How Open Source Ate Software
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How Open Source Ate Software
Learn how free software became open source and how you can sell open source software. This book provides a historical context of how open source has thoroughly transformed how we write software, how we cooperate, how we communicate, how we organize, and, ultimately, how we think about business values. You’ll look at project and community examples including Linux, BSD, Apache, and Kubernetes, understand the open source development model, and how open source has influenced approaches more broadly, even proprietary software, such as open betas. You'll also examine the flipside, the "Second Machine Age," and the challenges of open source-based business models. Today, open source serves as shorthand for much broader trends and behaviors. It’s not just about a free (in all senses of the word) alternative to commercial software. It increasingly is the new commercial software. How Open Source Ate Software reveals how open source has much in common, and is often closely allied, with many other trends in business and society. You'll see how it enables projects that go beyond any individual company. That makes open source not just a story about software, but a story about almost everything. What You'll Learn Understand open source opportunities and challenges Sell software if you’re giving it away Apply open source principles more broadly to openorg, devops, etc. Review which organizational incentives you can implement Who This Book Is For Anyone who has an interest in what is happening in open source and the open source community, and anyone who is contemplating making a business that involves open source.
How Open Source Ate Software
Providing project and community examples, this book provides a historical context of how open source has thoroughly transformed how we write software, how we cooperate, how we communicate, how we organize, and, ultimately, how we think about business values. --
Data Mining and Exploration
This book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.