Bazy danych
Matjaz B Juric, Kapil Pant
The book provides a well-balanced mixture of theoretical discussion and real-world examples. It explains the concepts and approaches, and describes methodology and notation. It demonstrates these concepts on real-world examples and provides a step-by-step example tutorial that guides readers from business process modeling in BPMN through transformation into BPEL to execution on the SOA process server. It also discusses some key concepts using practical examples and business scenarios around Business Rules Management and Business Activity Monitoring with BPM and SOA. This book is for CIOs, executives, SOA project managers, business process analysts, BPM and SOA architects, who are responsible for improving the efficiency of business processes through IT, or for designing SOA. It provides a high-level coverage of business process modeling, but it also gives practical development examples on how to move from model to execution. We expect the readers to be familiar with the basics of SOA.
Code with me. Zostań game developerem
Krzysztof Pianta
Projektuj, programuj, promuj! Zostań twórcą gier komputerowych! Nie zaglądaj tu, nie warto! Stracisz tylko czas, na sto procent nie dowiesz się niczego ciekawego, znudzisz się i będziesz rozczarowany, bo... z pewnością nie chcesz dołączyć do prawdziwej elity programistów, zdobyć poszukiwanych na rynku umiejętności, nauczyć się czegoś naprawdę ekscytującego ani uzyskać wpływu na jedną z najdynamiczniej rozwijających się gałęzi przemysłu komputerowego, prawda? Jeśli jednak mocno pragniesz zostać twórcą gier komputerowych, dobrze trafiłeś! Ta książka powstała właśnie z myślą o tych, którzy chcą rozpocząć karierę profesjonalnego game developera. Bezboleśnie wprowadzi Cię w zagadnienia związane z tworzeniem gier sieciowych 2D w językach: HTML5, PHP i MySQL. Nauczysz się projektować oprogramowanie, dbać o jakość rozwiązania, opracowywać niezbędne materiały graficzne i dźwiękowe, a nawet promować i sprzedawać swoje dzieło. Niszczenie terenu jak w grach Worms i Soldat Scrollowanie obrazu (kamera 2D) Pseudooświetlenie (2D lighting) Manipulowanie pikselami (getImageData) i proste efekty, na przykład blur (rozmycie) Różne typy kolizji, perfekcyjna kolizja (pixel perfect collision) System cząsteczek (efekty 2D): efekt gwiezdny (starfield effect), deszcz, śnieg, deszcz 3D, mgła lub dym NW.js (node-webkit) Rysowanie prostych kształtów, obrazków i sprite'ów Własny loader plików Grawitacja Menu obsługiwane za pomocą klawiatury lub myszy Zrób pierwszy krok na drodze do profesjonalnej kariery!
Corey Weisinger, Maarit Widmann, Daniele Tonini
This book will take you on a practical journey, teaching you how to implement solutions for many use cases involving time series analysis techniques.This learning journey is organized in a crescendo of difficulty, starting from the easiest yet effective techniques applied to weather forecasting, then introducing ARIMA and its variations, moving on to machine learning for audio signal classification, training deep learning architectures to predict glucose levels and electrical energy demand, and ending with an approach to anomaly detection in IoT. There’s no time series analysis book without a solution for stock price predictions and you’ll find this use case at the end of the book, together with a few more demand prediction use cases that rely on the integration of KNIME Analytics Platform and other external tools.By the end of this time series book, you’ll have learned about popular time series analysis techniques and algorithms, KNIME Analytics Platform, its time series extension, and how to apply both to common use cases.
Cameron Dodd
The CompTIA Data+ certification exam not only helps validate a skill set required to enter one of the fastest-growing fields in the world, but also is starting to standardize the language and concepts within the field. However, there’s a lot of conflicting information and a lack of existing resources about the topics covered in this exam, and even professionals working in data analytics may need a study guide to help them pass on their first attempt.The CompTIA Data + (DAO-001) Certification Guide will give you a solid understanding of how to prepare, analyze, and report data for better insights.You’ll get an introduction to Data+ certification exam format to begin with, and then quickly dive into preparing data. You'll learn about collecting, cleaning, and processing data along with data wrangling and manipulation. As you progress, you’ll cover data analysis topics such as types of analysis, common techniques, hypothesis techniques, and statistical analysis, before tackling data reporting, common visualizations, and data governance. All the knowledge you've gained throughout the book will be tested with the mock tests that appear in the final chapters.By the end of this book, you’ll be ready to pass the Data+ exam with confidence and take the next step in your career.
Kedeisha Bryan, Taamir Ransome
Preparing for a data engineering interview can often get overwhelming due to the abundance of tools and technologies, leaving you struggling to prioritize which ones to focus on. This hands-on guide provides you with the essential foundational and advanced knowledge needed to simplify your learning journey.The book begins by helping you gain a clear understanding of the nature of data engineering and how it differs from organization to organization. As you progress through the chapters, you’ll receive expert advice, practical tips, and real-world insights on everything from creating a resume and cover letter to networking and negotiating your salary. The chapters also offer refresher training on data engineering essentials, including data modeling, database architecture, ETL processes, data warehousing, cloud computing, big data, and machine learning. As you advance, you’ll gain a holistic view by exploring continuous integration/continuous development (CI/CD), data security, and privacy. Finally, the book will help you practice case studies, mock interviews, as well as behavioral questions.By the end of this book, you will have a clear understanding of what is required to succeed in an interview for a data engineering role.
Dane grafowe w praktyce. Jak technologie grafowe ułatwiają rozwiązywanie złożonych problemów
Denise Gosnell, Matthias Broecheler
Komputer do pracy potrzebuje liczb i danych. Człowiek chętniej wysnuwa wnioski i wyodrębnia kontekst na podstawie relacji. Te dwa sposoby myślenia są tak odmienne, że komputery do niedawna z trudem wykonywały zadania związane z operowaniem na relacjach. Obecnie może się to zmienić dzięki grafom. Technologie grafowe łączą ludzkie postrzeganie świata i liniową pamięć komputerów. Ich wdrożenie na szerszą skalę będzie stanowić przełom i pozwoli osiągnąć nieznany dziś poziom. Ale najpierw trzeba nauczyć się stosować myślenie grafowe w rozwiązywaniu problemów technicznych. Dzięki tej książce opanujesz podstawy myślenia grafowego. Zapoznasz się z elementarnymi koncepcjami grafowymi: teorią grafów, schematami baz danych, systemami rozproszonymi, a także analizą danych. Dowiesz się również, jak wyglądają typowe wzorce wykorzystania danych grafowych w aplikacjach produkcyjnych. Poznasz sposób, w jaki można te wzorce stosować w praktyce. Pokazano tu, jak używać technik programowania funkcyjnego oraz systemów rozproszonych do tworzenia zapytań i analizowania danych grafowych. Opisano też podstawowe podejścia do proceduralnego przechodzenia przez dane grafowe i ich wykorzystanie za pomocą narzędzi grafowych. W książce: nowy paradygmat rozwiązywania problemów: dane grafowe wzorce wykorzystania danych grafowych przykładowa architektura aplikacji w technologiach relacyjnych i grafowych technologie grafowe a przewidywanie preferencji i zaufania użytkowników filtrowanie kolaboratywne i jego zastosowanie Grafy: przełomowa koncepcja w analizie danych!
Michael Walker
Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results.As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You’ll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you’ll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You’ll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book.By the end of this book, you’ll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.
Data Engineering Best Practices. Architect robust and cost-effective data solutions in the cloud era
Richard J. Schiller, David Larochelle
Revolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications.By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.