Regression Analysis with R. Design and develop statistical nodes to identify unique relationships within data at scale

Giuseppe Ciaburro

Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables.This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are – supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process – loading the training dataset, handling missing values, EDA on the dataset, transformations and feature engineering, model building, assessing the model fitting and performance, and finally making predictions on unseen datasets. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. The practical examples are illustrated using R code including the different packages in R such as R Stats, Caret and so on. Each chapter is a mix of theory and practical examples.By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects.

Ripple Quick Start Guide. Get started with XRP and develop applications on Ripple's blockchain

Febin John James

This book starts by giving you an understanding of the basics of blockchain and the Ripple protocol. You will then get some hands-on experience of working with XRP.You will learn how to set up a Ripple wallet and see how seamlessly you can transfer money abroad. You will learn about different types of wallets through which you can store and transact XRP, along with the security precautions you need to take to keep your money safe.Since Ripple is currency agnostic, it can enable the transfer of value in USD, EUR, and any other currency. You can even transfer digital assets using Ripple. You will see how you can pay an international merchant with their own native currency and how Ripple can exchange it on the ?y. Once you understand the applications of Ripple, you will learn how to create a conditionally-held escrow using the Ripple API, and how to send and cash checks.Finally, you will also understand the common misconceptions people have about Ripple and discover the potential risks you must consider before making investment decisions.By the end of this book, you will have a solid foundation for working with Ripple's blockchain. Using it, you will be able to solve problems caused by traditional systems in your respective industry.

SAS for Finance. Forecasting and data analysis techniques with real-world examples to build powerful financial models

Harish Gulati

SAS is a groundbreaking tool for advanced predictive and statistical analytics used by top banks and financial corporations to establish insights from their financial data.SAS for Finance offers you the opportunity to leverage the power of SAS analytics in redefining your data. Packed with real-world examples from leading financial institutions, the author discusses statistical models using time series data to resolve business issues.This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate financial models. You can easily assess the pros and cons of models to suit your unique business needs.By the end of this book, you will be able to leverage the true power of SAS to design and develop accurate analytical models to gain deeper insights into your financial data.

Scala for Machine Learning. Build systems for data processing, machine learning, and deep learning - Second Edition

Patrick R. Nicolas

The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies. The book is your one stop guide that introduces you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits. You start by learning data preprocessing and filtering techniques. Following this, you'll move on to unsupervised learning techniques such as clustering and dimension reduction, followed by probabilistic graphical models such as Naïve Bayes, hidden Markov models and Monte Carlo inference. Further, it covers the discriminative algorithms such as linear, logistic regression with regularization, kernelization, support vector machines, neural networks, and deep learning. You’ll move on to evolutionary computing, multibandit algorithms, and reinforcement learning.Finally, the book includes a comprehensive overview of parallel computing in Scala and Akka followed by a description of Apache Spark and its ML library. With updated codes based on the latest version of Scala and comprehensive examples, this book will ensure that you have more than just a solid fundamental knowledge in machine learning with Scala.

Scala Machine Learning Projects. Build real-world machine learning and deep learning projects with Scala

Md. Rezaul Karim

Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development.If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet.At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment.

Scalable Data Analytics with Azure Data Explorer. Modern ways to query, analyze, and perform real-time data analysis on large volumes of data

Jason Myerscough, Arunee Singhchawla

Azure Data Explorer (ADX) enables developers and data scientists to make data-driven business decisions. This book will help you rapidly explore and query your data at scale and secure your ADX clusters.The book begins by introducing you to ADX, its architecture, core features, and benefits. You'll learn how to securely deploy ADX instances and navigate through the ADX Web UI, cover data ingestion, and discover how to query and visualize your data using the powerful Kusto Query Language (KQL). Next, you'll get to grips with KQL operators and functions to efficiently query and explore your data, as well as perform time series analysis and search for anomalies and trends in your data. As you progress through the chapters, you'll explore advanced ADX topics, including deploying your ADX instances using Infrastructure as Code (IaC). The book also shows you how to manage your cluster performance and monthly ADX costs by handling cluster scaling and data retention periods. Finally, you'll understand how to secure your ADX environment by restricting access with best practices for improving your KQL query performance.By the end of this Azure book, you'll be able to securely deploy your own ADX instance, ingest data from multiple sources, rapidly query your data, and produce reports with KQL and Power BI.

Scaling Big Data with Hadoop and Solr. Understand, design, build, and optimize your big data search engine with Hadoop and Apache Solr

Hrishikesh Vijay Karambelkar

Seven NoSQL Databases in a Week. Get up and running with the fundamentals and functionalities of seven of the most popular NoSQL databases

Aaron Ploetz, Devram Kandhare, Sudarshan Kadambi, Xun (Brian) Wu

This is the golden age of open source NoSQL databases. With enterprises having to work with large amounts of unstructured data and moving away from expensive monolithic architecture, the adoption of NoSQL databases is rapidly increasing. Being familiar with the popular NoSQL databases and knowing how to use them is a must for budding DBAs and developers.This book introduces you to the different types of NoSQL databases and gets you started with seven of the most popular NoSQL databases used by enterprises today. We start off with a brief overview of what NoSQL databases are, followed by an explanation of why and when to use them. The book then covers the seven most popular databases in each of these categories: MongoDB, Amazon DynamoDB, Redis, HBase, Cassandra, In?uxDB, and Neo4j. The book doesn't go into too much detail about each database but teachesyou enough to get started with them.By the end of this book, you will have a thorough understanding of the different NoSQL databases and their functionalities, empowering you to select and use the rightdatabase according to your needs.