Take a look at our Data Modeling & Design books. Shulph carries a great selection of Data Modeling & Design books, and we are always adding more.
Leverage the power of Tableau 2019.1's new features to create impactful data visualization Key Features Get up and running with the newly released features of Tableau 2019.1 Create enterprise-grade dashboard and reports to communicate your insights effectively Begin your Tableau journey by understanding its core functionalities Book Description Tableau is one of the leading data visualization tools and is regularly updated with new functionalities and features. The latest release, Tableau 2019.1, promises new and advanced features related to visual analytics, reporting, dashboarding, and a host of other data visualization aspects. Getting Started with Tableau 2019.1 will get you up to speed with these additional functionalities. The book starts by highlighting the new functionalities of Tableau 2019.1, providing concrete examples of how to use them. However, if you're new to Tableau, don't worry – you'll be guided through the major aspects of Tableau with relevant examples. You'll learn how to connect to data, build a data source, visualize your data, build a dashboard, and even share data online. In the concluding chapters, you'll delve into advanced techniques such as creating a cross-database join and data blending. By the end of this book, you will be able to use Tableau effectively to create quick, cost-effective, and business-efficient Business Intelligence (BI) solutions. What you will learn Discover new functionalities such as 'Ask Data', the new way to interact with your data using natural language Connect tables and make transformations such as pivoting the field and splitting columns Build an efficient data source for analysis Design insightful data visualization using different mark types and properties Develop powerful dashboards and stories Share your work and interact with Tableau Server Use Tableau to explore your data and find new insights Explore Tableau's advanced features and gear up for upcoming challenges Who this book is for Existing Tableau users and BI professionals who want to get up to speed with what's new in Tableau 2019 will find this beginner-level book to be a very useful resource. Some experience of Tableau is assumed, however, the book also features introductory concepts, which even beginners can take advantage of.
Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs Key Features Work with large amounts of agile data using distributed datasets and in-memory caching Source data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3 Employ the easy-to-use PySpark API to deploy big data Analytics for production Book Description Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively. What you will learn Get practical big data experience while working on messy datasets Analyze patterns with Spark SQL to improve your business intelligence Use PySpark's interactive shell to speed up development time Create highly concurrent Spark programs by leveraging immutability Discover ways to avoid the most expensive operation in the Spark API: the shuffle operation Re-design your jobs to use reduceByKey instead of groupBy Create robust processing pipelines by testing Apache Spark jobs Who this book is for This book is for developers, data scientists, business analysts, or anyone who needs to reliably analyze large amounts of large-scale, real-world data. Whether you're tasked with creating your company's business intelligence function or creating great data platforms for your machine learning models, or are looking to use code to magnify the impact of your business, this book is for you.
Solve all big data problems by learning how to create efficient data models Key Features Create effective models that get the most out of big data Apply your knowledge to datasets from Twitter and weather data to learn big data Tackle different data modeling challenges with expert techniques presented in this book Book Description Modeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements. To start with, you'll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you'll work with structured and semi-structured data with the help of real-life examples. Once you've got to grips with the basics, you'll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You'll also learn to create graph data models and explore data modeling with streaming data using real-world datasets. By the end of this book, you'll be able to design and develop efficient data models for varying data sizes easily and efficiently. What you will learn Get insights into big data and discover various data models Explore conceptual, logical, and big data models Understand how to model data containing different file types Run through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modeling Create data models such as Graph Data and Vector Space Model structured and unstructured data using Python and R Who this book is for This book is great for programmers, geologists, biologists, and every professional who deals with spatial data. If you want to learn how to handle GIS, GPS, and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful.
Build an Ethereum gaming application from scratch in a span of seven days, by mastering smart contracts in Solidity Key Features Create a simple, functional decentralized application, on the Ethereum network Learn fundamental blockchain programming concepts to become a blockchain developer Understand the development life cycle of a blockchain application Book Description Blockchain is a revolutionary technology that is currently been used in a variety of industrial applications, such as finance, healthcare, data analytics, and much more. This book will teach you the key blockchain principles and methodologies that are required to build decentralized applications in just 7 days. This book will teach you to build an online gaming application using Ethereum. Each section will introduce fundamental blockchain programming concepts as they relate to creating an online game, followed by practical exercises that readers can implement as homework assignments. With this book, you will learn core blockchain application development skills, create smart contracts, and build user interfaces. You will not only learn how to interact with the Ethereum network, but also how to deploy your application to the Internet. This book supplies seven self-contained lessons taught in a practical, hands-on way. By the end of the book, you will be amazed at how much you have learned about the blockchain application development on the Ethereum network, in just one week! What you will learn Work with blockchain networks to create interactive applications Learn how to create and use variables in smart contracts Use automated tests to eliminate mistakes and errors in the code Interact with the Ethereum network from a user interface Build a user interface for smart contracts using React Send and receive funds in smart contracts using wallets Deploy blockchain applications on AWS Who this book is for This book is for software engineers and IT professionals, who are eager to learn blockchain application development skills and want to master how blockchain applications are developed. This book is perfect for those with limited programming experience.
Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges. Key Features Learn the fundamentals of Convolutional Neural Networks Harness Python and Tensorflow to train CNNs Build scalable deep learning models that can process millions of items Book Description Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learn Train machine learning models with TensorFlow Create systems that can evolve and scale during their life cycle Use CNNs in image recognition and classification Use TensorFlow for building deep learning models Train popular deep learning models Fine-tune a neural network to improve the quality of results with transfer learning Build TensorFlow models that can scale to large datasets and systems Who this book is for This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.
A step-by-step approach to building stunning dashboards with QlikView Key Features Perform effective storytelling through interactive dashboards built with QlikView Create different types of visualizations from a variety of data sources Includes tips, tricks, and best practices to perform effective Business Intelligence using QlikView Book Description QlikView is one of the market leaders when it comes to building effective Business Intelligence solutions. This book will show how you can leverage its power to build your own dashboards to tell your own data story. The book starts with showing you how to connect your data to QlikView and create your own QlikView application. You will learn how to add data from multiple sources, create a data model by joining data, and then review it on the front end. You will work with QlikView components such as charts, list boxes, input boxes, and text objects to create stunning visualizations that help give actionable business insights. You will also learn how to perform analysis on your data in QlikView and master the various types of security measures to be taken in QlikView. By the end of this book, you will have all the essential knowledge required for insightful data storytelling and creating useful BI dashboards using QlikView. What you will learn Learn to use the latest and newest features of QlikView Connect QlikView to various data sources, such as databases and websites Create a fully featured data model without circular references Display your data in maps, charts, and text across multiple sheets Apply set analysis to your data in QlikView expressions Secure your data based on the various audience types Who this book is for This book is best suited for BI professionals, data analysts and budding QlikView developers who wish to build effective dashboards using QlikView. Some basic understanding of the data visualization concepts and Business Intelligence is required.
A hands-on guide for professionals to perform various data science tasks in R Key Features Explore the popular R packages for data science Use R for efficient data mining, text analytics and feature engineering Become a thorough data science professional with the help of hands-on examples and use-cases in R Book Description R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity. What you will learn Understand the R programming language and its ecosystem of packages for data science Obtain and clean your data before processing Master essential exploratory techniques for summarizing data Examine various machine learning prediction, models Explore the H2O analytics platform in R for deep learning Apply data mining techniques to available datasets Work with interactive visualization packages in R Integrate R with Spark and Hadoop for large-scale data analytics Who this book is for If you are a budding data scientist keen to learn about the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course
Big data processing and analytics at speed and scale using command line tools. Key Features Perform string processing, numerical computations, and more using CLI tools Understand the essential components of data science development workflow Automate data pipeline scripts and visualization with the command line Book Description The Command Line has been in existence on UNIX-based OSes in the form of Bash shell for over 3 decades. However, very little is known to developers as to how command-line tools can be OSEMN (pronounced as awesome and standing for Obtaining, Scrubbing, Exploring, Modeling, and iNterpreting data) for carrying out simple-to-advanced data science tasks at speed. This book will start with the requisite concepts and installation steps for carrying out data science tasks using the command line. You will learn to create a data pipeline to solve the problem of working with small-to medium-sized files on a single machine. You will understand the power of the command line, learn how to edit files using a text-based and an. You will not only learn how to automate jobs and scripts, but also learn how to visualize data using the command line. By the end of this book, you will learn how to speed up the process and perform automated tasks using command-line tools. What you will learn Understand how to set up the command line for data science Use AWK programming language commands to search quickly in large datasets. Work with files and APIs using the command line Share and collect data with CLI tools Perform visualization with commands and functions Uncover machine-level programming practices with a modern approach to data science Who this book is for This book is for data scientists and data analysts with little to no knowledge of the command line but has an understanding of data science. Perform everyday data science tasks using the power of command line tools.
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems Key Features Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python Master the art of data-driven problem-solving with hands-on examples Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms Book Description Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You'll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you'll gain a thorough understanding of its theory and learn when to apply it. As you advance, you'll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms. By the end of this machine learning book, you'll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You'll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production. What you will learn Understand when to use supervised, unsupervised, or reinforcement learning algorithms Find out how to collect and prepare your data for machine learning tasks Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff Apply supervised and unsupervised algorithms to overcome various machine learning challenges Employ best practices for tuning your algorithm's hyper parameters Discover how to use neural networks for classification and regression Build, evaluate, and deploy your machine learning solutions to production Who this book is for This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.
Create and unleash the power of neural networks by implementing C# and .Net code Key Features Get a strong foundation of neural networks with access to various machine learning and deep learning libraries Real-world case studies illustrating various neural network techniques and architectures used by practitioners Cutting-edge coverage of Deep Networks, optimization algorithms, convolutional networks, autoencoders and many more Book Description Neural networks have made a surprise comeback in the last few years and have brought tremendous innovation in the world of artificial intelligence. The goal of this book is to provide C# programmers with practical guidance in solving complex computational challenges using neural networks and C# libraries such as CNTK, and TensorFlowSharp. This book will take you on a step-by-step practical journey, covering everything from the mathematical and theoretical aspects of neural networks, to building your own deep neural networks into your applications with the C# and .NET frameworks. This book begins by giving you a quick refresher of neural networks. You will learn how to build a neural network from scratch using packages such as Encog, Aforge, and Accord. You will learn about various concepts and techniques, such as deep networks, perceptrons, optimization algorithms, convolutional networks, and autoencoders. You will learn ways to add intelligent features to your .NET apps, such as facial and motion detection, object detection and labeling, language understanding, knowledge, and intelligent search. Throughout this book, you will be working on interesting demonstrations that will make it easier to implement complex neural networks in your enterprise applications. What you will learn Understand perceptrons and how to implement them in C# Learn how to train and visualize a neural network using cognitive services Perform image recognition for detecting and labeling objects using C# and TensorFlowSharp Detect specific image characteristics such as a face using Accord.Net Demonstrate particle swarm optimization using a simple XOR problem and Encog Train convolutional neural networks using ConvNetSharp Find optimal parameters for your neural network functions using numeric and heuristic optimization techniques. Who this book is for This book is for Machine Learning Engineers, Data Scientists, Deep Learning Aspirants and Data Analysts who are now looking to move into advanced machine learning and deep learning with C#. Prior knowledge of machine learning and working experience with C# programming is required to take most out of this book