Bayesian Analysis with Python. Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ - Second Edition

Osvaldo Martin

The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.

Become a Python Data Analyst. Perform exploratory data analysis and gain insight into scientific computing using Python

Alvaro Fuentes

Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations.Become a Python Data Analyst introduces Python’s most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations.In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques.By the end of this book, you will have hands-on experience performing data analysis with Python.

Big Data Analytics with Hadoop 3. Build highly effective analytics solutions to gain valuable insight into your big data

Sridhar Alla

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples.Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases.By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.

Big Data Architect's Handbook. A guide to building proficiency in tools and systems used by leading big data experts

Syed Muhammad Fahad Akhtar

The big data architects are the “masters” of data, and hold high value in today’s market. Handling big data, be it of good or bad quality, is not an easy task. The prime job for any big data architect is to build an end-to-end big data solution that integrates data from different sources and analyzes it to find useful, hidden insights.Big Data Architect’s Handbook takes you through developing a complete, end-to-end big data pipeline, which will lay the foundation for you and provide the necessary knowledge required to be an architect in big data. Right from understanding the design considerations to implementing a solid, efficient, and scalable data pipeline, this book walks you through all the essential aspects of big data. It also gives you an overview of how you can leverage the power of various big data tools such as Apache Hadoop and ElasticSearch in order to bring them together and build an efficient big data solution.By the end of this book, you will be able to build your own design system which integrates, maintains, visualizes, and monitors your data. In addition, you will have a smooth design flow in each process, putting insights in action.

Big Data Forensics - Learning Hadoop Investigations. Perform forensic investigations on Hadoop clusters with cutting-edge tools and techniques

Joseph Sremack, Joe Sremack

Big Data. Najlepsze praktyki budowy skalowalnych systemów obsługi danych w czasie rzeczywistym

Nathan Marz, James Warren

Obsługa aplikacji, które operują na ogromnych zbiorach danych, czyli na przykład portali społecznościowych, przekracza możliwości zwykłych relacyjnych baz. Praca ze złożonymi zbiorami danych wymaga architektury obejmującej wielomaszynowe klastry, dzięki którym możliwe jest przechowywanie i przesyłanie informacji praktycznie dowolnej wielkości. Architektura taka powinna dodatkowo być prosta w użyciu, niezawodna i skalowalna. Dzięki tej książce nauczysz się budować tego rodzaju architekturę. Zapoznasz się z technologią wykorzystywania klastrów maszyn. Dowiesz się, jak działają narzędzia przeznaczone specjalnie do przechwytywania i analizy danych na wielką skalę. W książce zaprezentowano łatwe do zrozumienia podejście do obsługi systemów wielkich zbiorów danych, które mogą być budowane i uruchamiane przez niewielki zespół. Nie zabrakło też wyczerpującego opisu praktycznej implementacji systemu Big Data z wykorzystaniem rzeczywistego przykładu. W tej książce znajdziesz: teoretyczne podstawy koncepcji systemów Big Data wskazówki umożliwiające optymalne wykorzystanie zasobów do obsługi danych wybór technik przetwarzania i obsługi wielkich ilości danych w czasie rzeczywistym zagadnienia dotyczące baz danych NoSQL, przetwarzania strumieniowego i zarządzania złożonością obliczeń przyrostowych informacje o praktycznym stosowaniu takich narzędzi jak Hadoop, Cassandra i Storm wskazówki umożliwiające poszerzenie wiedzy o zwykłych bazach danych Big Data — to skalowalność i prostota obsługi wielkich ilości danych!

Big Data Using Hadoop and Hive. Master Big Data Solutions with Hadoop and Hive

Mercury Learning and Information, Nitin Kumar

This book is a guide for developers and engineers to use Hadoop and Hive for scalable big data applications. It covers reading, writing, and managing large datasets with Hive and provides a concise introduction to Apache Hadoop and Hive, detailing their collaboration to simplify development. Through clear examples, the book explains the logic, code, and configurations needed for building successful distributed applications.The course starts with an introduction to big data and Apache Hadoop fundamentals. It then covers the Hadoop Distributed Filesystem and how to get started with Hadoop. The journey continues with interfaces to access HDFS files, resource management with Yet Another Resource Negotiator, and MapReduce for data processing. The book also explores Hive architecture, storage types, and the Hive query language.Mastering these concepts is vital for creating scalable big data solutions. This book ensures a smooth transition from novice to proficient Hadoop and Hive user, providing practical skills and comprehensive knowledge. By the end, readers will be able to set up, configure, and optimize Hadoop, utilize Hive for data management, and effectively solve big data challenges.

Blender 3D Printing by Example. Learn to use Blender's modeling tools for 3D printing by creating 4 projects

Vicky Somma

Blender is an open-source modeling and animation program popular in the 3D printing community. 3D printing brings along different considerations than animation and virtual reality.This book walks you through four projects to learn using Blender for 3D Printing, giving you information that you need to know to create high-quality 3D printed objects.The book starts with two jewelry projects-- a pendant of a silhouette and a bracelet with custom text. We then explore architectural modeling as you learn to makes a figurine from photos of a home. The final project, a human hand, illustrates how Blender can be used for organic models and how colors can be added to the design.You will learn modeling for 3D printing with the help of these projects. Whether you plan to print at-home or use a service bureau, you’ll start by understanding design requirements. The book begins with simple projects to get you started with 3D modeling basics and the tools available in Blender. As the book progresses, you’ll get exposed to more robust mesh modeling techniques, modifiers, and Blender shortcuts. By the time you reach your final project, you’ll be ready for organic modeling and learning how to add colors. In the final section, you’ll learn how to check for and correct common modeling issues to ensure the 3D printer can make your idea a reality!

Blockchain By Example. A developer's guide to creating decentralized applications using Bitcoin, Ethereum, and Hyperledger

Bellaj Badr, Richard Horrocks, Xun (Brian) Wu

The Blockchain is a revolution promising a new world without middlemen. Technically, it is an immutable and tamper-proof distributed ledger of all transactions across a peer-to-peer network. With this book, you will get to grips with the blockchain ecosystem to build real-world projects.This book will walk you through the process of building multiple blockchain projects with different complexity levels and hurdles. Each project will teach you just enough about the field's leading technologies, Bitcoin, Ethereum, Quorum, and Hyperledger in order to be productive from the outset. As you make your way through the chapters, you will cover the major challenges that are associated with blockchain ecosystems such as scalability, integration, and distributed file management. In the concluding chapters, you’ll learn to build blockchain projects for business, run your ICO, and even create your own cryptocurrency. Blockchain by Example also covers a range of projects such as Bitcoin payment systems, supply chains on Hyperledger, and developing a Tontine Bank Every is using Ethereum.By the end of this book, you will not only be able to tackle common issues in the blockchain ecosystem, but also design and build reliable and scalable distributed systems.

Blockchain for Business 2019. A user-friendly introduction to blockchain technology and its business applications

Peter Lipovyanov

Blockchain for Business 2019 is a comprehensive guide that enables you to bring in various blockchain functionalities to extend your existing business models and make correct fully-informed decisions. You will learn how decentralized applications are transforming numerous business sectors that are expected to play a huge role in the future. You will see how large corporations are already implementing blockchain technology now. You will then learn about the various blockchain services, such as Bitcoin, Ethereum, Hyperledger, and others to understand their use cases in a variety of business domains. You will develop a solid fundamental understanding of blockchain architecture. Moving ahead, you will get to grips with the inner workings of blockchain, with detailed explanations of mining, decentralized consensus, cryptography, smart contracts, and many other important concepts. You will delve into a realistic view of the current state of blockchain technology, along with its issues, limitations, and potential solutions that can take it to the next level.By the end of this book, you will all be well versed in the latest innovations and developments in the emerging blockchain space.

Blockchain for Enterprise. Build scalable blockchain applications with privacy, interoperability, and permissioned features

Narayan Prusty

The increasing growth in blockchain use is enormous, and it is changing the way business is done. Many leading organizations are already exploring the potential of blockchain. With this book, you will learn to build end-to-end enterprise-level decentralized applications and scale them across your organization to meet your company's needs. This book will help you understand what DApps are and how the blockchain ecosystem works, via real-world examples. This extensive end-to-end book covers every blockchain aspect for business and for developers. You will master process flows and incorporate them into your own enterprise. You will learn how to use J.P. Morgan’s Quorum to build blockchain-based applications. You will also learn how to write applications that can help communicate enterprise blockchain solutions. You will learn how to write smart contracts that run without censorship and third-party interference.Once you've grasped what a blockchain is and have learned about Quorum, you will jump into building real-world practical blockchain applications for sectors such as payment and money transfer, healthcare, cloud computing, supply chain management, and much more.

Blockchain Quick Reference. A guide to exploring decentralized blockchain application development

Brenn Hill, Samanyu Chopra, Paul Valencourt

Blockchain Quick Reference takes you through the electrifying world of blockchain technology and is designed for those who want to polish their existing knowledge regarding the various pillars of the blockchain ecosystem.This book is your go-to guide, teaching you how to apply principles and ideas for making your life and business better. You will cover the architecture, Initial Coin Offerings (ICOs), tokens, smart contracts, and terminologies of the blockchain technology, before studying how they work. All you need is a curious mind to get started with blockchain technology. Once you have grasped the basics, you will explore components of Ethereum, such as ether tokens, transactions, and smart contracts, in order to build simple Dapps. You will then move on to learning why Solidity is used specifically for Ethereum-based projects, followed by exploring different types of blockchain with easy-to-follow examples. All this will help you tackle challenges and problems. By the end of this book, you will not only have solved current and future problems relating to blockchain technology but will also be able to build efficient decentralized applications.

Blockchain Quick Start Guide. A beginner's guide to developing enterprise-grade decentralized applications

Xun (Brian) Wu, Weimin Sun

Blockchain is a technology that powers the development of decentralized applications.This technology allows the construction of a network with no single control that enables participants to make contributions to and receive benefits from the network directly.This book will give you a thorough overview of blockchain and explain how a blockchain works.You will begin by going through various blockchain consensus mechanisms and cryptographic hash functions. You will then learn the fundamentals of programming in Solidity – the defacto language for developing decentralize, applications in Ethereum. After that, you will set up an Ethereum development environment and develop, package, build, and test campaign-decentralized applications.The book also shows you how to set up Hyperledger composer tools, analyze business scenarios, design business models, and write a chain code. Finally, you will get a glimpse of how blockchain is actually used in different real-world domains. By the end of this guide, you will be comfortable working with basic blockchain frameworks, and develop secure, decentralized applications in a hassle-free manner.

Building Data Streaming Applications with Apache Kafka. Design, develop and streamline applications using Apache Kafka, Storm, Heron and Spark

Chanchal Singh, Manish Kumar

Apache Kafka is a popular distributed streaming platform that acts as a messaging queue or an enterprise messaging system. It lets you publish and subscribe to a stream of records, and process them in a fault-tolerant way as they occur.This book is a comprehensive guide to designing and architecting enterprise-grade streaming applications using Apache Kafka and other big data tools. It includes best practices for building such applications, and tackles some common challenges such as how to use Kafka efficiently and handle high data volumes with ease. This book first takes you through understanding the type messaging system and then provides a thorough introduction to Apache Kafka and its internal details. The second part of the book takes you through designing streaming application using various frameworks and tools such as Apache Spark, Apache Storm, and more. Once you grasp the basics, we will take you through more advanced concepts in Apache Kafka such as capacity planning and security.By the end of this book, you will have all the information you need to be comfortable with using Apache Kafka, and to design efficient streaming data applications with it.

Cloud Analytics with Google Cloud Platform. An end-to-end guide to processing and analyzing big data using Google Cloud Platform

Sanket Thodge

With the ongoing data explosion, more and more organizations all over the world are slowly migrating their infrastructure to the cloud. These cloud platforms also provide their distinct analytics services to help you get faster insights from your data. This book will give you an introduction to the concept of analytics on the cloud, and the different cloud services popularly used for processing and analyzing data. If you’re planning to adopt the cloud analytics model for your business, this book will help you understand the design and business considerations to be kept in mind, and choose the best tools and alternatives for analytics, based on your requirements. The chapters in this book will take you through the 70+ services available in Google Cloud Platform and their implementation for practical purposes. From ingestion to processing your data, this book contains best practices on building an end-to-end analytics pipeline on the cloud by leveraging popular concepts such as machine learning and deep learning.By the end of this book, you will have a better understanding of cloud analytics as a concept as well as a practical know-how of its implementation

Cloud Native Development Patterns and Best Practices. Practical architectural patterns for building modern, distributed cloud-native systems

John Gilbert

Build systems that leverage the benefits of the cloud and applications faster than ever before with cloud-native development. This book focuses on architectural patterns for building highly scalable cloud-native systems. You will learn how the combination of cloud, reactive principles, devops, and automation enable teams to continuously deliver innovation with confidence.Begin by learning the core concepts that make these systems unique. You will explore foundational patterns that turn your database inside out to achieve massive scalability with cloud-native databases. You will also learn how to continuously deliver production code with confidence by shifting deployment and testing all the way to the left and implementing continuous observability in production. There's more—you will also learn how to strangle your monolith and design an evolving cloud-native system.By the end of the book, you will have the ability to create modern cloud-native systems.

Computer Vision Projects with OpenCV and Python 3. Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow

Matthew Rever

Python is the ideal programming language for rapidly prototyping and developing production-grade codes for image processing and Computer Vision with its robust syntax and wealth of powerful libraries. This book will help you design and develop production-grade Computer Vision projects tackling real-world problems.With the help of this book, you will learn how to set up Anaconda and Python for the major OSes with cutting-edge third-party libraries for Computer Vision. You'll learn state-of-the-art techniques for classifying images, finding and identifying human postures, and detecting faces within videos. You will use powerful machine learning tools such as OpenCV, Dlib, and TensorFlow to build exciting projects such as classifying handwritten digits, detecting facial features,and much more. The book also covers some advanced projects, such as reading text from license plates from real-world images using Google’s Tesseract software, and tracking human body poses using DeeperCut within TensorFlow.By the end of this book, you will have the expertise required to build your own Computer Vision projects using Python and its associated libraries.

Cracking the Data Science Interview. Unlock insider tips from industry experts to master the data science field

Leondra R. Gonzalez, Aaren Stubberfield, Angela Baltes

The data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company.Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview.By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.

Dancing with Qubits. From qubits to algorithms, embark on the quantum computing journey shaping our future - Second Edition

Robert S. Sutor

Dancing with Qubits, Second Edition, is a comprehensive quantum computing textbook that starts with an overview of why quantum computing is so different from classical computing and describes several industry use cases where it can have a major impact. A full description of classical computing and the mathematical underpinnings of quantum computing follows, helping you better understand concepts such as superposition, entanglement, and interference. Next up are circuits and algorithms, both basic and sophisticated, as well as a survey of the physics and engineering ideas behind how quantum computing hardware is built. Finally, the book looks to the future and gives you guidance on understanding how further developments may affect you.This new edition is updated throughout with more than 100 new exercises and includes new chapters on NISQ algorithms and quantum machine learning.Understanding quantum computing requires a lot of math, and this book doesn't shy away from the necessary math concepts you'll need. Each topic is explained thoroughly and with helpful examples, leaving you with a solid foundation of knowledge in quantum computing that will help you pursue and leverage quantum-led technologies.

Dancing with Qubits. How quantum computing works and how it can change the world

Robert S. Sutor

Quantum computing is making us change the way we think about computers. Quantum bits, a.k.a. qubits, can make it possible to solve problems that would otherwise be intractable with current computing technology. Dancing with Qubits is a quantum computing textbook that starts with an overview of why quantum computing is so different from classical computing and describes several industry use cases where it can have a major impact. From there it moves on to a fuller description of classical computing and the mathematical underpinnings necessary to understand such concepts as superposition, entanglement, and interference. Next up is circuits and algorithms, both basic and more sophisticated. It then nicely moves on to provide a survey of the physics and engineering ideas behind how quantum computing hardware is built. Finally, the book looks to the future and gives you guidance on understanding how further developments will affect you.Really understanding quantum computing requires a lot of math, and this book doesn't shy away from the necessary math concepts you'll need. Each topic is introduced and explained thoroughly, in clear English with helpful examples.

Data Analysis with IBM SPSS Statistics. Implementing data modeling, descriptive statistics and ANOVA

Ken Stehlik-Barry, Anthony Babinec

SPSS Statistics is a software package used for logical batched and non-batched statistical analysis. Analytical tools such as SPSS can readily provide even a novice user with an overwhelming amount of information and a broad range of options for analyzing patterns in the data. The journey starts with installing and configuring SPSS Statistics for first use and exploring the data to understand its potential (as well as its limitations). Use the right statistical analysis technique such as regression, classification and more, and analyze your data in the best possible manner. Work with graphs and charts to visualize your findings. With this information in hand, the discovery of patterns within the data can be undertaken. Finally, the high level objective of developing predictive models that can be applied to other situations will be addressed. By the end of this book, you will have a firm understanding of the various statistical analysis techniques offered by SPSS Statistics, and be able to master its use for data analysis with ease.

Data Lake for Enterprises. Lambda Architecture for building enterprise data systems

Vivek Mishra, Tomcy John, Pankaj Misra

The term Data Lake has recently emerged as a prominent term in the big data industry. Data scientists can make use of it in deriving meaningful insights that can be used by businesses to redefine or transform the way they operate. Lambda architecture is also emerging as one of the very eminent patterns in the big data landscape, as it not only helps to derive useful information from historical data but also correlates real-time data to enable business to take critical decisions. This book tries to bring these two important aspects — data lake and lambda architecture—together.This book is divided into three main sections. The first introduces you to the concept of data lakes, the importance of data lakes in enterprises, and getting you up-to-speed with the Lambda architecture. The second section delves into the principal components of building a data lake using the Lambda architecture. It introduces you to popular big data technologies such as Apache Hadoop, Spark, Sqoop, Flume, and ElasticSearch. The third section is a highly practical demonstration of putting it all together, and shows you how an enterprise data lake can be implemented, along with several real-world use-cases. It also shows you how other peripheral components can be added to the lake to make it more efficient.By the end of this book, you will be able to choose the right big data technologies using the lambda architectural patterns to build your enterprise data lake.

Data Lakehouse in Action. Architecting a modern and scalable data analytics platform

Pradeep Menon

The Data Lakehouse architecture is a new paradigm that enables large-scale analytics. This book will guide you in developing data architecture in the right way to ensure your organization's success.The first part of the book discusses the different data architectural patterns used in the past and the need for a new architectural paradigm, as well as the drivers that have caused this change. It covers the principles that govern the target architecture, the components that form the Data Lakehouse architecture, and the rationale and need for those components. The second part deep dives into the different layers of Data Lakehouse. It covers various scenarios and components for data ingestion, storage, data processing, data serving, analytics, governance, and data security. The book's third part focuses on the practical implementation of the Data Lakehouse architecture in a cloud computing platform. It focuses on various ways to combine the Data Lakehouse pattern to realize macro-patterns, such as Data Mesh and Data Hub-Spoke, based on the organization's needs and maturity level. The frameworks introduced will be practical and organizations can readily benefit from their application.By the end of this book, you'll clearly understand how to implement the Data Lakehouse architecture pattern in a scalable, agile, and cost-effective manner.

Data Science Algorithms in a Week. Top 7 algorithms for scientific computing, data analysis, and machine learning - Second Edition

David Natingga

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well.Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis.By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem