Booch illustrates essential concepts, explains the method, and shows successful applications in a variety of fields. Booch also gives pragmatic advice on a host of issues, including classification, implementation strategies, and cost-effective project management. A two-time winner of Software Development's coveted Jolt Cola Product Excellence Award!

    This book provides information on software tools, specific work flow for specific kinds of projects, and information on various topics. It includes resources for both instructors and students such as checklists, 700 categorized web references, and more.

    With the 97 short and extremely useful tips for programmers in this book, you'll expand your skills by adopting new approaches to old problems, learning appropriate best practices, and honing your craft through sound advice.With contributions from some of the most experienced and respected practitioners in the industry--including Michael Feathers, Pete Goodliffe, Diomidis Spinellis, Cay Horstmann, Verity Stob, and many more--this book contains practical knowledge and principles that you can apply to all kinds of projects.A few of the 97 things you should know:"Code in the Language of the Domain" by Dan North"Write Tests for People" by Gerard Meszaros"Convenience Is Not an -ility" by Gregor Hohpe"Know Your IDE" by Heinz Kabutz"A Message to the Future" by Linda Rising"The Boy Scout Rule" by Robert C. Martin (Uncle Bob)"Beware the Share" by Udi Dahan

    He needed a way to keep track of software development over time – whatever he was thinking about or working on. He researched subjects he found interesting, then documented his research with a public blog post, which he could easily find and refer to later. Over time, increasing numbers of blog visitors found the posts helpful, relevant and interesting. Now, approximately 100,000 readers visit the blog per day and nearly as many comment and interact on the site.Effective Programming: More Than Writing Code is your one-stop shop for all things programming. Jeff writes with humor and understanding, allowing for both seasoned programmers and newbies to appreciate the depth of his research. From such posts as“The Programmer’s Bill of Rights” and “Why Cant Programmers... Program?” to “Working With the Chaos Monkey,” this book introduces the importance of writing responsible code, the logistics involved, and how people should view it more as a lifestyle than a career.

    New JVM-based languages like Groovy, Scala, and Clojure are redefining what it means to be a Java developer. The core Standard and Enterprise APIs now co-exist with a large and growing body of open source technologies. Multicore processors, concurrency, and massive data stores require new patterns and approaches to development. And with Java 7 due to release in 2011, there's still more to absorb.The Well-Grounded Java Developer is a unique guide written for developers with a solid grasp of Java fundamentals. It provides a fresh, practical look at new Java 7 features along with the array of ancillary technologies that a working developer will use in building the next generation of business software.

    Consumer's identities are being stolen, and a person's every step is being tracked and stored. What once might have been dismissed as paranoia is now a hard truth, and privacy is a luxury few can afford or understand.In this explosive yet practical book, Kevin Mitnick illustrates what is happening without your knowledge--and he teaches you "the art of invisibility." Mitnick is the world's most famous--and formerly the Most Wanted--computer hacker. He has hacked into some of the country's most powerful and seemingly impenetrable agencies and companies, and at one point he was on a three-year run from the FBI. Now, though, Mitnick is reformed and is widely regarded as the expert on the subject of computer security. He knows exactly how vulnerabilities can be exploited and just what to do to prevent that from happening. In THE ART OF INVISIBILITY Mitnick provides both online and real life tactics and inexpensive methods to protect you and your family, in easy step-by-step instructions. He even talks about more advanced "elite" techniques, which, if used properly, can maximize your privacy. Invisibility isn't just for superheroes--privacy is a power you deserve and need in this modern age.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    In this unique collection, you'll learn about the processes that led to specific design decisions, including the goals they had in mind, the trade-offs they had to make, and how their experiences have left an impact on programming today. Masterminds of Programming includes individual interviews with:Adin D. Falkoff: APL Thomas E. Kurtz: BASIC Charles H. Moore: FORTH Robin Milner: ML Donald D. Chamberlin: SQL Alfred Aho, Peter Weinberger, and Brian Kernighan: AWK Charles Geschke and John Warnock: PostScript Bjarne Stroustrup: C++ Bertrand Meyer: Eiffel Brad Cox and Tom Love: Objective-C Larry Wall: Perl Simon Peyton Jones, Paul Hudak, Philip Wadler, and John Hughes: Haskell Guido van Rossum: Python Luiz Henrique de Figueiredo and Roberto Ierusalimschy: Lua James Gosling: Java Grady Booch, Ivar Jacobson, and James Rumbaugh: UML Anders Hejlsberg: Delphi inventor and lead developer of C# If you're interested in the people whose vision and hard work helped shape the computer industry, you'll find Masterminds of Programming fascinating.

    Topics covered include:Basic version control commands and conceptsIntroduction to Distributed Version Control Systems (DVCS)Advanced branching workflowsStrengths and weaknesses of DVCS vs. centralized toolsBest practicesHow distributed version control works under the hoodFeaturing these open source version control tools:Apache SubversionMercurialGitVeracity

    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--explain how this system fits into the lifecycle of a distributed application. You will learn how to use tools and APIs to automate scalable distributed systems, whether it is for online services, machine-learning applications, or a cluster of Raspberry Pi computers.Explore the distributed system challenges that Kubernetes addressesDive into containerized application development, using containers such as DockerCreate and run containers on Kubernetes, using Docker's Image format and container runtimeExplore specialized objects essential for running applications in productionReliably roll out new software versions without downtime or errorsGet examples of how to develop and deploy real-world applications in Kubernetes

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas 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 color graphics. It should be 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. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

     The author spent two years researching every bug avoidance technique she could find. This book contains the best of them. If you want to program faster, with fewer bugs, and write more secure code, buy this book!

    In this book, Agile Alliance cofounder Mike Cohn discusses the philosophy of agile estimating and planning and shows you exactly how to get the job done, with real-world examples and case studies.Concepts are clearly illustrated and readers are guided, step by step, toward how to answer the following questions: What will we build? How big will it be? When must it be done? How much can I really complete by then? You will first learn what makes a good plan-and then what makes it agile.Using the techniques in Agile Estimating and Planning , you can stay agile from start to finish, saving time, conserving resources, and accomplishing more. Highlights include:Why conventional prescriptive planning fails and why agile planning works How to estimate feature size using story points and ideal days--and when to use each How and when to re-estimate How to prioritize features using both financial and nonfinancial approaches How to split large features into smaller, more manageable ones How to plan iterations and predict your team's initial rate of progress How to schedule projects that have unusually high uncertainty or schedule-related risk How to estimate projects that will be worked on by multiple teams Agile Estimating and Planning supports any agile, semiagile, or iterative process, including Scrum, XP, Feature-Driven Development, Crystal, Adaptive Software Development, DSDM, Unified Process, and many more. It will be an indispensable resource for every development manager, team leader, and team member.

    It's aimed at developers who might not be quite comfortable with the idea of pointers and static typing.http://openmymind.net/The-Little-Go-B...

    And how to move all of this data becomes nearly as important as the data itself. If you� re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds.Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you� ll learn Kafka� s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.Understand publish-subscribe messaging and how it fits in the big data ecosystem.Explore Kafka producers and consumers for writing and reading messagesUnderstand Kafka patterns and use-case requirements to ensure reliable data deliveryGet best practices for building data pipelines and applications with KafkaManage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasksLearn the most critical metrics among Kafka� s operational measurementsExplore how Kafka� s stream delivery capabilities make it a perfect source for stream processing systems