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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>
The Model Thinker: What You Need to Know to Make Data Work for Yo...
by Scott E. Page

Language

English

Pages

398

Publication Date

November 27, 2018

Product Description
Customer Reviews
<div><b>How anyone can become a data ninja</b><br /><br /> From the stock market to genomics laboratories, census figures to marketing email blasts, we are awash with data. But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In <i>The Model Thinker</i>, social scientist Scott E. Page shows us the mathematical, statistical, and computational models--from linear regression to random walks and far beyond--that can turn anyone into a genius. At the core of the book is Page's "many-model paradigm," which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. <i>The Model Thinker </i>provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.<br /><br /></div>
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>
Data Science from Scratch with Python: Step-by-Step Beginner Guid...
by , AI Sciences

Language

English

Pages

169

Publication Date

August 20, 2018

Product Description
Customer Reviews
<h4> ***** BUY NOW (will soon return to 15.77 $) *****</h4><br /><h1>Are you thinking of learning data science from scratch using Python? (For Beginners)</h1><br />If you are looking for a complete step-by-step guide to data science using Python from scratch, this book is for you. <br />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.<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. Readers are advised to adopt a hands on approach, which would lead to better mental representations.<br /><p></p><br /><h2>Step by Step Guide and Visual Illustrations and Examples</h2><br />The 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.<br /><p></p><br /><h2> Target Users</h2><br /><ul><li>Beginners who want to approach data science, but are too afraid of complex math to start</li><li>Newbies in computer science techniques and data science</li><li>Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way</li><li>Students and academicians, especially those focusing on data science</li></ul><br /><p></p><br /><h2>What’s Inside This Book?</h2><br /><p><strong>Part 1: Data Science Fundamentals, Concepts and Algorithms</strong></p><br /><ul><li>Introduction</li><li>Statistics</li><li>Probability</li><li>Bayes’ Theorem and Naïve Bayes Algorithm</li><li>Asking the Right Question</li><li>Data Acquisition</li><li>Data Preparation</li><li>Data Exploration</li><li>Data Modelling</li><li>Data Presentation</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>The Bias-Variance Trade-off</li><li>Feature Extraction and Selection</li></ul><br /><p><strong>Part 2: Data Science in Practice</strong></p><br /><ul><li>Overview of Python Programming Language</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>GLM models</li><li>Decision Trees and Random forest</li><li>Perceptrons</li><li>Backpropagation</li><li>Clustering</li><li>Natural Language Processing</li></ul><hr ><br /><h2>Frequently Asked Questions</h2><br /><p></p><br />Q: Does this book include everything I need to become a data science expert?<br />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.<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.<br /><h4>***** MONEY BACK GUARANTEE BY AMAZON ***** </h4><br /><hr ><br /><h2>Editorial Reviews</h2><br /><p><em>"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.."</em><br />- <strong>Lei Xia,</strong> <strong>Data Scientist Expert at Facebook
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>
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>
Cyber Security: Power and Technology (Intelligent Systems, Contro...
by Springer

Language

English

Pages

288

Publication Date

May 04, 2018

Product Description
Customer Reviews
<p>This book gathers the latest research results of scientists from different countries who have made essential contributions to the novel analysis of cyber security. Addressing open problems in the cyber world, the book consists of two parts. Part I focuses on cyber operations as a new tool in global security policy, while Part II focuses on new cyber security technologies when building cyber power capabilities. The topics discussed include strategic perspectives on cyber security and cyber warfare, cyber security implementation, strategic communication, trusted computing, password cracking, systems security and network security among others.</p><div><br /></div>
Intelligent City Evaluation System (Strategic Research on Constru...
by Zhiqiang Wu

Language

English

Pages

263

Publication Date

July 28, 2018

Product Description
Customer Reviews
<p>This book assesses various intelligent-city evaluation systems around the globe, and subsequently combines that assessment with local-government and enterprise practices to create an evaluation index system for quantifying the Intelligent City concept.</p> <p>In addition, the book provides the results of the CityIQ indicator ranking of intelligent cities in China and worldwide, a system that focuses on three of the most crucial aspects of urban development: the development environment, future trends, and construction and operation. After data sorting, calculation and dimensionless treatment, a score system ranging from 0 to 100 is created for ranking and analyzing cities.</p> <p>Providing unique strategies for promoting an intelligent city evaluation system, the book offers a valuable reference guide for intelligent-city decision-makers, as well as leaders in public urban economy, social welfare and environmental authorities.</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>
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

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