When inexperienced entrepreneurs ask my advice about their idea for a tech startup, they often worry "What if Google decides to compete with us? They will crush us!" I respond that far more startups die of suicide than homicide. If you can avoid hurting yourself, then you are already better off than most of your competitors. Startups are a chance to build something entirely original with brilliant and ambitious people. But startups are also dangerous. Limited money means there is little room for mistakes. One bad decision can mean bankruptcy. The potential payoff attracts capital, which in turn attracts scam artists. The unscrupulous often lack the skills needed to succeed, but sometimes they are smart enough to trick investors. Even entrepreneurs who start with a strong moral compass can find that the threat of failure unmoors their ethics from their ambition. Emotions matter. We might hope that those in leadership positions possess strength and resilience, but vanity and fragile egos have sabotaged many of the businesses that I’ve worked with. Defeat is always a possibility, and not everyone finds healthy ways to deal with the stress. In this book I offer both advice and also warnings. I've seen certain self-destructive patterns play out again and again, so I wanted to document one of the most extreme cases that I've witnessed. In 2015 I worked for a startup that began with an ingenious idea: to use the software techniques known as Natural Language Processing to allow people to interact with databases by writing ordinary English sentences. This was a multi-billion dollar idea that could have transformed the way people gathered and used information. However, the venture had inexperienced leadership. They burned through their $1.3 million seed money. As their resources dwindled, their confidence transformed into doubt, which was aggravated by edicts from the Board Of Directors ordering sudden changes that effectively threw away weeks' worth of work. Every startup forces its participants into extreme positions, often regarding budget and deadlines. Often these situations are absurd to the point of parody. Therefore, there is considerable humor in this story. The collision of inexperience and desperation gives rise to moments that are simply silly. I tell this story in a day-to-day format, both to capture the early optimism, and then the later sense of panic. Here then, is a cautionary tale, a warning about tendencies that everyone joining a startup should be on guard against."

     While very complex, the Internet operates on a few relatively simple concepts that anyone can understand. Networks and networked applications are embedded in our lives. Understanding how these technologies work is invaluable.  This book was written for everyone - no technical knowledge is required!While this book is not specifically about the Network+ or CCNA certifications, it as a way to give students interested in these certifications a starting point.

    It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

    The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    

    In addition to giving a strong foundation in the language itself, Lubanovic shows how to use it for a range of applications in business, science, and the arts, drawing on the rich collection of open source packages developed by Python fans.It's impressive how many commercial and production-critical programs are written now in Python. Developed to be easy to read and maintain, it has proven a boon to anyone who wants applications that are quick to write but robust and able to remain in production for the long haul.This book focuses on the current version of Python, 3.x, while including sidebars about important differences with 2.x for readers who may have to deal with programs in that version.

    And in a perfect world, those who produce the best code are the most successful. But in our perfectly messy world, success also depends on how you work with people to get your job done.In this highly entertaining book, Brian Fitzpatrick and Ben Collins-Sussman cover basic patterns and anti-patterns for working with other people, teams, and users while trying to develop software. It's valuable information from two respected software engineers whose popular video series, "Working with Poisonous People," has attracted hundreds of thousands of viewers.You'll learn how to deal with imperfect people--those irrational and unpredictable beings--in the course of your work. And you'll discover why playing well with others is at least as important as having great technical skills. By internalizing the techniques in this book, you'll get more software written, be more influential, be happier in your career.

    But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian Statistics the Fun Way will change that.This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples.By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to:- How to measure your own level of uncertainty in a conclusion or belief- Calculate Bayes theorem and understand what it's useful for- Find the posterior, likelihood, and prior to check the accuracy of your conclusions- Calculate distributions to see the range of your data- Compare hypotheses and draw reliable conclusions from themNext time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.

    This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.

    Beautiful Evidence is about how seeing turns into showing, how data and evidence turn into explanation. The book identifies excellent and effective methods for showing nearly every kind of information, suggests many new designs (including sparklines), and provides analytical tools for assessing the credibility of evidence presentations (which are seen from both sides: how to produce and how to consume presentations). For alert consumers of presentations, there are chapters on diagnosing evidence corruption and PowerPoint pitches. Beautiful Evidence concludes with two chapters that leave the world of pixel and paper flatland representations - and move onto seeing and thinking in space land, the real-land of three-space and time.

    The book is a complete revision of the commands section of Sobell's Practical Guide to Linux - a proven best-seller. The book is Linux distribution and release agnostic. It will appeal to users of ALL Linux distributions. Superior examples make this book the the best option on the market! System administrators, software developers, quality assurance engineers and others working on a Linux system need to work from the command line in order to be effective. Linux is famous for its huge number of command line utility programs, and the programs themselves are famous for their large numbers of options, switches, and configuration files. But the truth is that users will only use a limited (but still significant) number of these utilities on a recurring basis, and then only with a subset of the most important and useful options, switches and configuration files. This book cuts through all the noise and shows them which utilities are most useful, and which options most important. And it contains examples, lot's and lot's of examples. programmability. Utilities are designed, by default, to work wtih other utilities within shell programs as a way of automating system tasks. This book contains a superb introduction to Linux shell programming. And since shell programmers need to write their programs in text editors, this book covers the two most popular ones: vi and emacs.

    From economic development, to healthcare, to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems. And women pay tremendous costs for this bias, in time, money, and often with their lives.Celebrated feminist advocate Caroline Criado Perez investigates the shocking root cause of gender inequality and research in Invisible Women​, diving into women’s lives at home, the workplace, the public square, the doctor’s office, and more. Built on hundreds of studies in the US, the UK, and around the world, and written with energy, wit, and sparkling intelligence, this is a groundbreaking, unforgettable exposé that will change the way you look at the world.

    But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed.This book examines:Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable log structured storage engines, with differences and use-cases for eachDistributed systems: Learn step-by-step how nodes and processes connect and build complex communication patterns, from UDP to reliable consensus protocolsDatabase clusters: Discover how to achieve consistent models for replicated data

    It also covers MySQL, and PostgreSQL. It contains examples which have been tested against each SQL platform, with incompatibilities or platform distinctives called out and explained.

    Designed for readers who don't need the academic formality, it's a fast and easy computer science guide. It teaches essential concepts for people who want to program computers effectively. First, it introduces discrete mathematics, then it exposes the most common algorithms and data structures. It also shows the principles that make computers and programming languages work.