Analiza danych
Analiza danych jest ekscytującą dyscypliną, która umożliwia zrozumienie pewnych zjawisk, uzyskanie wglądu i wiedzy na podstawie surowych danych. Pojęcie to oznacza dokładnie przetwarzanie danych za pomocą technik matematycznych i statystycznych w celu uzyskania cennych wniosków, podjęcia ważnych decyzji i opracowania przydatnych produktów. Termin ten wywodzi się od angielskiego data science, często traktowanego jako synonim takich terminów, jak analityka biznesowa, badania operacyjne, business intelligence, wywiad konkurencyjny, analiza i modelowanie danych, a także pozyskiwanie wiedzy. Dzięki takim technologiom, jak języki Python czy R, platformy Hadoop i Spark masz szansę wyciągnąć maksimum wniosków, dostrzec szanse na rozwój swojej organizacji albo przewidzieć i zapobiec zagrożeniom.
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.
Toomey, Nikhil Borkar, Nikhil Akki, Juan Tomás...
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.
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.
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.
Yuvraj Gupta
Use the functionalities of Kibana to discover data and build attractive visualizations and dashboards for real-world scenariosAbout This BookPerform real-time data analytics and visualizations, on streaming data, using KibanaBuild beautiful visualizations and dashboards with simplicity and ease without any type of coding involvedLearn all the core concepts as well as detailed information about each component used in KibanaWho This Book Is ForWhether you are new to the world of data analytics and data visualization or an expert, this book will provide you with the skills required to use Kibana with ease and simplicity for real-time data visualization of streaming data.This book is intended for those professionals who are interested in learning about Kibana,its installations, and how to use it . As Kibana provides a user-friendly web page, no prior experience is required.What You Will LearnUnderstand the basic concepts of elasticsearch used in Kibana along with step by step guide to install Kibana in Windows and UbuntuExplore the functionality of all the components used in Kibana in detail, such as the Discover, Visualize, Dashboard,and Settings pagesAnalyze data using the powerful search capabilities of elasticsearchUnderstand the different types of aggregations used in Kibana for visualizationCreate and build different types of amazing visualizations and dashboards easilyCreate, save, share, embed, and customize the visualizations added to the dashboardCustomize and tweak the advanced settings of Kibana to ensure ease of useIn DetailWith the increasing interest in data analytics and visualization of large data around the globe, Kibana offers the best features to analyze data and create attractive visualizations and dashboards through simple-to-use web pages. The variety of visualizations provided, combined with the powerful underlying elasticsearch capabilities will help professionals improve their skills with this technology.This book will help you quickly familiarize yourself to Kibana and will also help you to understand the core concepts of this technology to build visualizations easily.Starting with setting up of Kibana and elasticsearch in Windows and Ubuntu, you will then use the Discover page to analyse your data intelligently. Next, you will learn to use the Visualization page to create beautiful visualizations without the need for any coding. Then, you will learn how to use the Dashboard page to create a dashboard and instantly share and embed the dashboards. You will see how to tweak the basic and advanced settings provided in Kibana to manage searches, visualizations, and dashboards. Finally, you will use Kibana to build visualizations and dashboards for real-world scenarios.You will quickly master the functionalities and components used in Kibana to create amazing visualizations based on real-world scenarios. With ample screenshots to guide you through every step, this book will assist you in creating beautiful visualizations with ease.Style and approachThis book is a comprehensive step-by-step guide to help you understand Kibana. It’s explained in an easy-to-follow style along with supporting images. Every chapter is explained sequentially , covering the basics of each component of Kibana and providing detailed explanations of all the functionalities of Kibana that appeal.
KNIME Essentials. Perform accurate data analysis using the power of KNIME
Gábor Bakos
KNIME is an open source data analytics, reporting, and integration platform, which allows you to analyze a small or large amount of data without having to reach out to programming languages like R.KNIME Essentials teaches you all you need to know to start processing your first data sets using KNIME. It covers topics like installation, data processing, and data visualization including the KNIME reporting features. Data processing forms a fundamental part of KNIME, and KNIME Essentials ensures that you are fully comfortable with this aspect of KNIME before showing you how to visualize this data and generate reports.KNIME Essentials guides you through the process of the installation of KNIME through to the generation of reports based on data. The main parts between these two phases are the data processing and the visualization. The KNIME variants of data analysis concepts are introduced, and after the configuration and installation description comes the data processing which has many options to convert or extend it. Visualization makes it easier to get an overview for parts of the data, while reporting offers a way to summarize them in a nice way.
Alberto Ferrari, Marco Russo
Kompletny przewodnik po DAX to najbardziej wyczerpujący i autorytatywny podręcznik języka Microsoft DAX w zastosowaniach BI i analityce. Podczas gdy inne książki omawiają tylko podstawy, ten przewodnik zapewnia wiedzę na poziomie eksperckim potrzebnych średnio i wysoko zaawansowanym użytkownikom Excela i profesjonalistom BI, od podstaw po innowacyjne techniki wysokiej wydajności. Dwaj czołowi konsultanci i wykładowcy Microsoft BI przedstawiają język DAX poprzez realistyczne i przydatne przykłady, prezentujące typowe obliczenia pozwalające użytkownikom od razu uzyskiwać wyniki. Alberto Ferrari i Marco Russo rozjaśniają złożone zagadnienia pokazując czytelnikom, co się dzieje pod maską silnika DAX, gdy wykonywane są wyrażenia. Ferrari i Russo starannie przechodzą przez typowe scenariusze, w których działanie DAX może być trudne do zrozumienia; pokazują, jak optymalizować modele danych, aby wyrażenia DAX były szybko wykonywane; demonstrują, jak pisać szybki i odporny kod DAX; wyjaśniają, jak usuwać wąskie gardła z istniejącego kodu. Pokazują działanie DAX zarówno w środowisku SQL Server, jak i Microsoft Excel, ułatwiając pracę bez względu na to, którą platformę Microsoft BI preferuje czytelnik.