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A New York Times Bestseller

An audacious, irreverent investigation of human behavior—and a first look at a revolution in the making
 
Our personal data has been used to spy on us, hire and fire us, and sell us stuff we don’t need. In Dataclysm, Christian Rudder uses it to show us who we truly are.
 
For centuries, we’ve relied on polling or small-scale lab experiments to study human behavior. Today, a new approach is possible. As we live more of our lives online, researchers can finally observe us directly, in vast numbers, and without filters. Data scientists have become the new demographers.
 
In this daring and original book, Rudder explains how Facebook "likes" can predict, with surprising accuracy, a person’s sexual orientation and even intelligence; how attractive women receive exponentially more interview requests; and why you must have haters to be hot. He charts the rise and fall of America’s most reviled word through Google Search and examines the new dynamics of collaborative rage on Twitter. He shows how people express themselves, both privately and publicly. What is the least Asian thing you can say? Do people bathe more in Vermont or New Jersey? What do black women think about Simon & Garfunkel? (Hint: they don’t think about Simon & Garfunkel.) Rudder also traces human migration over time, showing how groups of people move from certain small towns to the same big cities across the globe. And he grapples with the challenge of maintaining privacy in a world where these explorations are possible.
 
Visually arresting and full of wit and insight, Dataclysm is a new way of seeing ourselves—a brilliant alchemy, in which math is made human and numbers become the narrative of our time.
You want increased customer satisfaction, faster development cycles, and less wasted work. Domain-driven design (DDD) combined with functional programming is the innovative combo that will get you there. In this pragmatic, down-to-earth guide, you'll see how applying the core principles of functional programming can result in software designs that model real-world requirements both elegantly and concisely - often more so than an object-oriented approach. Practical examples in the open-source F# functional language, and examples from familiar business domains, show you how to apply these techniques to build software that is business-focused, flexible, and high quality.

Domain-driven design is a well-established approach to designing software that ensures that domain experts and developers work together effectively to create high-quality software. This book is the first to combine DDD with techniques from statically typed functional programming. This book is perfect for newcomers to DDD or functional programming - all the techniques you need will be introduced and explained.

Model a complex domain accurately using the F# type system, creating compilable code that is also readable documentation---ensuring that the code and design never get out of sync. Encode business rules in the design so that you have "compile-time unit tests," and eliminate many potential bugs by making illegal states unrepresentable. Assemble a series of small, testable functions into a complete use case, and compose these individual scenarios into a large-scale design. Discover why the combination of functional programming and DDD leads naturally to service-oriented and hexagonal architectures. Finally, create a functional domain model that works with traditional databases, NoSQL, and event stores, and safely expose your domain via a website or API.

Solve real problems by focusing on real-world requirements for your software.

What You Need:

The code in this book is designed to be run interactively on Windows, Mac and Linux.You will need a recent version of F# (4.0 or greater), and the appropriate .NET runtime for your platform.Full installation instructions for all platforms at fsharp.org.

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk – and answer – tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is For

If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.

What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn Detail

Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.

Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.

Style and approach

Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Influence action through data!

This is not a book. It is a one-of-a-kind immersive learning experience through which you can become—or teach others to be—a powerful data storyteller.

Let’s practice! helps you build confidence and credibility to create graphs and visualizations that make sense and weave them into action-inspiring stories. Expanding upon best seller storytelling with data’s foundational lessons, Let’s practice! delivers fresh content, a plethora of new examples, and over 100 hands-on exercises. Author and data storytelling maven Cole Nussbaumer Knaflic guides you along the path to hone core skills and become a well-practiced data communicator. Each chapter includes:

● Practice with Cole: exercises based on real-world examples first posed for you to consider and solve, followed by detailed step-by-step illustration and explanation

● Practice on your own: thought-provoking questions and even more exercises to be assigned or worked through individually, without prescribed solutions

● Practice at work: practical guidance and hands-on exercises for applying storytelling with data lessons on the job, including instruction on when and how to solicit useful feedback and refine for greater impact

The lessons and exercises found within this comprehensive guide will empower you to master—or develop in others—data storytelling skills and transition your work from acceptable to exceptional. By investing in these skills for ourselves and our teams, we can all tell inspiring and influential data stories!

Create powerful data visualizations and unlock intelligent business insights with TableauKey FeaturesExplore all the latest Tableau 2019 features and redefine business analytics for your organizationCreate impressive data visualizations and interactive dashboards to get insights from your dataLearn effective data storytelling to transform how your business leverages data and makes decisionsBook Description

Tableau is the gold standard of business intelligence and visual analytics tools in every industry. It enables rapid data visualization and interpretation with charts, graphs, dashboards, and much more. Updated with the latest features of Tableau, this book takes you from the foundations of the Tableau 2019 paradigm through to advanced topics.

This third edition of the bestselling guide by Tableau Zen Master, Joshua Milligan, will help you come to grips with updated features, such as set actions and transparent views. Beginning with installation, you'll create your first visualizations with Tableau and then explore practical examples and advanced techniques. You'll create bar charts, tree maps, scatterplots, time series, and a variety of other visualizations. Next, you'll discover techniques to overcome challenges presented by data structure and quality and engage in effective data storytelling and decision making with business critical information. Finally, you'll be introduced to Tableau Prep, and learn how to use it to integrate and shape data for analysis.

By the end of this book, you will be equipped to leverage the powerful features of Tableau 2019 for decision making.

What you will learnDevelop stunning visualizations that explain complexity with clarityExplore the exciting new features of Tableau Desktop and Tableau PrepConnect to various data sources to bring all your data togetherUncover techniques to prep and structure your data for easy analysisCreate and use calculations to solve problems and enrich analyticsMaster advanced topics such as sets, LOD calcs, and much moreEnable smart decisions with clustering, distribution, and forecastingShare your data stories to build a culture of trust and actionWho this book is for

This Tableau book is for anyone who wants to understand data. If you’re new to Tableau, don’t worry. This book builds on the foundations to help you understand how Tableau really works and then builds on that knowledge with practical examples before moving on to advanced techniques. Working experience with databases will be useful but is not necessary to get the most out of this book.

Make your data work for you!

Tableau For Dummies brings order to the chaotic world of data. Understanding your data and organizing it into formats and visualizations that make sense to you are crucial to making a real impact on your business with the information that's already at your fingertips. This easy-to-use reference explores the user interface, and guides you through the process of connecting your data sources to the software. Additionally, this approachable, yet comprehensive text shows you how to use graphs, charts, and other images to bring visual interest to your data, how to create dashboards from multiple data sources, and how to export the visualizations that you have developed into multiple formats that translate into positive change for your business.

The mission of Tableau Software is to grant you access to data that, when put into action, will help you build your company. Learning to use the data available to you helps you make informed, grounded business decisions that can spell success for your company.

Navigate the user interface to efficiently access the features you need Connect to various spreadsheets, databases, and other data sources to create a multi-dimensional snapshot of your business Develop visualizations with easy to use drag and drop features Start building your data with templates and sample workbooks to spark your creativity and help you organize your information

Tableau For Dummies is a step-by-step resource that helps you make sense of the data landscape—and put your data to work in support of your business.

A case for building a digital environment that can make us happier and healthier, not just more productive, and a theoretical framework for doing so.

On the eve of Google's IPO in 2004, Larry Page and Sergey Brin vowed not to be evil. Today, a growing number of technologists would go further, trying to ensure that their work actively improves people's lives. Technology, so pervasive and ubiquitous, has the capacity to increase stress and suffering; but it also has the less-heralded potential to improve the well-being of individuals, society, and the planet. In this book, Rafael Calvo and Dorian Peters investigate what they term “positive computing”—the design and development of technology to support psychological well-being and human potential.

Calvo and Peters explain that technologists' growing interest in social good is part of a larger public concern about how our digital experience affects our emotions and our quality of life—which itself reflects an emerging focus on humanistic values in many different disciplines. Synthesizing theory, knowledge, and empirical methodologies from a variety of fields, they offer a rigorous and coherent foundational framework for positive computing. Sidebars by experts from psychology, neuroscience, human–computer interaction, and other disciplines supply essential context. Calvo and Peters examine specific well-being factors, including positive emotions, self-awareness, mindfulness, empathy, and compassion, and explore how technology can support these factors. Finally, they offer suggestions for future research and funding.

Sidebars
Timothy N. Bickmore, Jeremy Bailenson, danah boyd, Jane Burns, David R. Caruso, Mihaly Csikszentmihalyi, Felicia Huppert, Mary-Helen Immordino-Yang, Adele Krusche and J. Mark G. Williams, Jane McGonigal, Jonathan Nicholas, Don Norman, Yvonne Rogers

R is a programming language developed is widely used for statistical and graphical analysis. It can execute advance machine learning algorithms including earning algorithm, linear regression, time series, statistical inference.


R programming language is used by Fortune 500 companies and tech bellwethers like Uber, Google, Airbnb, Facebook, Apple.


R provides a data scientist tools and libraries (Dplyr) to perform the 3 steps of analysis 1) Extract 2) Transform, Cleanse 3) Analyze.


Table of Contents


Chapter 1: What is R Programming Language? Introduction & Basics


Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows


Chapter 3: R Data Types, Arithmetic & Logical Operators with Example


Chapter 4: R Matrix Tutorial: Create, Print, add Column, Slice


Chapter 5: Factor in R: Categorical & Continuous Variables


Chapter 6: R Data Frame: Create, Append, Select, Subset


Chapter 7: List in R: Create, Select Elements with Example


Chapter 8: R Sort a Data Frame using Order()


Chapter 9: R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread)


Chapter 10: Merge Data Frames in R: Full and Partial Match


Chapter 11: Functions in R Programming (with Example)


Chapter 12: IF, ELSE, ELSE IF Statement in R


Chapter 13: For Loop in R with Examples for List and Matrix


Chapter 14: While Loop in R with Example


Chapter 15: apply(), lapply(), sapply(), tapply() Function in R with Examples


Chapter 16: Import Data into R: Read CSV, Excel, SPSS, Stata, SAS Files


Chapter 17: How to Replace Missing Values(NA) in R: na.omit & na.rm


Chapter 18: R Exporting Data to Excel, CSV, SAS, STATA, Text File


Chapter 19: Correlation in R: Pearson & Spearman with Matrix Example


Chapter 20: R Aggregate Function: Summarise & Group_by() Example


Chapter 21: R Select(), Filter(), Arrange(), Pipeline with Example


Chapter 22: Scatter Plot in R using ggplot2 (with Example)


Chapter 23: How to make Boxplot in R (with EXAMPLE)


Chapter 24: Bar Chart & Histogram in R (with Example)


Chapter 25: T Test in R: One Sample and Paired (with Example)


Chapter 26: R ANOVA Tutorial: One way & Two way (with Examples)


Chapter 27: R Simple, Multiple Linear and Stepwise Regression [with Example]


Chapter 28: Decision Tree in R with Example


Chapter 29: R Random Forest Tutorial with Example


Chapter 30: Generalized Linear Model (GLM) in R with Example


Chapter 31: K-means Clustering in R with Example


Chapter 32: R Vs Python: What's the Difference?


Chapter 33: SAS vs R: What's the Difference?

Harness the power of Redis to integrate and manage your projects efficientlyAbout This BookLearn how to use Redis's data types efficiently to manage large data setsScale Redis to multiple servers with Twemproxy, Redis Sentinel, and Redis ClusterA fast-paced guide, full of real-world examples to help you get the best out of the features offered by RedisWho This Book Is For

If you are a competent developer with experience of working with data structure servers and want to boost your project's performance by learning about features of Redis, then this book is for you.

What You Will LearnBuild analytics applications using Bitmaps and HyperloglogsEnhance scalability with Twemproxy, Redis Sentinel, and Redis ClusterBuild a Time Series implementation in Node.js and RedisCreate your own Redis commands by extending Redis with LuaGet to know security techniques to protect your data (SSL encryption, firewall rules, basic authorization)Persist data to disk and learn the trade-offs of AOF and RDBUnderstand how to use Node.js, PHP, Python, and Ruby clients for RedisAvoid common pitfalls when designing your next solutionIn Detail

Redis is the most popular in-memory key-value data store. It's very lightweight and its data types give it an edge over the other competitors. If you need an in-memory database or a high-performance cache system that is simple to use and highly scalable, Redis is what you need.

Redis Essentials is a fast-paced guide that teaches the fundamentals on data types, explains how to manage data through commands, and shares experiences from big players in the industry.

We start off by explaining the basics of Redis followed by the various data types such as Strings, hashes, lists, and more. Next, Common pitfalls for various scenarios are described, followed by solutions to ensure you do not fall into common traps.

After this, major differences between client implementations in PHP, Python, and Ruby are presented. Next, you will learn how to extend Redis with Lua, get to know security techniques such as basic authorization, firewall rules, and SSL encryption, and discover how to use Twemproxy, Redis Sentinel, and Redis Cluster to scale infrastructures horizontally. At the end of this book, you will be able to utilize all the essential features of Redis to optimize your project's performance.

Style and approach

A practical guide that offers the foundation upon which you can begin to understand the capabilities of Redis using a step-by-step approach. This book is full of real-world problems and in-depth knowledge of the concepts and features of Redis, with plenty of examples.

Implement your Business Intelligence solutions without any coding - by leveraging the power of the Alteryx platformAbout This BookExperience the power of codeless analytics using Alteryx, a leading Business Intelligence toolUncover hidden trends and valuable insights from your data across different sources and make accurate predictionsIncludes real-world examples to put your understanding of the features in Alteryx to practical useWho This Book Is For

This book is for aspiring data professionals who want to learn and implement self-service analytics from scratch, without any coding. Those who have some experience with Alteryx and want to gain more proficiency will also find this book to be useful. A basic understanding of the data science concepts is all you need to get started with this book.

What You Will LearnCreate efficient workflows with Alteryx to answer complex business questionsLearn how to speed up the cleansing, data preparing, and shaping processBlend and join data into a single dataset for self-service analysisWrite advanced expressions in Alteryx leading to an optimal workflow for efficient processing of huge dataDevelop high-quality, data-driven reports to improve consistency in reporting and analysisExplore the flexibility of macros by automating analytic processesApply predictive analytics from spatial, demographic, and behavioral analysis and quickly publish, scheduleShare your workflows and insights with relevant stakeholdersIn Detail

Alteryx, as a leading data blending and advanced data analytics platform, has taken self-service data analytics to the next level. Companies worldwide often find themselves struggling to prepare and blend massive datasets that are time-consuming for analysts. Alteryx solves these problems with a repeatable workflow designed to quickly clean, prepare, blend, and join your data in a seamless manner.

This book will set you on a self-service data analytics journey that will help you create efficient workflows using Alteryx, without any coding involved. It will empower you and your organization to take well-informed decisions with the help of deeper business insights from the data.Starting with the fundamentals of using Alteryx such as data preparation and blending, you will delve into the more advanced concepts such as performing predictive analytics. You will also learn how to use Alteryx's features to share the insights gained with the relevant decision makers. To ensure consistency, we will be using data from the Healthcare domain throughout this book.

The knowledge you gain from this book will guide you to solve real-life problems related to Business Intelligence confidently. Whether you are a novice with Alteryx or an experienced data analyst keen to explore Alteryx's self-service analytics features, this book will be the perfect companion for you.

Style and approach

Comprehensive, step by step guide filled with real-world examples to step through the complex business questions using one of the leading data analytics platform.

With an interesting mix of theory and practicals, explore Python and its features, and progress from beginner to being skilled in this popular scripting languageKey FeaturesA comprehensive introduction to the world of Python programmingPaves an easy-to-follow path for you to navigate through conceptsFilled with over 90 practical exercises and activities to reinforce your learningBook Description

After a brief history of Python and key differences between Python 2 and Python 3, you'll understand how Python has been used in applications such as YouTube and Google App Engine. As you work with the language, you'll learn about control statements, delve into controlling program flow and gradually work on more structured programs via functions.

As you settle into the Python ecosystem, you'll learn about data structures and study ways to correctly store and represent information. By working through specific examples, you'll learn how Python implements object-oriented programming (OOP) concepts of abstraction, encapsulation of data, inheritance, and polymorphism. You'll be given an overview of how imports, modules, and packages work in Python, how you can handle errors to prevent apps from crashing, as well as file manipulation.

By the end of this book, you'll have built up an impressive portfolio of projects and armed yourself with the skills you need to tackle Python projects in the real world.

What you will learnUse control statementsManipulate primitive and non-primitive data structuresUse loops to iterate over objects or data for accurate resultsWrite encapsulated and succinct Python functionsBuild Python classes using object-oriented programmingManipulate files on the file system (open, read, write, and delete)Who this book is for

Python Fundamentals is great for anyone who wants to start using Python to build anything from simple command-line programs to web applications. Prior knowledge of Python isn't required.

Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0About This BookLearn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0Develop and deploy efficient, scalable real-time Spark solutionsTake your understanding of using Spark with Python to the next level with this jump start guideWho This Book Is For

If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory.

What You Will LearnLearn about Apache Spark and the Spark 2.0 architectureBuild and interact with Spark DataFrames using Spark SQLLearn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectivelyRead, transform, and understand data and use it to train machine learning modelsBuild machine learning models with MLlib and MLLearn how to submit your applications programmatically using spark-submitDeploy locally built applications to a clusterIn Detail

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark.

You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command.

By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.

Style and approach

This book takes a very comprehensive, step-by-step approach so you understand how the Spark ecosystem can be used with Python to develop efficient, scalable solutions. Every chapter is standalone and written in a very easy-to-understand manner, with a focus on both the hows and the whys of each concept.

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