It strikes a balance between theory and application by incorporating real examples, and helping students visualize the three-dimensional atomic and molecular structures that are the basis of chemical activity.

    Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    He or she must have a working knowledge of how the various technical aspects of the theater work (scenery, props, costumes, lights and sound), be part director, part playwright, part designer and part producer, and be prepared to act as confidant, counselor and confessor to everyone else in the company.This book addresses all of these considerations in detail and offers the reader-professional or amateur, veteran or beginner-helpful guidance and practical advice, supported by many forms and examples to illustrate the points covered in the text.The three phrases of mounting and performing a show are covered. Part I takes the reader through the pre-production phase-research, the script, planning and organization, and auditions. Part II covers the rehearsal process-rehearsal rules, blocking, cues, prompting, information distribution, technical and dress rehearsals. Part III discusses the performance phase-calling the show, maintaining the director's work, working with understudies and replacements, and more.Part IV provides insights into the organizational structure or some theaters and aspects of human behavior in those organizations. Many stage managers of long-running commercial productions believe that-once the show is up and running-only ten percent of their work is related to everything covered in Parts I, II and III. The other ninety percent is associated with issues in Part IV; i.e. managing human behavior and maintaining working relationships.

    The key to every biological problem must finally be sought in the cell, for every living organism is, or at sometime has been, a cell."

    And this time, she has to pass.Luckily, her ever-patient tutor Hikaru is there to help. Join them in the pages of The Manga Guide to Electricity as Rereko examines everyday electrical devices like flashlights, heaters, and circuit breakers, and learns the meaning of abstract concepts like voltage, potential, current, resistance, conductivity, and electrostatic force.The real-world examples that you'll find in The Manga Guide to Electricity will teach you:What electricity is, how it works, how it's created, and how it can be usedThe relationship between voltage, current, and resistance (Ohm's law)Key electrical concepts like inductance and capacitanceHow complicated components like transformers, semiconductors, diodes, and transistors workHow electricity produces heat and the relationship between current and magnetic fieldsIf thinking about how electricity works really fries your brain, let The Manga Guide to Electricity teach you all things electrical in a shockingly fun way.

    Some chapters have been rewritten, making topics such as conjugation and nucleophilic substitution more accessible. Problems are provided which challenge the readers' understanding. read.

    The text teaches the basics of solving problems using its trademark clear discussions and new art-enhanced format. The text moves students beyond rote quantitative problem solving, encouraging them to visualize and think about a problem before beginning to solve it; it conveys the key concepts being discussed to help students truly understand the chemistry by using unique, proven features. The clean, interesting writing style talks directly to students with a minimum of distractions. Molecular illustrations are abundant and are placed where they are referenced for maximum effectiveness with students. The well-integrated McMurry/Fay media program includes the IRCD, PH GradeAssist, a CW/CD suite and more.

    But the state-of-the-art has moved forward dramatically since Meyers last updated this book in 1997. (For instance, there s now STL. Design patterns. Even new functionality being added through TR1 and Boost.) So Meyers has done a top-to-bottom rewrite, identifying the 55 most valuable techniques you need now to be exceptionally effective with C++. Over half of this edition s content is new. Templates broadly impact C++ development, and you ll find them everywhere. There s extensive coverage of multithreaded systems. There s an entirely new chapter on resource management. You ll find substantial new coverage of exceptions. Much is gained, but nothing s lost: You ll find the same depth of practical insight that first made Effective C++ a classic all those years ago. Bill Camarda, from the July 2005 href="http://www.barnesandnoble.com/newslet... Only

    

    This book offers exciting -- and useful -- information about the mind and the brain that provides some answers on how people actually learn.

    Building on two widely acclaimed previous editions, Craig Larman has updated this book to fully reflect the new UML 2 standard, to help you master the art of object design, and to promote high-impact, iterative, and skillful agile modeling practices.Developers and students will learn object-oriented analysis and design (OOA/D) through three iterations of two cohesive, start-to-finish case studies. These case studies incrementally introduce key skills, essential OO principles and patterns, UML notation, and best practices. You won’t just learn UML diagrams - you’ll learn how to apply UML in the context of OO software development.Drawing on his unsurpassed experience as a mentor and consultant, Larman helps you understand evolutionary requirements and use cases, domain object modeling, responsibility-driven design, essential OO design, layered architectures, “Gang of Four” design patterns, GRASP, iterative methods, an agile approach to the Unified Process (UP), and much more. This edition’s extensive improvements include:- A stronger focus on helping you master OOA/D through case studies that demonstrate key OO principles and patterns, while also applying the UML- New coverage of UML 2, Agile Modeling, Test-Driven Development, and refactoring- Many new tips on combining iterative and evolutionary development with OOA/D- Updates for easier study, including new learning aids and graphics- New college educator teaching resources- Guidance on applying the UP in a light, agile spirit, complementary with other iterative methods such as XP and Scrum- Techniques for applying the UML to documenting architectures- A new chapter on evolutionary requirements, and much moreApplying UML and Patterns, Third Edition, is a lucid and practical introduction to thinking and designing with objects - and creating systems that are well crafted, robust, and maintainable.

     This new edition has an earlier evolution of object concepts, developed in a way that capitalizes on the power of objects without overwhelming beginning programmers. It includes all new Java 5 topics, including Scanner class, enumerated types, autoboxing, variable length parameter lists, the enhanced for loop, and generic types. This is in depth coverage on GUI applications. This book is appropriate for beginning programmers who want to learn to program with Java as well as experienced programmers who want to add Java to their skill-set.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data