| Book | Brief | |
|---|---|---|
| This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard to do, if not impossible, using native Power BI tools without Power BI Premium capacity. For example, you will learn to score Power BI data using custom data science models, including powerful models from Microsoft Cognitive Services.
The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration, but become easier through the application of data wrangling features that languages such as R and Python support. If you are a BI developer, business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you to do that. |
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Saturday, August 29, 2020
Advanced Analytics in Power BI with R and Python... Ingesting, Transforming, Visualizing
Getting Started with Oracle Cloud Free Tier...Create Modern Web Applications
| Book | Brief | |
|---|---|---|
| Reading this book and creating your own application in the Free Tier is an excellent way to build familiarity with, and expertise in, Oracle Cloud Infrastructure. Even better is that the Free Tier by itself is capable enough and provides all the ingredients needed for you to create secure and robust, multi-tiered web applications of modest size. Examples in this book introduce the broad suite of Always Free options that are available from Oracle Cloud Infrastructure. You will learn how to provision autonomous databases and autonomous Linux compute nodes. And you will see how to use Terraform to manage infrastructure as code. You also will learn about the virtual cloud network and application deployment, including how to create and deploy public-facing Oracle Application Express solutions and three-tier web applications on a foundation of Oracle REST Data Services. You will have the knowledge and skills that you need to deploy modest applications along with a growing understanding of Oracle’s Cloud platform that will serve you well as you go beyond the limits of the Always Free options and take full advantage of all that Oracle Cloud Infrastructure can offer. |
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BigQuery for Data Warehousing...Managed Data Analysis
| Brief | Book | |
|---|---|---|
BigQuery is a managed cloud platform from Google that provides enterprise data warehousing and reporting capabilities.
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Demystifying Azure AI...Implementing the Right Features
| Brief | Book | |
|---|---|---|
| Explore artificial intelligence offerings by Microsoft Azure, along with its other services. This book will help you implement AI features in various Azure services to help build your organization and customers.
The book starts by introducing you to the Azure Cognitive Search service to create and use an application. You then will learn the built-in automatic tuning intelligence mechanism in Azure SQL Database. This is an important feature you can use to enable Azure SQL Database to optimize the performance of your queries. Next, you will go through AI services with Azure Integration Platform service and Azure Logic Apps to build a modern intelligent workflow in your application. Azure functions are discussed as a part of its server-less feature. The book concludes by teaching you how to work with Power Automate to analyze your business workflow. After reading this book, you will be able to understand and work with different Azure Cognitive Services in AI. |
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SQL Server 2019 Administrator's Guide...for DBAs to implement, monitor, and maintain enterprise...
| Brief | Book | |
|---|---|---|
| You’ll start by learning how to set up your SQL Server and configure new and existing environments for optimal use. The book then takes you through designing aspects and delves into performance tuning by showing you how to use indexes effectively. You’ll understand certain choices that need to be made about backups, implement security policy, and discover how to keep your environment healthy. Tools available for monitoring and managing a SQL Server database, including automating health reviews, performance checks, and much more, will also be discussed in detail. As you advance, the book covers essential topics such as migration, upgrading, and consolidation, along with the techniques that will help you when things go wrong. Once you’ve got to grips with integration with Azure and streamlining big data pipelines, you’ll learn best practices from industry experts for maintaining a highly reliable database solution. Whether you are an administrator or are looking to get started with database administration, this SQL Server book will help you develop the skills you need to successfully create, design, and deploy database solutions. |
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Database Design for Mere Mortals
| Brief | Book | |
|---|---|---|
| Step by step, this book shows you how to design databases that are soundly structured, reliable, and flexible, even in modern web applications. The author guides you through everything from database planning to defining tables, fields, keys, table relationships, business rules, and views. You’ll learn practical ways to improve data integrity, how to avoid common mistakes, and when to break the rules. This edition has been updated to reflect the current landscape for databases and their prevalent uses in the world. Coverage includes
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SQL Server 2019 Analysis Services...Learn to query tabular and multidimensional models
| Book | Brief | |
|---|---|---|
| This book will help you understand MS SQL Server 2019’s new features and improvements, especially when it comes to SSAS.
First, you’ll cover a quick overview of SQL Server 2019 and learn how to choose the right analytical model to use, along with understanding their key differences. You’ll get to grips with creating a multidimensional model with SSAS and expand on that model with MDX measures. Next, you’ll create and deploy a tabular model using Visual Studio and Management Studio. Later chapters show you when and how to use both tabular and multidimensional model types, how to deploy and configure your servers to support them, and design principles that are relevant to each model. The book even comes packed with tips and tricks to build measures, optimize your design, and interact with models using Excel and Power BI. All this will help you to visualize data to gain useful insights and make better decisions. Finally, you’ll discover practices and tools for securing and maintaining your models once they are deployed. By the end of this book, you’ll be able to choose the right model and build and deploy it to support the analytical needs of your business. |
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SQL for Data Science...Cleaning, Wrangling and Analytics with Relational Databases
| Brief | Book | |
|---|---|---|
| The book is organized as follows. Chapter 1 describes the data life cycle, i.e. the sequence of stages from data acquisition to archiving, that data goes through as it is prepared and then actually analyzed, together with the different activities that take place at each stage. Chapter 2 gets into databases proper, explaining how relational databases organize data. Non-traditional data, like XML and text, are also covered. Chapter 3 introduces SQL queries, but unlike traditional textbooks, queries and their parts are described around typical data analysis tasks like data exploration, cleaning and transformation. Chapter 4 introduces some basic techniques for data analysis and shows how SQL can be used for some simple analyses without too much complication. Chapter 5 introduces additional SQL constructs that are important in a variety of situations and thus completes the coverage of SQL queries. Lastly, chapter 6 briefly explains how to use SQL from within R and from within Python programs. It focuses on how these languages can interact with a database, and how what has been learned about SQL can be leveraged to make life easier when using R or Python. All chapters contain a lot of examples and exercises on the way, and readers are encouraged to install the two open-source database systems (MySQL and Postgres) that are used throughout the book in order to practice and work on the exercises, because simply reading the book is much less useful than actually using it. |
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SQL Cookbook...Query Solutions and Techniques...
| Brief | Book | |
|---|---|---|
| You may know SQL basics, but are you taking advantage of its expressive power? The new edition of this cookbook applies a highly practical approach to Structured Query Language (SQL) so you can create and manipulate large stores of data. Based on real-world examples, this updated book provides a framework to help you construct solutions and executable examples in several flavors of SQL—including
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Practical Azure SQL Database for Modern Developers...in the Cloud
| Book | Brief | |
|---|---|---|
| Here is the expert-level, insider guidance you need on using Azure SQL Database as your back-end data store. This book highlights best practices in everything ranging from full-stack projects to mobile applications to critical, back-end APIs. The book provides instruction on accessing your data from any language and platform. And you learn how to push processing-intensive work into the database engine to be near the data and avoid undue networking traffic. Azure SQL is explained from a developer's point of view, helping you master its feature set and create applications that perform well and delight users. Core to the book is showing you how Azure SQL Database provides relational and post-relational support so that any workload can be managed with easy accessibility from any platform and any language. You will learn about features ranging from lock-free tables to column store indexes, and about support for data formats ranging from JSON and key-values to the nodes and edges in the graph database paradigm. Reading this book prepares you to deal with almost all data management challenges, allowing you to create lean and specialized solutions having the elasticity and scalability that are needed in the modern world. |
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SQL Server 2019 AlwaysOn...Supporting 24x7 Applications
| Book | Brief | |
|---|---|---|
| This third edition provides a solid and accurate understanding of how to implement systems requiring consistent and continuous uptime, as well as how to troubleshoot those systems in order to keep them running and reliable. This edition is updated to account for all new major functionality and also includes coverage of implementing atypical configurations, such as clusterless and domain-independent Availability Groups, distributed Availability Groups, and implementing Availability Groups on Azure. The book begins with an introduction to high-availability and disaster recovery concepts such as Recovery Point Objectives (RPOs), Recovery Time Objectives (RTOs), availability levels, and the cost of downtime. You’ll then move into detailed coverage of implementing and configuring the AlwaysOn feature set in order to meet the business objectives set by your organization. Content includes coverage on implementing clusters, building AlwaysOn failover clustered instances, and configuring AlwaysOn Availability Groups. |
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Azure SQL Revealed...for SQL Server Professionals
| Brief | Book | |
|---|---|---|
| Access detailed content and examples on Azure SQL, a set of cloud services that allows for SQL Server to be deployed in the cloud. This book teaches the fundamentals of deployment, configuration, security, performance, and availability of Azure SQL from the perspective of these same tasks and capabilities in SQL Server. This distinct approach makes this book an ideal learning platform for readers familiar with SQL Server on-premises who want to migrate their skills toward providing cloud solutions to an enterprise market that is increasingly cloud-focused. If you know SQL Server, you will love this book. You will be able to take your existing knowledge of SQL Server and translate that knowledge into the world of cloud services from the Microsoft Azure platform, and in particular into Azure SQL. This book provides information never seen before about the history and architecture of Azure SQL. Author Bob Ward is a leading expert with access to and support from the Microsoft engineering team that built Azure SQL and related database cloud services. He presents powerful, behind-the-scenes insights into the workings of one of the most popular database cloud services in the industry. |
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Mastering Kafka Streams and ksqlDB... Building Real-Time Data Systems
| Brief | Book | |
|---|---|---|
| With Kafka Streams and ksqlDB, building stream processing applications is easy and fun. This practical guide explores the world of real-time data systems through the lens of these popular technologies and explains important stream processing concepts against a backdrop of interesting business problems.
The author introduces you to both Kafka Streams and ksqlDB so that you can choose the best tool for each unique stream processing project. In this book, you’ll learn:
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Sunday, August 23, 2020
Machine Learning with ML.NET... popular machine learning algorithms in C#
| Brief | Book | |
|---|---|---|
| The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You'll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You'll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You'll also learn to integrate TensorFlow in ML.NET applications. Later you'll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR. |
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Python Feature Engineering Cookbook...Over 70 recipes...
| Brief | Book | |
|---|---|---|
| Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code.
Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you'll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You'll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you'll have discovered tips and practical solutions to all of your feature engineering problems. |
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Big Data and Machine Learning in Quantitative Investment
| Brief | Book | |
|---|---|---|
| Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the math or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance.
The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning.
Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment ― and this book shows you how. |
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Data Mining Techniques...Customer Relationship Management
| Book | Brief | |
|---|---|---|
| This new edition--more than 50% new and revised-- is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results. |
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Applied Text Analysis with Python... with Machine Learning
| Brief | Book | |
|---|---|---|
| From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. |
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Data Mining...The Textbook
| Brief | Book | |
|---|---|---|
| This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories:
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Introduction to Data Mining 1st Edition
| Book | Brief | |
|---|---|---|
| Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. |
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Deep Learning with TensorFlow 2 and Keras...Regression, GANs, RNNs, NLP, API
| Brief | Book | |
|---|---|---|
| Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.
TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. |
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Automated Machine Learning...Methods, Systems, Challenges
| Brief | Book | |
|---|---|---|
| This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. |
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