With this book’s easy step-by-step process, you’ll quickly learn how to build and maintain a complete Access database, using Access 2013’s new, simpler user interface and templates. You also get practices and tips from the pros for good database design—ideal whether you’re using Access for school, business, or at home.The important stuff you need to know:Build a database with ease. Store information to track numbers, products, documents, and more.Customize the interface. Build your own forms to make data entry a snap.Find what you need fast. Search, sort, and summarize huge amounts of information.Put your data to use. Turn raw info into printed reports with attractive formatting.Share your data. Collaborate online with SharePoint and the Access web database.Dive into Access programming. Get tricks and techniques to automate common tasks.Create rich data connections. Build dynamic links with SQL Server, SharePoint, and other systems.

    “Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena—from the price of airline tickets to the text of millions of books—into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior.In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing.www.big-data-book.com

    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.

    With this long-awaited revision, the authors continue to present the theory in a concise and straightforward manner, now with an eye out for the practical applications. They have revised this book to make it more accessible to today's students, including the addition of more material on writing proofs, more figures and pictures to convey ideas, side-boxes to highlight other interesting material, and a less formal writing style. Exercises at the end of each chapter, including some new, easier exercises, help readers confirm and enhance their understanding of the material. *NEW! Completely rewritten to be less formal, providing more accessibility to todays students. *NEW! Increased usage of figures and pictures to help convey ideas. *NEW! More detail and intuition provided for definitions and proofs. *NEW! Provides special side-boxes to present supplemental material that may be of interest to readers. *NEW! Includes more exercises, including many at a lower level. *NEW! Presents program-like notation for PDAs and Turing machines. *NEW! Increas

    The authors also include examples covering a variety of different integration technologies, such as JMS, MSMQ, TIBCO ActiveEnterprise, Microsoft BizTalk, SOAP, and XSL. A case study describing a bond trading system illustrates the patterns in practice, and the book offers a look at emerging standards, as well as insights into what the future of enterprise integration might hold. This book provides a consistent vocabulary and visual notation framework to describe large-scale integration solutions across many technologies. It also explores in detail the advantages and limitations of asynchronous messaging architectures. The authors present practical advice on designing code that connects an application to a messaging system, and provide extensive information to help you determine when to send a message, how to route it to the proper destination, and how to monitor the health of a messaging system. If you want to know how to manage, monitor, and maintain a messaging system once it is in use, get this book.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    You will be able to look over the shoulder of major coding and design experts to see problems through their eyes.This is not simply another design patterns book, or another software engineering treatise on the right and wrong way to do things. The authors think aloud as they work through their project's architecture, the tradeoffs made in its construction, and when it was important to break rules. Beautiful Code is an opportunity for master coders to tell their story. All author royalties will be donated to Amnesty International.

    You'll learn how to use Haskell in a variety of practical ways, from short scripts to large and demanding applications. Real World Haskell takes you through the basics of functional programming at a brisk pace, and then helps you increase your understanding of Haskell in real-world issues like I/O, performance, dealing with data, concurrency, and more as you move through each chapter. With this book, you will:Understand the differences between procedural and functional programming Learn the features of Haskell, and how to use it to develop useful programs Interact with filesystems, databases, and network services Write solid code with automated tests, code coverage, and error handling Harness the power of multicore systems via concurrent and parallel programming You'll find plenty of hands-on exercises, along with examples of real Haskell programs that you can modify, compile, and run. Whether or not you've used a functional language before, if you want to understand why Haskell is coming into its own as a practical language in so many major organizations, Real World Haskell is the best place to start.

    Discover the “hidden gold” in Python’s standard library and start writing clean and Pythonic code today. Who Should Read This Book: If you’re wondering which lesser known parts in Python you should know about, you’ll get a roadmap with this book. Discover cool (yet practical!) Python tricks and blow your coworkers’ minds in your next code review. If you’ve got experience with legacy versions of Python, the book will get you up to speed with modern patterns and features introduced in Python 3 and backported to Python 2. If you’ve worked with other programming languages and you want to get up to speed with Python, you’ll pick up the idioms and practical tips you need to become a confident and effective Pythonista. If you want to make Python your own and learn how to write clean and Pythonic code, you’ll discover best practices and little-known tricks to round out your knowledge. What Python Developers Say About The Book: "I kept thinking that I wished I had access to a book like this when I started learning Python many years ago." — Mariatta Wijaya, Python Core Developer"This book makes you write better Python code!" — Bob Belderbos, Software Developer at Oracle"Far from being just a shallow collection of snippets, this book will leave the attentive reader with a deeper understanding of the inner workings of Python as well as an appreciation for its beauty." — Ben Felder, Pythonista"It's like having a seasoned tutor explaining, well, tricks!" — Daniel Meyer, Sr. Desktop Administrator at Tesla Inc.

    Proceed step-by-step through five progressively more complex web applications: from a "Hello World" app all the way to a robust Newspaper app with a custom user model, complete user authentication flow, foreign key relationships, and more. Learn current best practices around class-based views, templates, urls, user authentication, testing, and deployment. The material is up-to-date with the latest versions of both Django (2.0) and Python (3.6). TABLE OF CONTENTS: * Introduction * Chapter 1: Initial Setup * Chapter 2: Hello World app * Chapter 3: Pages app * Chapter 4: Message Board app * Chapter 5: Blog app * Chapter 6: Forms * Chapter 7: User Accounts * Chapter 8: Custom User Model * Chapter 9: User Authentication * Chapter 10: Bootstrap * Chapter 11: Password Change and Reset * Chapter 12: Email * Chapter 13: Newspaper app * Chapter 14: Permissions and Authorizations * Chapter 15: Comments * Conclusion

    We start off easy, defining some of the tech lingo you may have heard before, but didn’t fully understand. From there, each lesson introduces something new, slowly building up to the point where you are confident about what an API is and, for the brave, could actually take a stab at using one.

    Feynman gave his famous course on computation at the California Institute of Technology, he asked Tony Hey to adapt his lecture notes into a book. Although led by Feynman, the course also featured, as occasional guest speakers, some of the most brilliant men in science at that time, including Marvin Minsky, Charles Bennett, and John Hopfield. Although the lectures are now thirteen years old, most of the material is timeless and presents a “Feynmanesque” overview of many standard and some not-so-standard topics in computer science such as reversible logic gates and quantum computers.

    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.

    With this practical guide, you’ll learn how to use Python scripts and web APIs to gather and process data from thousands—or even millions—of web pages at once. Ideal for programmers, security professionals, and web administrators familiar with Python, this book not only teaches basic web scraping mechanics, but also delves into more advanced topics, such as analyzing raw data or using scrapers for frontend website testing. Code samples are available to help you understand the concepts in practice. Learn how to parse complicated HTML pages Traverse multiple pages and sites Get a general overview of APIs and how they work Learn several methods for storing the data you scrape Download, read, and extract data from documents Use tools and techniques to clean badly formatted data Read and write natural languages Crawl through forms and logins Understand how to scrape JavaScript Learn image processing and text recognition

    You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled "Python for Informatics: Exploring Information".