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Everybody Lies: Big Data, New Data, and What the Internet Can Tel...
by Seth Stephens-Davidowitz

Language

English

Pages

357

Publication Date

May 09, 2017

Product Description
Customer Reviews
<p>Foreword by Steven Pinker</p><p>Blending the informed analysis of <em>The Signal and the Noise</em> with the instructive iconoclasm of <em>Think Like a Freak</em>, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world—provided we ask the right questions.</p><p>By the end of an average day in the early twenty-first century, human beings searching the internet will amass eight trillion gigabytes of data. This staggering amount of information—unprecedented in history—can tell us a great deal about who we are—the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable. </p><p><em>Everybody Lies</em> offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women? </p><p>Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential—revealing biases deeply embedded within us, information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health—both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. <em>Everybody Lies</em> challenges us to think differently about how we see it and the world.</p>
Designing Data-Intensive Applications: The Big Ideas Behind Relia...
by Martin Kleppmann

Language

English

Pages

624

Publication Date

March 16, 2017

Product Description
Customer Reviews
<div><p>Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?</p><p>In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.</p><ul><li>Peer under the hood of the systems you already use, and learn how to use and operate them more effectively</li><li>Make informed decisions by identifying the strengths and weaknesses of different tools</li><li>Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity</li><li>Understand the distributed systems research upon which modern databases are built</li><li>Peek behind the scenes of major online services, and learn from their architectures</li></ul></div>
Blockchain Basics: A Non-Technical Introduction in 25 Steps
by Daniel Drescher

Language

English

Pages

255

Publication Date

March 14, 2017

Product Description
Customer Reviews
<p>In 25 concise steps, you will learn the basics of blockchain technology. No mathematical formulas, program code, or computer science jargon are used. No previous knowledge in computer science, mathematics, programming, or cryptography is required. Terminology is explained through pictures, analogies, and metaphors.</p><p>This book bridges the gap that exists between purely technical books about the blockchain and purely business-focused books. It does so by explaining both the technical concepts that make up the blockchain and their role in business-relevant applications.</p><p></p><p><b>What You'll Learn</b></p><ul><li>What the blockchain is<br /></li><li>Why it is needed and what problem it solves<br /></li><li>Why there is so much excitement about the blockchain and its potential<br /></li><li>Major components and their purpose<br /></li><li>How various components of the blockchain work and interact<br /></li><li>Limitations, why they exist, and what has been done to overcome them<br /></li><li>Major application scenarios</li></ul><p></p><p></p><p></p><p></p><p></p><p></p><p></p><p><b>Who This Book Is For</b></p><p> </p><p>Everyone who wants to get a general idea of what blockchain technology is, how it works, and how it will potentially change the financial system as we know it</p><p><br /></p>
Data Science for Business: What You Need to Know about Data Minin...
by , Tom Fawcett

Language

English

Pages

414

Publication Date

July 27, 2013

Product Description
Customer Reviews
<div><p>Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.</p><p>Based on an MBA course Provost has taught at New York University over the past ten years, <i>Data Science for Business</i> provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.</p><ul><li>Understand how data science fits in your organization—and how you can use it for competitive advantage</li><li>Treat data as a business asset that requires careful investment if you’re to gain real value</li><li>Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way</li><li>Learn general concepts for actually extracting knowledge from data</li><li>Apply data science principles when interviewing data science job candidates</li></ul></div>
Practical Statistics for Data Scientists: 50 Essential Concepts
by , Andrew Bruce

Language

English

Pages

318

Publication Date

May 10, 2017

Product Description
Customer Reviews
<div><p>Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.</p><p>Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.</p><p>With this book, you’ll learn:</p><ul><li>Why exploratory data analysis is a key preliminary step in data science</li><li>How random sampling can reduce bias and yield a higher quality dataset, even with big data</li><li>How the principles of experimental design yield definitive answers to questions</li><li>How to use regression to estimate outcomes and detect anomalies</li><li>Key classification techniques for predicting which categories a record belongs to</li><li>Statistical machine learning methods that “learn” from data</li><li>Unsupervised learning methods for extracting meaning from unlabeled data</li></ul></div>
SQL in 10 Minutes, Sams Teach Yourself: Sams Teac Your SQL 10 Min...
by Ben Forta

Language

English

Pages

287

Publication Date

October 25, 2012

Product Description
Customer Reviews
Sams Teach Yourself SQL in 10 Minutes, Fourth Edition New full-color code examples help you see how SQL statements are structured Whether you're an application developer, database administrator, web application designer, mobile app developer, or Microsoft Office users, a good working knowledge of SQL is an important part of interacting with databases. And Sams Teach Yourself SQL in 10 Minutes offers the straightforward, practical answers you need to help you do your job. Expert trainer and popular author Ben Forta teaches you just the parts of SQL you need to know–starting with simple data retrieval and quickly going on to more complex topics including the use of joins, subqueries, stored procedures, cursors, triggers, and table constraints. You'll learn methodically, systematically, and simply–in 22 short, quick lessons that will each take only 10 minutes or less to complete. With the Fourth Edition of this worldwide bestseller, the book has been thoroughly updated, expanded, and improved. Lessons now cover the latest versions of IBM DB2, Microsoft Access, Microsoft SQL Server, MySQL, Oracle, PostgreSQL, SQLite, MariaDB, and Apache Open Office Base. And new full-color SQL code listings help the beginner clearly see the elements and structure of the language. 10 minutes is all you need to learn how to... Use the major SQL statements Construct complex SQL statements using multiple clauses and operators Retrieve, sort, and format database contents Pinpoint the data you need using a variety of filtering techniques Use aggregate functions to summarize data Join two or more related tables Insert, update, and delete data Create and alter database tables Work with views, stored procedures, and more Table of Contents 1 Understanding SQL 2 Retrieving Data 3 Sorting Retrieved Data 4 Filtering Data 5 Advanced Data Filtering 6 Using Wildcard Filtering 7 Creating Calculated Fields 8 Using Data Manipulation Functions 9 Summarizing Data 10 Grouping Data 11 Working with Subqueries 12 Joining Tables 13 Creating Advanced Joins 14 Combining Queries 15 Inserting Data 16 Updating and Deleting Data 17 Creating and Manipulating Tables 18 Using Views 19 Working with Stored Procedures 20 Managing Transaction Processing 21 Using Cursors 22 Understanding Advanced SQL Features Appendix A: Sample Table Scripts Appendix B: Working in Popular Applications Appendix C : SQL Statement Syntax Appendix D: Using SQL Datatypes Appendix E: SQL Reserved Words
Microsoft Excel Data Analysis and Business Modeling
by Wayne Winston

Language

English

Pages

863

Publication Date

November 29, 2016

Product Description
Customer Reviews
<B> <I> <P style="MARGIN: 0px">This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book.</P> <P style="MARGIN: 0px"> </I> </B> </P> <P style="MARGIN: 0px">Master business modeling and analysis techniques with Microsoft Excel 2016, and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands on, scenario-focused guide helps you use Excel’s newest tools to ask the right questions and get accurate, actionable answers. This edition adds 150+ new problems with solutions, plus a chapter of basic spreadsheet models to make sure you’re fully up to speed.</P> <P style="MARGIN: 0px"> <BR> </P> <P style="MARGIN: 0px"> <B>Solve real business problems with Excel–and build your competitive advantage</B> </P> <UL> <LI>Quickly transition from Excel basics to sophisticated analytics</LI> <LI>Summarize data by using PivotTables and Descriptive Statistics</LI> <LI>Use Excel trend curves, multiple regression, and exponential smoothing</LI> <LI>Master advanced functions such as OFFSET and INDIRECT</LI> <LI>Delve into key financial, statistical, and time functions</LI> <LI>Leverage the new charts in Excel 2016 (including box and whisker and waterfall charts)</LI> <LI>Make charts more effective by using Power View</LI> <LI>Tame complex optimizations by using Excel Solver</LI> <LI>Run Monte Carlo simulations on stock prices and bidding models</LI> <LI>Work with the AGGREGATE function and table slicers</LI> <LI>Create PivotTables from data in different worksheets or workbooks</LI> <LI>Learn about basic probability and Bayes’ Theorem</LI> <LI>Automate repetitive tasks by using macros</LI> </UL>
Pragmatic AI: An Introduction to Cloud-Based Machine Learning
by Noah Gift

Language

English

Pages

272

Publication Date

July 12, 2018

Product Description
Customer Reviews
<p style="margin:0px;"> <b>Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning</b> </p> <p style="margin:0px;"> <br /> </p> <p style="margin:0px;"> <i> <b>Pragmatic AI</b> </i> will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science.</p> <p style="margin:0px;"> </p> <p style="margin:0px;">Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value.</p> <p style="margin:0px;"> </p> <p style="margin:0px;">Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment.</p> <ul> <li>Get and configure all the tools you’ll need</li> <li>Quickly review all the Python you need to start building machine learning applications</li> <li>Master the AI and ML toolchain and project lifecycle</li> <li>Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn</li> <li>Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems</li> <li>Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services</li> <li>Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more</li> <li>Work with Microsoft Azure AI APIs</li> <li>Walk through building six real-world AI applications, from start to finish</li> </ul> <p style="margin:0px;"> <i>Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.</i> </p>
Data Professionals at Work
by Malathi Mahadevan

Language

English

Pages

356

Publication Date

October 11, 2018

Product Description
Customer Reviews
<div>Enjoy reading interviews with more than two dozen data professionals to see a picture of what it’s like to work in the industry managing and analyzing data, helping you to know what it takes to move from your current expertise into one of the fastest growing areas of technology today. <i>Data</i> is the hottest word of the century, and data professionals are in high demand. You may already be a data professional such as a database administrator or business intelligence analyst. Or you may be one of the many people who want to work as a data professional, and are curious how to get there. Either way, this collection helps you understand how data professionals work, what makes them successful, and what they do to keep up.</div><br />You’ll find interviews in this book with database administrators, database programmers, data architects, business intelligence professionals, and analytics professionals. Interviewees work across industry sectors ranging from healthcare and banking to finance and transportation and beyond. Each chapter illuminates a successful professional at the top of their game, who shares what helped them get to the top, and what skills and attitudes combine to make them successful in their respective fields.<div><br /></div><div><div>Interviewees in the book include: Mindy Curnutt, Julie Smith, Kenneth Fisher, Andy Leonard, Jes Borland, Kevin Feasel, Ginger Grant, Vicky Harp, Kendra Little, Jason Brimhall, Tim Costello, Andy Mallon, Steph Locke, Jonathan Stewart, Joseph Sack, John Q. Martin, John Morehouse, Kathi Kellenberger, Argenis Fernandez, Kirsten Benzel, Tracy Boggiano, Dave Walden, Matt Gordon, Jimmy May, Drew Furgiuele, Marlon Ribunal, and Joseph Fleming. All of them have been successful in their careers, and share their perspectives on working and succeeding in the field as data and database professionals. </div></div><div><br /></div><div><b>What You'll Learn</b></div><div><b><br /></b></div><div><ul><li>Stand out as an outstanding professional in your area of data work by developing the right set of skills and attitudes that lead to success<br /></li><li>Avoid common mistakes and pitfalls, and recover from operational failures and bad technology decisions<br /></li><li>Understand current trends and best practices, and stay out in front as the field evolves</li><li>Break into working with data through database administration, business intelligence, or any of the other career paths represented in this book<br /></li><li>Manage stress and develop a healthy work-life balance no matter which career path you decide upon<br /></li><li>Choose a suitable path for yourself from among the different career paths in working with data<br /></li></ul></div><div><b><br /></b></div><div><b>Who This Book Is For</b></div><div><b><br /></b></div><div><br /></div>Database administrators and developers, database and business intelligence architects, consultants, and analytic professionals, as well as those intent on moving into one of those career paths. Aspiring data professionals and those in related technical fields who want to make a move toward managing or analyzing data on a full-time basis will find the book useful. Existing data professionals who want to be outstanding and successful at what they do will also appreciate the book's advice and guidance.
Hands On Machine Learning with Python: Concepts and Applications ...
by John Anderson

Language

English

Pages

225

Publication Date

August 01, 2018

Product Description
Customer Reviews
<h4> ***** BUY NOW (will soon return to 15.77 $) *****</h4><br /><h1>Are you thinking of learning more about Machine Learning using Python? (For Beginners)</h1><br />This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions.<br />This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using machine learning.<br /><p></p><br /><h2> From AI Sciences Publisher </h2><br />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. It will help you in preparing a solid foundation and learn any other high-level courses.<br />To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations.<br /><p></p><br /><h2> Target Users</h2><br />The book designed for a variety of target audiences. The most suitable users would include: <br /><ul><li>Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. </li><li>Software developers and engineers with a strong programming background but seeking to break into the field of machine learning.</li><li>Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird’s eye view of current techniques and approaches.</li></ul><br /><p></p><br /><h2>What’s Inside This Book? </h2><br />< <ul><li>Overview of Python Programming Language</li><li>Statistics</li><li>Probability</li><li>The Data Science Process</li><li>Machine Learning</li><li>Supervised Learning Algorithms</li><li>Unsupervised Learning Algorithms</li><li>Semi-supervised Learning Algorithms</li><li>Reinforcement Learning Algorithms</li><li>Overfitting and Underfitting</li><li>Python Data Science Tools</li><li>Jupyter Notebook</li><li>Numerical Python (Numpy)</li><li>Pandas</li><li>Scientific Python (Scipy)</li><li>Matplotlib</li><li>Scikit-Learn</li><li>K-Nearest Neighbors</li><li>Naive Bayes</li><li>Simple and Multiple Linear Regression</li><li>Logistic Regression</li><li>Generalized Linear Models</li><li>Decision Trees and Random Forest</li><li>Neural Networks</li><li>Perceptrons</li><li>Backpropagation</li><li>Clustering</li><li>K-means with Scikit-Learn</li><li>Bottom-up Hierarchical Clustering</li><li>K-means Clustering</li><li>Network Analysis</li><li>Betweenness centrality</li><li>Eigenvector Centrality</li><li>Recommender Systems</li><li>Multi-Class Classification</li><li>Popular Classification Algorithms</li><li>Support Vector Machine</li><li>Deep Learning using TensorFlow</li><li>Deep Learning Case Studies</li></ul><br /><hr><br /><h2>Frequently Asked Questions</h2><br /><p></p><br />Q: Is this book for me and do I need programming experience?<br />A: If you want to smash Machine Learning from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, you’ll be OK.<br /><p></p><br />Q: Does this book include everything I need to become a Machine Learning expert?<br />A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning.<br /><p></p><br />Q: Can I have a refund if this book doesn’t fit for me?<br />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. We will also be happy to help you if you send us an email (email address inside the book).<br /><h4>***** MONEY BACK GUARANTEE BY AMAZON ***** </h4><br />

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