Edition, 285 pages

Big data: La revolución de los datos masivos (Noema) (Spanish Edition)
Un análisis esclarecedor sobre uno de los grandes temas de nuestro tiempo, y sobre el inmenso impacto que tendrá en la economía, la ciencia y la sociedad en general. Los datos masivos representan una revolución que ya está cambiando la forma de hacer negocios, la sanidad, la política, la educación y la innovación.Dos grandes expertos en la materia analizan qué son los datos masivos, cómo nos pueden cambiar la vida, y qué podemos hacer para defendernos de sus riesgos.Un gran ensayo, único en español, pionero en su campo, y que se adelanta a una tendencia que crece a un ritmo frenético.
Published by: Turner | Publication date: 04/01/2016
Kindle book details: Kindle Edition, 320 pages

Data Science from Scratch with Python: Step-by-Step Beginner Guide for Statistics, Machine Learning, Deep learning and NLP using Python, Numpy, Pandas, Scipy, Matplotlib, Sciki-Learn, TensorFlow
Are you thinking of learning data science from scratch using Python? (For Beginners)If you are looking for a complete step-by-step guide to data science using Python from scratch, this book is for you. After his great success with his first book “Data Analysis from Scratch with Python”, Peter Morgan publishes his second book focusing now in data science and machine learning. It is considered by practitioners as the easiest guide ever written in this domain. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations.Step by Step Guide and Visual Illustrations and ExamplesThe Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python. This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn, pandas, NumPy, IPython, and Jupiter in the Process. Target Users
  • Beginners who want to approach data science, but are too afraid of complex math to start
  • Newbies in computer science techniques and data science
  • Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
  • Students and academicians, especially those focusing on data science
What’s Inside This Book?Part 1: Data Science Fundamentals, Concepts and Algorithms
  • Introduction
  • Statistics
  • Probability
  • Bayes’ Theorem and Naïve Bayes Algorithm
  • Asking the Right Question
  • Data Acquisition
  • Data Preparation
  • Data Exploration
  • Data Modelling
  • Data Presentation
  • Supervised Learning Algorithms
  • Unsupervised Learning Algorithms
  • Semi-supervised Learning Algorithms
  • Reinforcement Learning Algorithms
  • Overfitting and Underfitting
  • The Bias-Variance Trade-off
  • Feature Extraction and Selection
Part 2: Data Science in Practice
  • Overview of Python Programming Language
  • Python Data Science Tools
  • Jupyter Notebook
  • Numerical Python (Numpy)
  • Pandas
  • Scientific Python (Scipy)
  • Matplotlib
  • Scikit-Learn
  • K-Nearest Neighbors
  • Naive Bayes
  • Simple and Multiple Linear Regression
  • Logistic Regression
  • GLM models
  • Decision Trees and Random forest
  • Perceptrons
  • Backpropagation
  • Clustering
  • Natural Language Processing
Frequently Asked QuestionsQ: Does this book include everything I need to become a data science expert?A: Unfortunately, no. This book is designed for readers taking their first steps in data science and machine learning using Python and further learning will be required beyond this book to master all aspects.Q: Can I have a refund if this book doesn’t fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform.***** MONEY BACK GUARANTEE BY AMAZON ***** Editorial Reviews"This is a fantastic book on Python-based data science, data analysis, machine learning, Reinforcement learning and deep learning. As a data scientist with more than 10 years, Peter has had long experience in data science and give in this book the key elements.."- Lei Xia, Data Scientist Expert at Facebook
Author: Peter Morgan
Published by: AI Sciences LLC | Publication date: 08/20/2018
Kindle book details: Kindle Edition, 169 pages

Big Data and Machine Learning in Quantitative Investment (Wiley Finance)
Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. •    Gain a solid reason to use machine learning •    Frame your question using financial markets laws •    Know your data•    Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.
Author: Tony Guida
Published by: Wiley | Publication date: 12/12/2018
Kindle book details: Kindle Edition, 296 pages

When Big Data Was Small: My Life in Baseball Analytics and Drug Design
Richard D. Cramer has been doing baseball analytics for just about as long as anyone alive, even before the term “sabermetrics” existed. He started analyzing baseball statistics as a hobby in the mid-1960s, not long after graduating from Harvard and MIT. He was a research scientist for SmithKline and in his spare time used his work computer to test his theories about baseball statistics. One of his earliest discoveries was that clutch hitting—then one of the most sacred pieces of received wisdom in the game—didn’t really exist. In When Big Data Was Small Cramer recounts his life and remarkable contributions to baseball knowledge. In 1971 Cramer learned about the Society for American Baseball Research (SABR) and began working with Pete Palmer, whose statistical work is credited with providing the foundation on which SABR is built. Cramer cofounded STATS Inc. and began working with the Houston Astros, Oakland A’s, Yankees, and White Sox, with the help of his new Apple II computer. Yet for Cramer baseball was always a side interest, even if a very intense one for most of the last forty years. His main occupation, which involved other “big data” activities, was that of a chemist who pioneered the use of specialized analytics, often known as computer-aided drug discovery, to help guide the development of pharmaceutical drugs. After a decade-long hiatus, Cramer returned to baseball analytics in 2004 and has done important work with Retrosheet since then. When Big Data Was Small is the story of the earliest days of baseball analytics and computer-aided drug discovery.      
Published by: University of Nebraska Press | Publication date: 05/01/2019
Kindle book details: Kindle Edition, 251 pages

Mathematics of Big Data: Spreadsheets, Databases, Matrices, and Graphs (MIT Lincoln Laboratory Series)
The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies.Today, the volume, velocity, and variety of data are increasing rapidly across a range of fields, including Internet search, healthcare, finance, social media, wireless devices, and cybersecurity. Indeed, these data are growing at a rate beyond our capacity to analyze them. The tools—including spreadsheets, databases, matrices, and graphs—developed to address this challenge all reflect the need to store and operate on data as whole sets rather than as individual elements. This book presents the common mathematical foundations of these data sets that apply across many applications and technologies. Associative arrays unify and simplify data, allowing readers to look past the differences among the various tools and leverage their mathematical similarities in order to solve the hardest big data challenges.The book first introduces the concept of the associative array in practical terms, presents the associative array manipulation system D4M (Dynamic Distributed Dimensional Data Model), and describes the application of associative arrays to graph analysis and machine learning. It provides a mathematically rigorous definition of associative arrays and describes the properties of associative arrays that arise from this definition. Finally, the book shows how concepts of linearity can be extended to encompass associative arrays. Mathematics of Big Data can be used as a textbook or reference by engineers, scientists, mathematicians, computer scientists, and software engineers who analyze big data.
Published by: The MIT Press | Publication date: 07/13/2018
Kindle book details: Kindle Edition, 448 pages

The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences
"Carefully distinguishing between big data and open data, and exploring various data infrastructures, Kitchin vividly illustrates how the data landscape is rapidly changing and calls for a revolution in how we think about data."- Evelyn Ruppert, Goldsmiths, University of London"Deconstructs the hype around the ‘data revolution’ to carefully guide us through the histories and the futures of ‘big data.’ The book skilfully engages with debates from across the humanities, social sciences, and sciences in order to produce a critical account of how data are enmeshed into enormous social, economic, and political changes that are taking place."- Mark Graham, University of OxfordTraditionally, data has been a scarce commodity which, given its value, has been either jealously guarded or expensively traded.  In recent years, technological developments and political lobbying have turned this position on its head. Data now flow as a deep and wide torrent, are low in cost and supported by robust infrastructures, and are increasingly open and accessible.  A data revolution is underway, one that is already reshaping how knowledge is produced, business conducted, and governance enacted, as well as raising many questions concerning surveillance, privacy, security, profiling, social sorting, and intellectual property rights.  In contrast to the hype and hubris of much media and business coverage, The Data Revolution provides a synoptic and critical analysis of the emerging data landscape.  Accessible in style, the book provides:
  • A synoptic overview of big data, open data and data infrastructures
  • An introduction to thinking conceptually about data, data infrastructures, data analytics and data markets
  • Acritical discussion of the technical shortcomings and the social, political and ethical consequences of the data revolution
  • An analysis of the implications of the data revolution to academic, business and government practices
Author: Rob Kitchin
Published by: SAGE Publications Ltd | Publication date: 08/18/2014
Kindle book details: Kindle Edition, 238 pages
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