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 DescriptionApache 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 forThis 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.

Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills Key Features Speed up your data analysis projects using powerful R packages and techniques Create multiple hands-on data analysis projects using real-world data Discover and practice graphical exploratory analysis techniques across domains Book DescriptionHands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You'll also uncover the structure of your data, and you'll learn graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process-data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context. What you will learn Learn powerful R techniques to speed up your data analysis projects Import, clean, and explore data using powerful R packages Practice graphical exploratory analysis techniques Create informative data analysis reports using ggplot2 Identify and clean missing and erroneous data Explore data analysis techniques to analyze multi-factor datasets Who this book is forHands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis.

Make sense of your data and predict the unpredictable About This Book * A unique book that centers around develop six key practical skills needed to develop and implement predictive analytics * Apply the principles and techniques of predictive analytics to effectively interpret big data * Solve real-world analytical problems with the help of practical case studies and real-world scenarios taken from the world of healthcare, marketing, and other business domains Who This Book Is For This book is for those with a mathematical/statistics background who wish to understand the concepts, techniques, and implementation of predictive analytics to resolve complex analytical issues. Basic familiarity with a programming language of R is expected. What You Will Learn * Master the core predictive analytics algorithm which are used today in business * Learn to implement the six steps for a successful analytics project * Classify the right algorithm for your requirements * Use and apply predictive analytics to research problems in healthcare * Implement predictive analytics to retain and acquire your customers * Use text mining to understand unstructured data * Develop models on your own PC or in Spark/Hadoop environments * Implement predictive analytics products for customers In Detail This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data. Style and Approach This book takes a practical hands-on approach wherein the algorithms will be explained with the help of real-world use cases. It is written in a well-researched academic style which is a great mix of theoretical and practical information. Code examples are supplied for both theoretical concepts as well as for the case studies. Key references and summaries will be provided at the end of each chapter so that you can explore those topics on their own.

Leverage the power of Python and statistical modeling techniques for building accurate predictive models Key Features Get introduced to Python's rich suite of libraries for statistical modeling Implement regression, clustering and train neural networks from scratch Includes real-world examples on training end-to-end machine learning systems in Python Book DescriptionPython's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book takes you through an exciting journey, of using these libraries to implement effective statistical models for predictive analytics. You'll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will look at supervised learning, where you will explore the principles of machine learning and train different machine learning models from scratch. You will also work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. You will also learn how neural networks can be trained and deployed for more accurate predictions, and which Python libraries can be used to implement them. By the end of this book, you will have all the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics. What you will learn Understand the importance of statistical modeling Learn about the various Python packages for statistical analysis Implement algorithms such as Naive Bayes, random forests, and more Build predictive models from scratch using Python's scikit-learn library Implement regression analysis and clustering Learn how to train a neural network in Python Who this book is forIf you are a data scientist, a statistician or a machine learning developer looking to train and deploy effective machine learning models using popular statistical techniques, then this book is for you. Knowledge of Python programming is required to get the most out of this book.

Search, analyze, and manage data effectively with Elasticsearch 7 Key Features Extend Elasticsearch functionalities and learn how to deploy on Elastic Cloud Deploy and manage simple Elasticsearch nodes as well as complex cluster topologies Explore the capabilities of Elasticsearch 7 with easy-to-follow recipes Book DescriptionElasticsearch is a Lucene-based distributed search server that allows users to index and search unstructured content with petabytes of data. With this book, you'll be guided through comprehensive recipes on what's new in Elasticsearch 7, and see how to create and run complex queries and analytics. Packed with recipes on performing index mapping, aggregation, and scripting using Elasticsearch, this fourth edition of Elasticsearch Cookbook will get you acquainted with numerous solutions and quick techniques for performing both every day and uncommon tasks such as deploying Elasticsearch nodes, integrating other tools to Elasticsearch, and creating different visualizations. You will install Kibana to monitor a cluster and also extend it using a variety of plugins. Finally, you will integrate your Java, Scala, Python, and big data applications such as Apache Spark and Pig with Elasticsearch, and create efficient data applications powered by enhanced functionalities and custom plugins. By the end of this book, you will have gained in-depth knowledge of implementing Elasticsearch architecture, and you'll be able to manage, search, and store data efficiently and effectively using Elasticsearch. What you will learn Create an efficient architecture with Elasticsearch Optimize search results by executing analytics aggregations Build complex queries by managing indices and documents Monitor the performance of your cluster and nodes Design advanced mapping to take full control of index steps Integrate Elasticsearch in Java, Scala, Python, and big data applications Install Kibana to monitor clusters and extend it for plugins Who this book is forIf you're a software engineer, big data infrastructure engineer, or Elasticsearch developer, you'll find this book useful. This Elasticsearch book will also help data professionals working in the e-commerce and FMCG industry who use Elastic for metrics evaluation and search analytics to get deeper insights for better business decisions. Prior experience with Elasticsearch will help you get the most out of this book.

A practical guide to administer, monitor and replicate your PostgreSQL 11 database Key Features Study and apply the newly introduced features in PostgreSQL 11 Tackle any problem in PostgreSQL 11 administration and management Catch up on expert techniques for monitoring, fine-tuning, and securing your database Book DescriptionPostgreSQL is a powerful, open source database management system with an enviable reputation for high performance and stability. With many new features in its arsenal, PostgreSQL 11 allows you to scale up your PostgreSQL infrastructure. This book takes a step-by-step, recipe-based approach to effective PostgreSQL administration. The book will introduce you to new features such as logical replication, native table partitioning, additional query parallelism, and much more to help you to understand and control, crash recovery and plan backups. You will learn how to tackle a variety of problems and pain points for any database administrator such as creating tables, managing views, improving performance, and securing your database. As you make steady progress, the book will draw attention to important topics such as monitoring roles, backup, and recovery of your PostgreSQL 11 database to help you understand roles and produce a summary of log files, ensuring high availability, concurrency, and replication. By the end of this book, you will have the necessary knowledge to manage your PostgreSQL 11 database efficiently. What you will learn Troubleshoot open source PostgreSQL version 11 on various platforms Deploy best practices for planning and designing live databases Select and implement robust backup and recovery techniques in PostgreSQL 11 Use pgAdmin or OmniDB to perform database administrator (DBA) tasks Adopt efficient replication and high availability techniques in PostgreSQL Improve the performance of your PostgreSQL solution Who this book is forThis book is designed for database administrators, data architects, database developers, or anyone with an interest in planning and running live production databases using PostgreSQL 11. It is also ideal if you're looking for hands-on solutions to any problem associated with PostgreSQL 11 administration. Some experience with handling PostgreSQL databases will be beneficial

This book documents recent attempts to conduct systematic, prodigious and multidisciplinary research in learning analytics and present their findings and identify areas for further research and development. The book also unveils the distinguished and exemplary works by educators and researchers in the field highlighting the current trends, privacy and ethical issues, creative and unique approaches, innovative methods, frameworks, and theoretical and practical aspects of learning analytics.

Build a strong foundation of machine learning algorithms in 7 days Key Features Use Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a week Know when and where to apply data science algorithms using this guide Book DescriptionMachine 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 What you will learn Understand how to identify a data science problem correctly Implement well-known machine learning algorithms efficiently using Python Classify your datasets using Naive Bayes, decision trees, and random forest with accuracy Devise an appropriate prediction solution using regression Work with time series data to identify relevant data events and trends Cluster your data using the k-means algorithm Who this book is forThis book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You'll also find this book useful if you're currently working with data science algorithms in some capacity and want to expand your skill set

Put your Haskell skills to work and generate publication-ready visualizations in no time at all Key Features Take your data analysis skills to the next level using the power of Haskell Understand regression analysis, perform multivariate regression, and untangle different cluster varieties Create publication-ready visualizations of data Book DescriptionEvery business and organization that collects data is capable of tapping into its own data to gain insights how to improve. Haskell is a purely functional and lazy programming language, well-suited to handling large data analysis problems. This book will take you through the more difficult problems of data analysis in a hands-on manner. This book will help you get up-to-speed with the basics of data analysis and approaches in the Haskell language. You'll learn about statistical computing, file formats (CSV and SQLite3), descriptive statistics, charts, and progress to more advanced concepts such as understanding the importance of normal distribution. While mathematics is a big part of data analysis, we've tried to keep this course simple and approachable so that you can apply what you learn to the real world. By the end of this book, you will have a thorough understanding of data analysis, and the different ways of analyzing data. You will have a mastery of all the tools and techniques in Haskell for effective data analysis. What you will learn Learn to parse a CSV file and read data into the Haskell environment Create Haskell functions for common descriptive statistics functions Create an SQLite3 database using an existing CSV file Learn the versatility of SELECT queries for slicing data into smaller chunks Apply regular expressions in large-scale datasets using both CSV and SQLite3 files Create a Kernel Density Estimator visualization using normal distribution Who this book is forThis book is intended for people who wish to expand their knowledge of statistics and data analysis via real-world examples. A basic understanding of the Haskell language is expected. If you are feeling brave, you can jump right into the functional programming style.

A complete guide to understanding, developing, and testing popular security-token smart contracts Key Features Understand key Blockchain and Ethereum platforms concepts Step-by-step guide to developing STO smart contracts on Ethereum Monetize digital tokens under various U.S. securities laws Book DescriptionThe failure of initial coin offerings (ICOs) is no accident, as most ICOs do not link to a real asset and are not regulated. Realizing the shortcomings of ICOs, the blockchain community and potential investors embraced security token offerings (STOs) and stablecoins enthusiastically. In this book, we start with an overview of the blockchain technology along with its basic concepts. We introduce the concept behind STO, and cover the basic requirements for launching a STO and the relevant regulations governing its issuance. We discuss U.S. securities laws development in launching security digital tokens using blockchain technology and show some real use cases. We also explore the process of STO launches and legal considerations. We introduce popular security tokens in the current blockchain space and talk about how to develop a security token DApp, including smart contract development for ERC1404 tokens. Later, you'll learn to build frontend side functionalities to interact with smart contracts. Finally, we discuss stablecoin technical design functionalities for issuing and operating STO tokens by interacting with Ethereum smart contracts. By the end of this book, you will have learned more about STOs and gained a detailed knowledge of building relevant applications-all with the help of practical examples. What you will learn Understand the basic requirements for launching a security token offering Explore various US securities laws governing the offering of security digital tokens Get to grips with the stablecoin concept with the help of use cases Learn how to develop security token decentralized applications Understand the difference between ERC-20 and ERC-721 tokens Learn how to set up a development environment and build security tokens Explore the technical design of stablecoins Who this book is forThis book is ideal for blockchain beginners and business user developers who want to quickly master popular Security Token Offerings and stablecoins. Readers will learn how to develop blockchain/digital cryptos, guided by U.S. securities laws and utilizing some real use cases. Prior exposure to an Object-Oriented Programming language such as JavaScript would be an advantage, but is not mandatory.

Implement real-world decentralized applications using Python, Vyper, Populus, and Ethereum Key Features Stay up-to-date with everything you need to know about the blockchain ecosystem Implement smart contracts, wallets, and decentralized applications(DApps) using Python libraries Get deeper insights into storing content in a distributed storage platform Book DescriptionBlockchain is seen as the main technological solution that works as a public ledger for all cryptocurrency transactions. This book serves as a practical guide to developing a full-fledged decentralized application with Python to interact with the various building blocks of blockchain applications. Hands-On Blockchain for Python Developers starts by demonstrating how blockchain technology and cryptocurrency hashing works. You will understand the fundamentals and benefits of smart contracts such as censorship resistance and transaction accuracy. As you steadily progress, you'll go on to build smart contracts using Vyper, which has a similar syntax to Python. This experience will further help you unravel the other benefits of smart contracts, including reliable storage and backup, and efficiency. You'll also use web3.py to interact with smart contracts and leverage the power of both the web3.py and Populus framework to build decentralized applications that offer security and seamless integration with cryptocurrencies. As you explore later chapters, you'll learn how to create your own token on top of Ethereum and build a cryptocurrency wallet graphical user interface (GUI) that can handle Ethereum and Ethereum Request for Comments (ERC-20) tokens using the PySide2 library. This will enable users to seamlessly store, send, and receive digital money. Toward the end, you'll implement InterPlanetary File System (IPFS) technology in your decentralized application to provide a peer-to-peer filesystem that can store and expose media. By the end of this book, you'll be well-versed in blockchain programming and be able to build end-to-end decentralized applications on a range of domains using Python. What you will learn Understand blockchain technology and what makes it an immutable database Use the features of web3.py API to interact with the smart contract Create your own cryptocurrency and token in Ethereum using Vyper Use IPFS features to store content on the decentralized storage platform Implement a Twitter-like decentralized application with a desktop frontend Build decentralized applications in the shape of console, web, and desktop applications Who this book is forIf you are a Python developer who wants to enter the world of blockchain, Hands-On Blockchain for Python Developers is for you. The book will be your go-to guide to becoming well-versed with the blockchain ecosystem and building your own decentralized applications using Python and library support.

A quick start guide to visualize your Elasticsearch data Key Features Your hands-on guide to visualizing the Elasticsearch data as well as navigating the Elastic stack Work with different Kibana plugins and create effective machine learning jobs using Kibana Build effective dashboards and reports without any hassle Book DescriptionThe Elastic Stack is growing rapidly and, day by day, additional tools are being added to make it more effective. This book endeavors to explain all the important aspects of Kibana, which is essential for utilizing its full potential. This book covers the core concepts of Kibana, with chapters set out in a coherent manner so that readers can advance their learning in a step-by-step manner. The focus is on a practical approach, thereby enabling the reader to apply those examples in real time for a better understanding of the concepts and to provide them with the correct skills in relation to the tool. With its succinct explanations, it is quite easy for a reader to use this book as a reference guide for learning basic to advanced implementations of Kibana. The practical examples, such as the creation of Kibana dashboards from CSV data, application RDBMS data, system metrics data, log file data, APM agents, and search results, can provide readers with a number of different drop-off points from where they can fetch any type of data into Kibana for the purpose of analysis or dashboarding. What you will learn Explore how Logstash is configured to fetch CSV data Understand how to create index patterns in Kibana Become familiar with how to apply filters on data Discover how to create ML jobs Explore how to analyze APM data from APM agents Get to grips with how to save, share, inspect, and edit visualizations Understand how to find an anomaly in data Who this book is forKibana 7 Quick Start Guide is for developers new to Kibana who want to learn the fundamentals of using the tool for visualization, as well as existing Elastic developers.

This book documents recent attempts to conduct systematic, prodigious and multidisciplinary research in learning analytics and present their findings and identify areas for further research and development. The book also unveils the distinguished and exemplary works by educators and researchers in the field highlighting the current trends, privacy and ethical issues, creative and unique approaches, innovative methods, frameworks, and theoretical and practical aspects of learning analytics.

Data is constantly increasing and data analysts are in higher demand than ever. This book is an essential guide to the role of data analyst. Aspiring data analysts will discover what data analysts do all day, what skills they will need for the role, and what regulations they will be required to adhere to. Practising data analysts can explore useful data analysis tools, methods and techniques, brush up on best practices and look at how they can advance their career.

Designing and deploying solutions using the SAP BusinessObjects Business Intelligence platform 4.2. Key Features Get up and running with the SAP BusinessObjects Business Intelligence platform Perform effective data analysis and visualization for actionable insights Enhance your BI strategy by creating different types of reports and dashboards using SAP BusinessObjects Book DescriptionThe SAP BusinessObjects Business Intelligence platform is a powerful reporting and analysis tool. This book is the ideal introduction to the SAP BusinessObjects Business Intelligence platform, introducing you to its data visualization, visual analytics, reporting, and dashboarding capabilities. The book starts with an overview of the BI platform and various data sources for reporting. Then, we move on to looking at data visualization, analysis, reporting, and analytics using BusinessObjects Business Intelligence tools. You will learn about the features associated with reporting, scheduling, and distribution and learn how to deploy the platform. Toward the end, you will learn about the strategies and factors that should be considered during deployment. By the end, you will be confident working with the SAP BusinessObjects Business Intelligence platform to deliver better insights for more effective decision making. What you will learn Work with various tools to create interactive data visualization and analysis Query, report, and analyze with SAP Business Objects Web Intelligence Create a report in SAP Crystal Reports for Enterprise Visualize and manipulate data using an SAP Lumira Storyboard Deep dive into the workings of the SAP predictive analytics tool Deploy and configure SAP BO Intelligence platform 4.2 Who this book is forThis book is for Business Intelligence professionals and existing SAP ecosystem users who want to perform effective Business Intelligence using SAP BusinessObjects.

Integrate open source data analytics and build business intelligence on SQL databases with Apache Superset. The quick, intuitive nature for data visualization in a web application makes it easy for creating interactive dashboards. Key Features Work with Apache Superset's rich set of data visualizations Create interactive dashboards and data storytelling Easily explore data Book DescriptionApache Superset is a modern, open source, enterprise-ready business intelligence (BI) web application. With the help of this book, you will see how Superset integrates with popular databases like Postgres, Google BigQuery, Snowflake, and MySQL. You will learn to create real time data visualizations and dashboards on modern web browsers for your organization using Superset. First, we look at the fundamentals of Superset, and then get it up and running. You'll go through the requisite installation, configuration, and deployment. Then, we will discuss different columnar data types, analytics, and the visualizations available. You'll also see the security tools available to the administrator to keep your data safe. You will learn how to visualize relationships as graphs instead of coordinates on plain orthogonal axes. This will help you when you upload your own entity relationship dataset and analyze the dataset in new, different ways. You will also see how to analyze geographical regions by working with location data. Finally, we cover a set of tutorials on dashboard designs frequently used by analysts, business intelligence professionals, and developers. What you will learn Get to grips with the fundamentals of data exploration using Superset Set up a working instance of Superset on cloud services like Google Compute Engine Integrate Superset with SQL databases Build dashboards with Superset Calculate statistics in Superset for numerical, categorical, or text data Understand visualization techniques, filtering, and grouping by aggregation Manage user roles and permissions in Superset Work with SQL Lab Who this book is forThis book is for data analysts, BI professionals, and developers who want to learn Apache Superset. If you want to create interactive dashboards from SQL databases, this book is what you need. Working knowledge of Python will be an advantage but not necessary to understand this book.

Learn how some Go-specific language features help to simplify building web scrapers along with common pitfalls and best practices regarding web scraping. Key Features Use Go libraries like Goquery and Colly to scrape the web Common pitfalls and best practices to effectively scrape and crawl Learn how to scrape using the Go concurrency model Book DescriptionWeb scraping is the process of extracting information from the web using various tools that perform scraping and crawling. Go is emerging as the language of choice for scraping using a variety of libraries. This book will quickly explain to you, how to scrape data data from various websites using Go libraries such as Colly and Goquery. The book starts with an introduction to the use cases of building a web scraper and the main features of the Go programming language, along with setting up a Go environment. It then moves on to HTTP requests and responses and talks about how Go handles them. You will also learn about a number of basic web scraping etiquettes. You will be taught how to navigate through a website, using a breadth-first and then a depth-first search, as well as find and follow links. You will get to know about the ways to track history in order to avoid loops and to protect your web scraper using proxies. Finally the book will cover the Go concurrency model, and how to run scrapers in parallel, along with large-scale distributed web scraping. What you will learn Implement Cache-Control to avoid unnecessary network calls Coordinate concurrent scrapers Design a custom, larger-scale scraping system Scrape basic HTML pages with Colly and JavaScript pages with chromedp Discover how to search using the "strings" and "regexp" packages Set up a Go development environment Retrieve information from an HTML document Protect your web scraper from being blocked by using proxies Control web browsers to scrape JavaScript sites Who this book is forData scientists, and web developers with a basic knowledge of Golang wanting to collect web data and analyze them for effective reporting and visualization.

From data to actionable business insights using Amazon QuickSight! About This Book * A practical hands-on guide to improving your business with the power of BI and Quicksight * Immerse yourself with an end-to-end journey for effective analytics using QuickSight and related services * Packed with real-world examples with Solution Architectures needed for a cloud-powered Business Intelligence service Who This Book Is For This book is for Business Intelligence architects, BI developers, Big Data architects, and IT executives who are looking to modernize their business intelligence architecture and deliver a fast, easy-to-use, cloud powered business intelligence service. What You Will Learn * Steps to test drive QuickSight and see how it fits in AWS big data eco system * Load data from various sources such as S3, RDS, Redshift, Athena, and SalesForce and visualize using QuickSight * Understand how to prepare data using QuickSight without the need of an IT developer * Build interactive charts, reports, dashboards, and storyboards using QuickSight * Access QuickSight using the mobile application * Architect and design for AWS Data Lake Solution, leveraging AWS hosted services * Build a big data project with step-by-step instructions for data collection, cataloguing, and analysis * Secure your data used for QuickSight from S3, RedShift, and RDS instances * Manage users, access controls, and SPICE capacity In Detail Amazon QuickSight is the next-generation Business Intelligence (BI) cloud service that can help you build interactive visualizations on top of various data sources hosted on Amazon Cloud Infrastructure. QuickSight delivers responsive insights into big data and enables organizations to quickly democratize data visualizations and scale to hundreds of users at a fraction of the cost when compared to traditional BI tools. This book begins with an introduction to Amazon QuickSight, feature differentiators from traditional BI tools, and how it fits in the overall AWS big data ecosystem. With practical examples, you will find tips and techniques to load your data to AWS, prepare it, and finally visualize it using QuickSight. You will learn how to build interactive charts, reports, dashboards, and stories using QuickSight and share with others using just your browser and mobile app. The book also provides a blueprint to build a real-life big data project on top of AWS Data Lake Solution and demonstrates how to build a modern data lake on the cloud with governance, data catalog, and analysis. It reviews the current product shortcomings, features in the roadmap, and how to provide feedback to AWS. Grow your profits, improve your products, and beat your competitors. Style and approach This book takes a fast-paced, example-driven approach to demonstrate the power of QuickSight to improve your business' efficiency. Every chapter is accompanied with a use case that shows the practical implementation of the step being explained.

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 DescriptionThe 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 forThis 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.

Speed up the design and implementation of deep learning solutions using Apache Spark Key Features Explore the world of distributed deep learning with Apache Spark Train neural networks with deep learning libraries such as BigDL and TensorFlow Develop Spark deep learning applications to intelligently handle large and complex datasets Book DescriptionDeep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases. What you will learn Understand the basics of deep learning Set up Apache Spark for deep learning Understand the principles of distribution modeling and different types of neural networks Obtain an understanding of deep learning algorithms Discover textual analysis and deep learning with Spark Use popular deep learning frameworks, such as Deeplearning4j, TensorFlow, and Keras Explore popular deep learning algorithms Who this book is forIf you are a Scala developer, data scientist, or data analyst who wants to learn how to use Spark for implementing efficient deep learning models, Hands-On Deep Learning with Apache Spark is for you. Knowledge of the core machine learning concepts and some exposure to Spark will be helpful.

Build smarter systems by combining artificial intelligence and the Internet of Things-two of the most talked about topics today Key Features Leverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT data Process IoT data and predict outcomes in real time to build smart IoT models Cover practical case studies on industrial IoT, smart cities, and home automation Book DescriptionThere are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence. What you will learn Apply different AI techniques including machine learning and deep learning using TensorFlow and Keras Access and process data from various distributed sources Perform supervised and unsupervised machine learning for IoT data Implement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platforms Forecast time-series data using deep learning methods Implementing AI from case studies in Personal IoT, Industrial IoT, and Smart Cities Gain unique insights from data obtained from wearable devices and smart devices Who this book is forIf you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how popular artificial intelligence (AI) techniques can be used in the Internet of Things domain, this book will also be of benefit. A basic understanding of machine learning concepts will be required to get the best out of this book.

Explore TensorFlow's capabilities to perform efficient deep learning on images Key Features Discover image processing for machine vision Build an effective image classification system using the power of CNNs Leverage TensorFlow's capabilities to perform efficient deep learning Book DescriptionTensorFlow is Google's popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow's capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras. What you will learn Build machine learning models particularly focused on the MNIST digits Work with Docker and Keras to build an image classifier Understand natural language models to process text and images Prepare your dataset for machine learning Create classical, convolutional, and deep neural networks Create a RESTful image classification server Who this book is forHands-On Deep Learning for Images with TensorFlow is for you if you are an application developer, data scientist, or machine learning practitioner looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and basics of deep learning are required to get the best out of this book.

Get to grips with the most popular Python packages that make data analysis possible Key Features Explore the tools you need to become a data analyst Discover practical examples to help you grasp data processing concepts Walk through hierarchical indexing and grouping for data analysis Book DescriptionPython, a multi-paradigm programming language, has become the language of choice for data scientists for visualization, data analysis, and machine learning. Hands-On Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Python's NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Python's pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them. By the end of this book, you will have learned to index and group your data for sophisticated data analysis and manipulation. What you will learn Understand how to install and manage Anaconda Read, sort, and map data using NumPy and pandas Find out how to create and slice data arrays using NumPy Discover how to subset your DataFrames using pandas Handle missing data in a pandas DataFrame Explore hierarchical indexing and plotting with pandas Who this book is forHands-On Data Analysis with NumPy and Pandas is for you if you are a Python developer and want to take your first steps into the world of data analysis. No previous experience of data analysis is required to enjoy this book.

Build reporting applications and dashboards using the different MicroStrategy objects Key Features Learn the fundamentals of MicroStrategy Use MicroStrategy to get actionable insights from your business data Create visualizations and build intuitive dashboards and reports Book DescriptionMicroStrategy is an enterprise business intelligence application. It turns data into reports for making and executing key organization decisions. This book shows you how to implement Business Intelligence (BI) with MicroStrategy. It takes you from setting up and configuring MicroStrategy to security and administration. The book starts by detailing the different components of the MicroStrategy platform, and the key concepts of Metadata and Project Source. You will then install and configure MicroStrategy and lay down the foundations for building MicroStrategy BI solutions. By learning about objects and different object types, you will develop a strong understanding of the MicroStrategy Schema and Public Objects. With these MicroStrategy objects, you will enhance and scale your BI and Analytics solutions. Finally, you will learn about the administration, security, and monitoring of your BI solution. What you will learn Set up the MicroStrategy Intelligence Server and client tools Create a MicroStrategy metadata repository and your first Project Explore the main MicroStrategy object types and their dependencies Create, manipulate, and share Reports Create and share Dashboards Manage Users and Groups Who this book is forThis book is for Business Intelligence professionals or data analysts who want to get started with Microstrategy. Some basic understanding of BI and data analysis will be required to get the most from this book.