Język R. Receptury. Analiza danych, statystyka i przetwarzanie grafiki. Wydanie II

JD Long, Paul Teetor

Język R jest potężnym narzędziem używanym w statystyce, przetwarzaniu grafiki i programowaniu statystycznym; stanowi konkurencję dla komercyjnych systemów do obliczeń tego rodzaju. Zawiera wszystkie narzędzia, których potrzebują statystycy. Równocześnie jest to specyficzny język, przez co jego użytkowanie może sprawiać problemy. Zarówno proste, jak i złożone zadania są łatwe do wykonania, jeśli tylko wiadomo, w jaki sposób je zrobić. Jeżeli jednak trzeba stopniowo dochodzić do właściwego rozwiązania, może to kosztować sporo cierpliwości i zniechęcać. Oto zbiór 275 receptur instruktażowych, z których każda pomaga w rozwiązaniu konkretnego problemu. Wszystkie zostały starannie przetestowane i wielokrotnie dowodziły swojej przydatności. Każda z omawianych receptur została poprzedzona krótkim wprowadzeniem i omówieniem zastosowanych mechanizmów działania. Nie jest to klasyczny podręcznik programowania, jednak z pewnością przyśpieszy naukę praktycznego wykorzystania możliwości R. Jeśli masz już pewne doświadczenie z tym językiem, odświeżysz swoją wiedzę i uzyskasz szerszą perspektywę. Wśród receptur znajdziesz obejmujące szeroki zakres zadania - od podstawowych operacji na danych wejściowych i wyjściowych, poprzez statystykę ogólną, aż po grafikę i regresję liniową. Dowiesz się również, jak wykorzystać język R do wizualizacji danych za pomocą ciekawych wykresów graficznych. W tej książce między innymi: przygotowywanie danych wejściowych i upraszczanie danych wyjściowych macierze, listy, wektory czynnikowe, ramki danych testy statystyczne, przedziały ufności, prawdopodobieństwa modele statystyczne z wykorzystaniem regresji liniowej i analizy wariancji stosowanie zaawansowanych technik statystycznych R: błyskawicznie osiągniesz znakomite wyniki!

Julia 1.0 Programming Cookbook. Over 100 numerical and distributed computing recipes for your daily data science work?ow

Bogumił Kamiński, Przemysław Szufel

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia.Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia.By the end of the book, you will have acquired the skills to work more effectively with your data

Julia for Data Science. high-performance computing simplified

Anshul Joshi

Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century).This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game.This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations.You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning.This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia.

Jupyter Cookbook. Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more

Dan Toomey

Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications.The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.

Jupyter for Data Science. Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

Dan Toomey

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook. If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter's features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks.By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.

Kibana 7 Quick Start Guide. Visualize your Elasticsearch data with ease

Anurag Srivastava

The Elastic Stack is growing rapidly and, day by day, additional tools are being added to make it more effective. This book endeavors to explain all the important aspects of Kibana, which is essential for utilizing its full potential.This book covers the core concepts of Kibana, with chapters set out in a coherent manner so that readers can advance their learning in a step-by-step manner. The focus is on a practical approach, thereby enabling the reader to apply those examples in real time for a better understanding of the concepts and to provide them with the correct skills in relation to the tool. With its succinct explanations, it is quite easy for a reader to use this book as a reference guide for learning basic to advanced implementations of Kibana. The practical examples, such as the creation of Kibana dashboards from CSV data, application RDBMS data, system metrics data, log file data, APM agents, and search results, can provide readers with a number of different drop-off points from where they can fetch any type of data into Kibana for the purpose of analysis or dashboarding.

Kibana 8.x - A Quick Start Guide to Data Analysis. Learn about data exploration, visualization, and dashboard building with Kibana

Krishna Shah

Unleash the full potential of Kibana—an indispensable tool for data analysts to seamlessly explore vast datasets, uncover key insights, identify trends and anomalies, and share results. This book guides you through its user-friendly interface, interactive visualizations, and robust features, including real-time data monitoring and advanced analytics, showing you how Kibana revolutionizes your approach to navigating and analyzing complex datasets.Starting with the foundational steps of installing, configuring, and running Kibana, this book progresses systematically to explain the search and data visualization capabilities for data stored in the Elasticsearch cluster. You’ll then delve into the practical details of creating data views and optimizing spaces to better organize the analysis environment. As you advance, you'll get to grips with using the discover interface and learn how to build different types of extensive visualizations using Lens.By the end of this book, you’ll have a complete understanding of how Kibana works, helping you leverage its capabilities to build an analytics and visualization solution from scratch for your data-driven use case.

Kibana Essentials. Use the functionalities of Kibana to discover data and build attractive visualizations and dashboards for real-world scenarios

Surendra Mohan, Yuvraj Gupta, Tim Roes