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  • Online Resource  (10)
  • Mailund, Thomas  (10)
  • [Erscheinungsort nicht ermittelbar] : Apress  (5)
  • [Place of publication not identified] : Apress  (5)
  • Electronic books ; local  (10)
  • Electronic videos ; local
  • 1
    Online Resource
    Online Resource
    Pointers in C Programming : A Modern Approach to Memory Management, Recursive Data Structures, Strings, and Arrays (2021)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    ISBN: 9781484269275
    Language: English
    Pages: 1 online resource (539 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local ; Electronic books
    Abstract: Gain a better understanding of pointers, from the basics of how pointers function at the machine level, to using them for a variety of common and advanced scenarios. This short contemporary guide book on pointers in C programming provides a resource for professionals and advanced students needing in-depth hands-on coverage of pointer basics and advanced features. It includes the latest versions of the C language, C20, C17, and C14. You’ll see how pointers are used to provide vital C features, such as strings, arrays, higher-order functions and polymorphic data structures. Along the way, you’ll cover how pointers can optimize a program to run faster or use less memory than it would otherwise. There are plenty of code examples in the book to emulate and adapt to meet your specific needs. What You Will Learn Work effectively with pointers in your C programming Learn how to effectively manage dynamic memory Program with strings and arrays Create recursive data structures Implement function pointers Who This Book Is For Intermediate to advanced level professional programmers, software developers, and advanced students or researchers. Prior experience with C programming is expected.
    Note: Online resource; Title from title page (viewed April 22, 2021) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 2
    Online Resource
    Online Resource
    Introduction to Computational Thinking : Problem Solving, Algorithms, Data Structures, and More (2021)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    ISBN: 9781484270776
    Language: English
    Pages: 1 online resource (661 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local ; Electronic books
    Abstract: Learn approaches of computational thinking and the art of designing algorithms. Most of the algorithms you will see in this book are used in almost all software that runs on your computer. Learning how to program can be very rewarding. It is a special feeling to seeing a computer translate your thoughts into actions and see it solve your problems for you. To get to that point, however, you must learn to think about computations in a new way—you must learn computational thinking. This book begins by discussing models of the world and how to formalize problems. This leads onto a definition of computational thinking and putting computational thinking in a broader context. The practical coding in the book is carried out in Python; you’ll get an introduction to Python programming, including how to set up your development environment. What You Will Learn Think in a computational way Acquire general techniques for problem solving See general and concrete algorithmic techniques Program solutions that are both computationally efficient and maintainable Who This Book Is For Those new to programming and computer science who are interested in learning how to program algorithms and working with other computational aspects of programming.
    Note: Online resource; Title from title page (viewed July 16, 2021) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 3
    Online Resource
    Online Resource
    String Algorithms in C : Efficient Text Representation and Search (2020)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    ISBN: 9781484259207
    Language: English
    Pages: 1 online resource (299 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local ; Electronic books
    Abstract: Implement practical data structures and algorithms for text search and discover how it is used inside other larger applications. This unique in-depth guide explains string algorithms using the C programming language. String Algorithms in C teaches you the following algorithms and how to use them: classical exact search algorithms; tries and compact tries; suffix trees and arrays; approximative pattern searches; and more. In this book, author Thomas Mailund provides a library with all the algorithms and applicable source code that you can use in your own programs. There are implementations of all the algorithms presented in this book so there are plenty of examples. You’ll understand that string algorithms are used in various applications such as image processing, computer vision, text analytics processing from data science to web applications, information retrieval from databases, network security, and much more. What You Will Learn Use classical exact search algorithms including naive search, borders/border search, Knuth-Morris-Pratt, and Boyer-Moor with or without Horspool Search in trees, use tries and compact tries, and work with the Aho-Carasick algorithm Process suffix trees including the use and development of McCreight’s algorithm Work with suffix arrays including binary searches; sorting naive constructions; suffix tree construction; skew algorithms; and the Borrows-Wheeler transform (BWT) Deal with enhanced suffix arrays including longest common prefix (LCP) Carry out approximative pattern searches among suffix trees and approximative BWT searches Who This Book Is For Those with at least some prior programming experience with C or Assembly and have at least prior experience with programming algorithms.
    Note: Online resource; Title from title page (viewed August 28, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 4
    Online Resource
    Online Resource
    Introducing Markdown and Pandoc : Using Markup Language and Document Converter (2019)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    ISBN: 9781484251492
    Language: English
    Pages: 1 online resource (141 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local ; Electronic books
    Abstract: Discover how to write manuscripts in Markdown and translate them with Pandoc into different output formats. You’ll use Markdown to annotate text formatting information with a strong focus on semantic information: you can annotate your text with information about where chapters and sections start, but not how chapter and heading captions should be formatted. As a result, you’ll decouple the structure of a text from how it is visualized and make it easier for you to produce different kinds of output. The same text can easily be formatted as HTML, PDF, or Word documents, with various visual styles, by tools that understand the markup annotations. Finally, you’ll learn to use Pandoc, a tool for translating between different markup languages, such as LaTeX, HTML, and Markdown. This book will not describe all the functionality that Pandoc provides, but will teach you how to translate Markdown documents, how to customize your documents using templates, and how to extend Pandoc’s functionality using filters. If that is something you are interested in, Introducing Markdown and Pandoc will get you started. With this set of skills you’ll be able to write more efficiently without worrying about needless formatting and other distractions. What You Will Learn Why and how to use Markdown and Pandoc Write Markdown Use extensions available in Pandoc and Markdown Write math and code blocks Use templates and produce documents Who This Book Is For Programmers and problem solvers looking for technical documentation solutions.
    Note: Online resource; Title from title page (viewed August 16, 2019)
    URL: lizenzpflichtig
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  • 5
    Online Resource
    Online Resource
    R Data Science Quick Reference : A Pocket Guide to APIs, Libraries, and Packages (2019)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    ISBN: 9781484248942
    Language: English
    Pages: 1 online resource (246 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local ; Electronic books
    Abstract: In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more. After using this handy quick reference guide, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. What You Will Learn Import data with readr Work with categories using forcats, time and dates with lubridate, and strings with stringr Format data using tidyr and then transform that data using magrittr and dplyr Write functions with R for data science, data mining, and analytics-based applications Visualize data with ggplot2 and fit data to models using modelr Who This Book Is For Programmers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended.
    Note: Online resource; Title from title page (viewed August 7, 2019)
    URL: lizenzpflichtig
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  • 6
    Online Resource
    Online Resource
    Domain-specific languages in R : advanced statistical programming (2018)
    [Place of publication not identified] : Apress
    Details
    ISBN: 9781484235881
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: R (Computer program language) ; Domain-specific programming languages ; Electronic books ; local ; Electronic books
    Abstract: Gain an accelerated introduction to domain-specific languages in R, including coverage of regular expressions. This compact, in-depth book shows you how DSLs are programming languages specialized for a particular purpose, as opposed to general purpose programming languages. Along the way, you'll learn to specify tasks you want to do in a precise way and achieve programming goals within a domain-specific context. Domain-Specific Languages in R includes examples of DSLs including large data sets or matrix multiplication; pattern matching DSLs for application in computer vision; and DSLs for continuous time Markov chains and their applications in data science. After reading and using this book, you'll understand how to write DSLs in R and have skills you can extrapolate to other programming languages. What You'll Learn Program with domain-specific languages using R Discover the components of DSLs Carry out large matrix expressions and multiplications Implement metaprogramming with DSLs Parse and manipulate expressions Who This Book Is For Those with prior programming experience. R knowledge is helpful but not required.
    Note: Includes bibliographical references. - Description based on online resource; title from cover (Safari, viewed July 18, 2018)
    URL: lizenzpflichtig
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  • 7
    Online Resource
    Online Resource
    Functional programming in R : advanced statistical programming for data science, analysis and finance (2017)
    [Place of publication not identified] : Apress
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Functional programming (Computer science) ; R (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Master functions and discover how to write functional programs in R. In this concise book, you'll make your functions pure by avoiding side-effects; you'll write functions that manipulate other functions, and you'll construct complex functions using simpler functions as building blocks. In Functional Programming in R , you'll see how we can replace loops, which can have side-effects, with recursive functions that can more easily avoid them. In addition, the book covers why you shouldn't use recursion when loops are more efficient and how you can get the best of both worlds. Functional programming is a style of programming, like object-oriented programming, but one that focuses on data transformations and calculations rather than objects and state. Where in object-oriented programming you model your programs by describing which states an object can be in and how methods will reveal or modify that state, in functional programming you model programs by describing how functions translate input data to output data. Functions themselves are considered to be data you can manipulate and much of the strength of functional programming comes from manipulating functions; that is, building more complex functions by combining simpler functions. What You'll Learn Write functions in R including infix operators and replacement functions Create higher order functions Pass functions to other functions and start using functions as data you can manipulate Use Filer, Map and Reduce functions to express the intent behind code clearly and safely Build new functions from existing functions without necessarily writing any new functions, using point-free programming Create functions that carry data along with them Who This Book Is For Those with at least some experience with programming in R.
    Note: Description based on online resource; title from cover (Safari, viewed April 18, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781484227466/?ar
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  • 8
    Online Resource
    Online Resource
    Advanced object-oriented programming in R : statistical programming for data science, analysis and finance (2017)
    [Place of publication not identified] : Apress
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: R (Computer program language) ; Object-oriented programming (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Learn how to write object-oriented programs in R and how to construct classes and class hierarchies in the three object-oriented systems available in R. This book gives an introduction to object-oriented programming in the R programming language and shows you how to use and apply R in an object-oriented manner. You will then be able to use this powerful programming style in your own statistical programming projects to write flexible and extendable software. After reading Advanced Object-Oriented Programming in R , you'll come away with a practical project that you can reuse in your own analytics coding endeavors. You'll then be able to visualize your data as objects that have state and then manipulate those objects with polymorphic or generic methods. Your projects will benefit from the high degree of flexibility provided by polymorphism, where the choice of concrete method to execute depends on the type of data being manipulated. What You'll Learn Define and use classes and generic functions using R Work with the R class hierarchies Benefit from implementation reuse Handle operator overloading Apply the S4 and R6 classes Who This Book Is For Experienced programmers and for those with at least some prior experience with R programming language.
    Note: Description based on online resource; title from cover (Safari, viewed September 29, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781484229187/?ar
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  • 9
    Online Resource
    Online Resource
    Metaprogramming in R : advanced statistical programming for data science, analysis and finance (2017)
    [Place of publication not identified] : Apress
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: R (Computer program language) ; Computer programming ; Mathematical statistics ; Electronic books ; Electronic books ; local
    Abstract: Learn how to manipulate functions and expressions to modify how the R language interprets itself. This book is an introduction to metaprogramming in the R language, so you will write programs to manipulate other programs. Metaprogramming in R shows you how to treat code as data that you can generate, analyze, or modify. R is a very high-level language where all operations are functions and all functions are data that can be manipulated. This book shows you how to leverage R's natural flexibility in how function calls and expressions are evaluated, to create small domain-specific languages to extend R within the R language itself. What You'll Learn Find out about the anatomy of a function in R Look inside a function call Work with R expressions and environments Manipulate expressions in R Use substitutions Who This Book Is For Those with at least some experience with R and certainly for those with experience in other programming languages
    Note: Description based on online resource; title from cover page (Safari, viewed September 29, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781484228814/?ar
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  • 10
    Online Resource
    Online Resource
    Beginning data science in R : data analysis, visualization, and modelling for the data scientist (2017)
    [Place of publication not identified] : Apress
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Quantitative research ; R (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language.
    Note: Description based on online resource; title from cover (Safari, viewed April 18, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781484226711/?ar
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