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Designing with Data
On the surface, design practices and data science may not seem like obvious partners. But these disciplines actually work toward the same goal, helping designers and product managers understand users so they can craft elegant digital experiences. While data can enhance design, design can bring deeper meaning to data. This practical guide shows you how to conduct data-driven A/B testing for making design decisions on everything from small tweaks to large-scale UX concepts. Complete with real-world examples, this book shows you how to make data-driven design part of your product design workflow. Understand the relationship between data, business, and design Get a firm grounding in data, data types, and components of A/B testing Use an experimentation framework to define opportunities, formulate hypotheses, and test different options Create hypotheses that connect to key metrics and business goals Design proposed solutions for hypotheses that are most promising Interpret the results of an A/B test and determine your next move
Killing Lincoln
A riveting historical narrative of the heart-stopping events surrounding the assassination of Abraham Lincoln, and the first work of history from mega-bestselling author Bill O'Reilly The iconic anchor of The O'Reilly Factor recounts one of the most dramatic stories in American history—how one gunshot changed the country forever. In the spring of 1865, the bloody saga of America's Civil War finally comes to an end after a series of increasingly harrowing battles. President Abraham Lincoln's generous terms for Robert E. Lee's surrender are devised to fulfill Lincoln's dream of healing a divided nation, with the former Confederates allowed to reintegrate into American society. But one man an...
HTTP: The Definitive Guide
This guide gives a complete and detailed description of the HTTP protocol and how it shapes the landscape of the Web by the technologies that it supports.
Debugging Teams
In the course of their 20+-year engineering careers, authors Brian Fitzpatrick and Ben Collins-Sussman have picked up a treasure trove of wisdom and anecdotes about how successful teams work together. Their conclusion? Even among people who have spent decades learning the technical side of their jobs, most haven’t really focused on the human component. Learning to collaborate is just as important to success. If you invest in the "soft skills" of your job, you can have a much greater impact for the same amount of effort. The authors share their insights on how to lead a team effectively, navigate an organization, and build a healthy relationship with the users of your software. This is valuable information from two respected software engineers whose popular series of talks—including "Working with Poisonous People"—has attracted hundreds of thousands of followers.
Think Stats
If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts. Develop your understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Learn topics not usually covered in an introductory course, such as Bayesian estimation Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data
Site Reliability Engineering
In this collection of essays and articles, key members of Google's Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world.
Think Complexity
Dive into Python's advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands-on guide.
Kubernetes: Up and Running
Legend has it that Google deploys over two billion application containers a week. How’s that possible? Google revealed the secret through a project called Kubernetes, an open source cluster orchestrator (based on its internal Borg system) that radically simplifies the task of building, deploying, and maintaining scalable distributed systems in the cloud. This practical guide shows you how Kubernetes and container technology can help you achieve new levels of velocity, agility, reliability, and efficiency. Authors Kelsey Hightower, Brendan Burns, and Joe Beda—who’ve worked on Kubernetes at Google and other organizatons—explain how this system fits into the lifecycle of a distributed a...
Sendmail
Reliable, flexible, and configurable enough to solve the mail routing needs of any web site, sendmail has withstood the test of time, but has become no less daunting in its complexity. Even the most experienced system administrators have found it challenging to configure and difficult to understand. For help in unraveling its intricacies, sendmail administrators have turned unanimously to one reliable source--the bat book, or sendmail by Bryan Costales and the creator of sendmail, Eric Allman. Now in its third edition, this best-selling reference will help you master the most demanding version of sendmail yet.The new edition of sendmail has been completely revised to cover sendmail 8.12--a v...
