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Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

By Seth Stephens-Davidowitz

Dey Street Books
Released: 2017-05-09
Hardcover (354 pages)

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
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  • Dey Street Books
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Foreword by Steven Pinker, author of The Better Angels of our Nature

Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, 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.

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.

Everybody Lies 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?

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. Everybody Lies challenges us to think differently about how we see it and the world.

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

By Aurélien Géron

O Reilly Media
Paperback (568 pages)

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
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Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

  • Explore the machine learning landscape, particularly neural nets
  • Use scikit-learn to track an example machine-learning project end-to-end
  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
  • Use the TensorFlow library to build and train neural nets
  • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn techniques for training and scaling deep neural nets
  • Apply practical code examples without acquiring excessive machine learning theory or algorithm details

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

By Hadley Wickham

O Reilly Media
Paperback (522 pages)

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
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Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.

You’ll learn how to:

  • Wrangle—transform your datasets into a form convenient for analysis
  • Program—learn powerful R tools for solving data problems with greater clarity and ease
  • Explore—examine your data, generate hypotheses, and quickly test them
  • Model—provide a low-dimensional summary that captures true "signals" in your dataset
  • Communicate—learn R Markdown for integrating prose, code, and results

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

By Martin Kleppmann

O'Reilly Media
Paperback (614 pages)

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
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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?

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.

  • Peer under the hood of the systems you already use, and learn how to use and operate them more effectively
  • Make informed decisions by identifying the strengths and weaknesses of different tools
  • Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity
  • Understand the distributed systems research upon which modern databases are built
  • Peek behind the scenes of major online services, and learn from their architectures

Don't Make Me Think, Revisited: A Common Sense Approach to Web Usability (3rd Edition) (Voices That Matter)

By Steve Krug

Krug Steve
Paperback (216 pages)

Don t Make Me Think, Revisited: A Common Sense Approach to Web Usability (3rd Edition) (Voices That Matter)
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  • Don t Make Me Think Revisited A Common Sense Approach to Web Usability
Product Description:
Since Don’t Make Me Think was first published in 2000, hundreds of thousands of Web designers and developers have relied on usability guru Steve Krug’s guide to help them understand the principles of intuitive navigation and information design. Witty, commonsensical, and eminently practical, it’s one of the best-loved and most recommended books on the subject.

Now Steve returns with fresh perspective to reexamine the principles that made Don’t Make Me Think a classic–with updated examples and a new chapter on mobile usability. And it’s still short, profusely illustrated…and best of all–fun to read.

If you’ve read it before, you’ll rediscover what made Don’t Make Me Think so essential to Web designers and developers around the world. If you’ve never read it, you’ll see why so many people have said it should be required reading for anyone working on Web sites.


“After reading it over a couple of hours and putting its ideas to work for the past five years, I can say it has done more to improve my abilities as a Web designer than any other book.”
–Jeffrey Zeldman, author of Designing with Web Standards

 

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

By Trevor Hastie & Jerome Friedman

imusti
Hardcover (745 pages)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
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This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.

SQL in 10 Minutes, Sams Teach Yourself (4th Edition)

By Ben Forta

Sams Publishing
Paperback (288 pages)

SQL in 10 Minutes, Sams Teach Yourself (4th Edition)
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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


Practical Statistics for Data Scientists: 50 Essential Concepts

By Peter Bruce

O'Reilly Media
Paperback (318 pages)

Practical Statistics for Data Scientists: 50 Essential Concepts
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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.

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.

With this book, you’ll learn:

  • Why exploratory data analysis is a key preliminary step in data science
  • How random sampling can reduce bias and yield a higher quality dataset, even with big data
  • How the principles of experimental design yield definitive answers to questions
  • How to use regression to estimate outcomes and detect anomalies
  • Key classification techniques for predicting which categories a record belongs to
  • Statistical machine learning methods that “learn” from data
  • Unsupervised learning methods for extracting meaning from unlabeled data

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

By Foster Provost

imusti
Paperback (414 pages)

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
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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.

Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business 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.

  • Understand how data science fits in your organization—and how you can use it for competitive advantage
  • Treat data as a business asset that requires careful investment if you’re to gain real value
  • Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
  • Learn general concepts for actually extracting knowledge from data
  • Apply data science principles when interviewing data science job candidates

Microsoft Excel Data Analysis and Business Modeling (5th Edition)

By Wayne Winston

Microsoft Press
Paperback (864 pages)

Microsoft Excel Data Analysis and Business Modeling (5th Edition)
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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.


Solve real business problems with Excel—and build your competitive advantage

  • Quickly transition from Excel basics to sophisticated analytics
  • Summarize data by using PivotTables and Descriptive Statistics
  • Use Excel trend curves, multiple regression, and exponential smoothing
  • Master advanced functions such as OFFSET and INDIRECT
  • Delve into key financial, statistical, and time functions
  • Leverage the new charts in Excel 2016 (including box and whisker and waterfall charts)
  • Make charts more effective by using Power View
  • Tame complex optimizations by using Excel Solver
  • Run Monte Carlo simulations on stock prices and bidding models
  • Work with the AGGREGATE function and table slicers
  • Create PivotTables from data in different worksheets or workbooks
  • Learn about basic probability and Bayes’ Theorem
  • Automate repetitive tasks by using macros

 




 
 



   

 
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