Data Wrangling with R. Load, explore, transform and visualize data for modeling with tidyverse libraries

Gustavo R Santos

In this information era, where large volumes of data are being generated every day, companies want to get a better grip on it to perform more efficiently than before. This is where skillful data analysts and data scientists come into play, wrangling and exploring data to generate valuable business insights. In order to do that, you’ll need plenty of tools that enable you to extract the most useful knowledge from data.Data Wrangling with R will help you to gain a deep understanding of ways to wrangle and prepare datasets for exploration, analysis, and modeling. This data book enables you to get your data ready for more optimized analyses, develop your first data model, and perform effective data visualization.The book begins by teaching you how to load and explore datasets. Then, you’ll get to grips with the modern concepts and tools of data wrangling. As data wrangling and visualization are intrinsically connected, you’ll go over best practices to plot data and extract insights from it. The chapters are designed in a way to help you learn all about modeling, as you will go through the construction of a data science project from end to end, and become familiar with the built-in RStudio, including an application built with Shiny dashboards.By the end of this book, you’ll have learned how to create your first data model and build an application with Shiny in R.

Data Wrangling with SQL. A hands-on guide to manipulating, wrangling, and engineering data using SQL

Raghav Kandarpa, Shivangi Saxena

The amount of data generated continues to grow rapidly, making it increasingly important for businesses to be able to wrangle this data and understand it quickly and efficiently. Although data wrangling can be challenging, with the right tools and techniques you can efficiently handle enormous amounts of unstructured data.The book starts by introducing you to the basics of SQL, focusing on the core principles and techniques of data wrangling. You’ll then explore advanced SQL concepts like aggregate functions, window functions, CTEs, and subqueries that are very popular in the business world. The next set of chapters will walk you through different functions within SQL query that cause delays in data transformation and help you figure out the difference between a good query and bad one. You’ll also learn how data wrangling and data science go hand in hand. The book is filled with datasets and practical examples to help you understand the concepts thoroughly, along with best practices to guide you at every stage of data wrangling.By the end of this book, you’ll be equipped with essential techniques and best practices for data wrangling, and will predominantly learn how to use clean and standardized data models to make informed decisions, helping businesses avoid costly mistakes.

Database Design and Modeling with Google Cloud. Learn database design and development to take your data to applications, analytics, and AI

Abirami Sukumaran, Priyanka Vergadia, Bagirathi Narayanan

In the age of lightning-speed delivery, customers want everything developed, built, and delivered at high speed and at scale. Knowledge, design, and choice of database is critical in that journey, but there is no one-size-fits-all solution. This book serves as a comprehensive and practical guide for data professionals who want to design and model their databases efficiently. The book begins by taking you through business, technical, and design considerations for databases. Next, it takes you on an immersive structured database deep dive for both transactional and analytical real-world use cases using Cloud SQL, Spanner, and BigQuery. As you progress, you’ll explore semi-structured and unstructured database considerations with practical applications using Firestore, cloud storage, and more. You’ll also find insights into operational considerations for databases and the database design journey for taking your data to AI with Vertex AI APIs and generative AI examples. By the end of this book, you will be well-versed in designing and modeling data and databases for your applications using Google Cloud.

Database Design and Modeling with PostgreSQL and MySQL. Build efficient and scalable databases for modern applications using open source databases

Alkin Tezuysal, Ibrar Ahmed, Peter Zaitsev

Database Design and Modeling with PostgreSQL and MySQL will equip you with the knowledge and skills you need to architect, build, and optimize efficient databases using two of the most popular open-source platforms.As you progress through the chapters, you'll gain a deep understanding of data modeling, normalization, and query optimization, supported by hands-on exercises and real-world case studies that will reinforce your learning. You'll explore topics like concurrency control, backup and recovery strategies, and seamless integration with web and mobile applications. These advanced topics will empower you to tackle complex database challenges confidently and effectively. Additionally, you’ll explore emerging trends, such as NoSQL databases and cloud-based solutions, ensuring you're well-versed in the latest developments shaping the database landscape. By embracing these cutting-edge technologies, you'll be prepared to adapt and innovate in today's ever-evolving digital world.By the end of this book, you’ll be able to understand the technologies that exist to design a modern and scalable database for developing web applications using MySQL and PostgreSQL open-source databases.

Database Security. Master the Art of Protecting Your Data with Cutting-Edge Techniques

Mercury Learning and Information, Christopher Diaz

This book provides a comprehensive guide to resolving database security issues during design, implementation, and production phases. It emphasizes specific measures and controls unique to database security, beyond general information security. Topics include account credential management, data access management, and techniques like database normalization, referential integrity, transactions, locks, and check constraints.The importance of database security lies in protecting sensitive data from unauthorized access and ensuring data integrity. This book is designed for professionals, workshops, and self-learners, offering hands-on demonstrations with major Database Management Systems (MySQL, Oracle, and Microsoft SQL Server) across various computing platforms (Linux/UNIX, MacOS, Windows).Starting with an introduction to information, data, and database security, the book covers database design, management, administration, user accounts, privileges, roles, and security controls for confidentiality. It also delves into transactions and data integrity with concurrent access. Each chapter includes questions and projects to reinforce learning and comprehension.

Data-Centric Applications with Vaadin 8. Develop and maintain high-quality web applications using Vaadin

Alejandro Duarte

Vaadin is an open-source Java framework used to build modern user interfaces. Vaadin 8 simplifies application development and improves user experience. The book begins with an overview of the architecture of Vaadin applications and the way you can organize your code in modules.Then it moves to the more advanced topics about advanced topics such as internationalization, authentication, authorization, and database connectivity. The book also teaches you how to implement CRUD views, how to generate printable reports, and how to manage data with lazy loading.By the end of this book you will be able to architect, implement, and deploy stunning Vaadin applications, and have the knowledge to master web development with Vaadin.

Debugging Machine Learning Models with Python. Develop high-performance, low-bias, and explainable machine learning and deep learning models

Ali Madani, Stephen MacKinnon

Debugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-performance models for industrial applications. Whether you're a data scientist, analyst, machine learning engineer, or Python developer, this book will empower you to design modular systems for data preparation, accurately train and test models, and seamlessly integrate them into larger technologies.By bridging the gap between theory and practice, you'll learn how to evaluate model performance, identify and address issues, and harness recent advancements in deep learning and generative modeling using PyTorch and scikit-learn. Your journey to developing high quality models in practice will also encompass causal and human-in-the-loop modeling and machine learning explainability. With hands-on examples and clear explanations, you'll develop the skills to deliver impactful solutions across domains such as healthcare, finance, and e-commerce.

Deep Learning with Hadoop. Distributed Deep Learning with Large-Scale Data

Dipayan Dev

This book will teach you how to deploylarge-scale dataset in deep neural networks with Hadoop foroptimal performance.Starting with understanding what deeplearning is, and what the various modelsassociated with deep neural networks are, thisbook will then show you how to set up theHadoop environment for deep learning.In this book, you will also learn how toovercome the challenges that you facewhile implementing distributed deeplearning with large-scale unstructured datasets. The book willalso show you how you can implementand parallelize the widely used deep learning models such as Deep Belief Networks,Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann machines and autoencoder using the popular deep learning library Deeplearning4j.Get in-depth mathematical explanationsand visual representations to helpyou understand the design and implementationsof Recurrent Neural network and Denoising Autoencoders withDeeplearning4j. To give you a morepractical perspective, the book will alsoteach you the implementation of large-scale video processing, image processing andnatural language processing on Hadoop.By the end of this book, you willknow how to deploy various deep neural networks indistributed systems using Hadoop.