Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time Key Features Make a hands-on start in the fields of Big Data, Distributed Technologies and Machine Learning Learn how to design, develop and interpret the results of common Machine Learning algorithms Uncover hidden patterns in your data in order to derive real actionable insights and business value Book DescriptionEvery person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data. What you will learn Understand how Spark fits in the context of the big data ecosystem Understand how to deploy and configure a local development environment using Apache Spark Understand how to design supervised and unsupervised learning models Build models to perform NLP, deep learning, and cognitive services using Spark ML libraries Design real-time machine learning pipelines in Apache Spark Become familiar with advanced techniques for processing a large volume of data by applying machine learning algorithms Who this book is forThis book is aimed at Business Analysts, Data Analysts and Data Scientists who wish to make a hands-on start in order to take advantage of modern Big Data technologies combined with Advanced Analytics.

Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing. Key Features Clear and concise explanations Gives important insights into deep learning models Practical demonstration of key concepts Book DescriptionPyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text. By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease. What you will learn Set up the deep learning environment using the PyTorch library Learn to build a deep learning model for image classification Use a convolutional neural network for transfer learning Understand to use PyTorch for natural language processing Use a recurrent neural network to classify text Understand how to optimize PyTorch in multiprocessor and distributed environments Train, optimize, and deploy your neural networks for maximum accuracy and performance Learn to deploy production-ready models Who this book is forDevelopers and Data Scientist familiar with Machine Learning but new to deep learning, or existing practitioners of deep learning who would like to use PyTorch to train their deep learning models will find this book to be useful. Having knowledge of Python programming will be an added advantage, while previous exposure to PyTorch is not needed.

Learn quick and effective techniques to get up and running with building blockchain including Ethereum and Hyperledger Fabric. Key Features Understand the key concepts of decentralized applications and consensus algorithms Learn key concepts of Ethereum and Solidity programming Practical guide to get started with build efficient Blockchain applications with Ethereum and Hyperledger Book DescriptionBlockchain is a technology that powers the development of decentralized applications.This technology allows the construction of a network with no single control that enables participants to make contributions to and receive benefits from the network directly. This book will give you a thorough overview of blockchain and explain how a blockchain works.You will begin by going through various blockchain consensus mechanisms and cryptographic hash functions. You will then learn the fundamentals of programming in Solidity - the defacto language for developing decentralize, applications in Ethereum. After that, you will set up an Ethereum development environment and develop, package, build, and test campaign-decentralized applications.The book also shows you how to set up Hyperledger composer tools, analyze business scenarios, design business models, and write a chain code. Finally, you will get a glimpse of how blockchain is actually used in different real-world domains. By the end of this guide, you will be comfortable working with basic blockchain frameworks, and develop secure, decentralized applications in a hassle-free manner. What you will learn Understand how blockchain hashing works Write and test a smart contract using Solidity Develop and test a decentralized application Build and test your application using Hyperledger Fabric Implement business network using Hyperledger Composer Test and interact with business network applications Who this book is forThe book is for developers, analysts, or anyone looking to learn about Blockchain in a quick and easy manner.

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.

Data Analysis in Criminal Justice and Criminology: History, Concept, and Application breaks down various data analysis techniques to help students build their conceptual understanding of key methods and processes. The information in the text encourages discussion and consideration of how and why data analysis plays an important role in the fields of criminal justice and criminology. The book is divided into three units. Unit 1 discusses how data analysis is used in criminal justice and criminology, various methods of data collection, the importance of identifying the purpose of analysis and key data elements prior to analyzing information, and graphical representation of data. Unit 2 introduces students to samples, distributions, and the central limit theorem as it relates to data analysis. This section provides students with the essential knowledge and skills needed to understand statistical concepts and calculations. The final unit explains how to move beyond statistical description to statistical inference and how sample statistics can be used to estimate population parameters. Highly accessible in nature, Data Analysis in Criminal Justice and Criminology is ideal for undergraduate and graduate courses in criminal justice, criminology, and sociology especially those with emphasis on data analysis.

Learn to view, edit and analyse geospatial data using QGIS and Python 3 Key Features Leverage the power of QGIS to add professionalism to your maps Explore and work with the newly released features like Python 3, GeoPackage, 3D views, Print layouts in QGIS 3.4 Build your own plugins and customize maps using QT designer Book DescriptionQGIS 3.4 is the first LTR (long term release) of QGIS version 3. This is a giant leap forward for the project with tons of new features and impactful changes. Learn QGIS is fully updated for QGIS 3.4, covering its processing engine update, Python 3 de-facto coding environment, and the GeoPackage format. This book will help you get started on your QGIS journey, guiding you to develop your own processing pathway. You will explore the user interface, loading your data, editing, and then creating data. QGIS often surprises new users with its mapping capabilities; you will discover how easily you can style and create your first map. But that's not all! In the final part of the book, you'll learn about spatial analysis and the powerful tools in QGIS, and conclude by looking at Python processing options. By the end of the book, you will have become proficient in geospatial analysis using QGIS and Python. What you will learn Explore various ways to load data into QGIS Understand how to style data and present it in a map Create maps and explore ways to expand them Get acquainted with the new processing toolbox in QGIS 3.4 Manipulate your geospatial data and gain quality insights Understand how to customize QGIS 3.4 Work with QGIS 3.4 in 3D Who this book is forIf you are a developer or consultant familiar with the basic functions and processes of GIS and want to learn how to use QGIS to analyze geospatial data and create rich mapping applications, this book is for you. You'll also find this book useful if you're new to QGIS and wish to grasp its fundamentals

Discover the new features and widely used packages in Julia to solve complex computational problems in your statistical applications. Key Features Address the core problems of programming in Julia with the most popular packages for common tasks Tackle issues while working with Databases and Parallel data processing with Julia Explore advanced features such as metaprogramming, functional programming, and user defined types Book DescriptionJulia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data What you will learn Boost your code's performance using Julia's unique features Organize data in to fundamental types of collections: arrays and dictionaries Organize data science processes within Julia and solve related problems Scale Julia computations with cloud computing Write data to IO streams with Julia and handle web transfer Define your own immutable and mutable types Speed up the development process using metaprogramming Who this book is forThis book is for developers who would like to enhance their Julia programming skills and would like to get some quick solutions to their common programming problems. Basic Julia programming knowledge is assumed.

Extend PostgreSQL using PostgreSQL server programming to create, test, debug, and optimize a range of user-defined functions in your favorite programming language Key Features Learn the concepts of PostgreSQL 11 with lots of real-world datasets and examples Learn queries, data replication, and database performance Extend the functionalities of your PostgreSQL instance to suit your organizational needs Book DescriptionPostgreSQL is a rock-solid, scalable, and safe enterprise-level relational database. With a broad range of features and stability, it is ever increasing in popularity.This book shows you how to take advantage of PostgreSQL 11 features for server-side programming. Server-side programming enables strong data encapsulation and coherence. The book begins with the importance of server-side programming and explains the risks of leaving all the checks outside the database. To build your capabilities further, you will learn how to write stored procedures, both functions and the new PostgreSQL 11 procedures, and create triggers to perform encapsulation and maintain data consistency. You will also learn how to produce extensions, the easiest way to package your programs for easy and solid deployment on different PostgreSQL installations. What you will learn Explore data encapsulation Write stored procedures in different languages Interact with transactions from within a function Get to grips with triggers and rules Create and manage custom data types Create extensions to package code and data Implement background workers and Inter-Process Communication (IPC) How to deal with foreign languages, in particular Java and Perl Who this book is forThis book is for database administrators, data engineers, and database engineers who want to implement advanced functionalities and master complex administrative tasks with PostgreSQL 11.

Build efficient, high-performance & scalable systems to process large volumes of data with Apache Ignite Key Features Understand Apache Ignite's in-memory technology Create High-Performance app components with Ignite Build a real-time data streaming and complex event processing system Book DescriptionApache Ignite is a distributed in-memory platform designed to scale and process large volume of data. It can be integrated with microservices as well as monolithic systems, and can be used as a scalable, highly available and performant deployment platform for microservices. This book will teach you to use Apache Ignite for building a high-performance, scalable, highly available system architecture with data integrity. The book takes you through the basics of Apache Ignite and in-memory technologies. You will learn about installation and clustering Ignite nodes, caching topologies, and various caching strategies, such as cache aside, read and write through, and write behind. Next, you will delve into detailed aspects of Ignite's data grid: web session clustering and querying data. You will learn how to process large volumes of data using compute grid and Ignite's map-reduce and executor service. You will learn about the memory architecture of Apache Ignite and monitoring memory and caches. You will use Ignite for complex event processing, event streaming, and the time-series predictions of opportunities and threats. Additionally, you will go through off-heap and on-heap caching, swapping, and native and Spring framework integration with Apache Ignite. By the end of this book, you will be confident with all the features of Apache Ignite 2.x that can be used to build a high-performance system architecture. What you will learn Use Apache Ignite's data grid and implement web session clustering Gain high performance and linear scalability with in-memory distributed data processing Create a microservice on top of Apache Ignite that can scale and perform Perform ACID-compliant CRUD operations on an Ignite cache Retrieve data from Apache Ignite's data grid using SQL, Scan and Lucene Text query Explore complex event processing concepts and event streaming Integrate your Ignite app with the Spring framework Who this book is forThe book is for Big Data professionals who want to learn the essentials of Apache Ignite. Prior experience in Java is necessary.

Learn how to architect, implement, and administer a complex Splunk Enterprise environment and extract valuable insights from business data. Key Features Understand the various components of Splunk and how they work together to provide a powerful Big Data analytics solution. Collect and index data from a wide variety of common machine data sources Design searches, reports, and dashboard visualizations to provide business data insights Book DescriptionSplunk is a leading platform and solution for collecting, searching, and extracting value from ever increasing amounts of big data - and big data is eating the world! This book covers all the crucial Splunk topics and gives you the information and examples to get the immediate job done. You will find enough insights to support further research and use Splunk to suit any business environment or situation. Splunk 7.x Quick Start Guide gives you a thorough understanding of how Splunk works. You will learn about all the critical tasks for architecting, implementing, administering, and utilizing Splunk Enterprise to collect, store, retrieve, format, analyze, and visualize machine data. You will find step-by-step examples based on real-world experience and practical use cases that are applicable to all Splunk environments. There is a careful balance between adequate coverage of all the critical topics with short but relevant deep-dives into the configuration options and steps to carry out the day-to-day tasks that matter. By the end of the book, you will be a confident and proficient Splunk architect and administrator. What you will learn Design and implement a complex Splunk Enterprise solution Configure your Splunk environment to get machine data in and indexed Build searches to get and format data for analysis and visualization Build reports, dashboards, and alerts to deliver critical insights Create knowledge objects to enhance the value of your data Install Splunk apps to provide focused views into key technologies Monitor, troubleshoot, and manage your Splunk environment Who this book is forThis book is intended for experienced IT personnel who are just getting started working with Splunk and want to quickly become proficient with its usage. Data analysts who need to leverage Splunk to extract critical business insights from application logs and other machine data sources will also benefit from this book.

Understand and build beautiful and advanced plots with Matplotlib and Python Key Features Practical guide with hands-on examples to design interactive plots Advanced techniques to constructing complex plots Explore 3D plotting and visualization using Jupyter Notebook Book DescriptionIn this book, you'll get hands-on with customizing your data plots with the help of Matplotlib. You'll start with customizing plots, making a handful of special-purpose plots, and building 3D plots. You'll explore non-trivial layouts, Pylab customization, and more about tile configuration. You'll be able to add text, put lines in plots, and also handle polygons, shapes, and annotations. Non-Cartesian and vector plots are exciting to construct, and you'll explore them further in this book. You'll delve into niche plots and visualize ordinal and tabular data. In this book, you'll be exploring 3D plotting, one of the best features when it comes to 3D data visualization, along with Jupyter Notebook, widgets, and creating movies for enhanced data representation. Geospatial plotting will also be explored. Finally, you'll learn how to create interactive plots with the help of Jupyter. Learn expert techniques for effective data visualization using Matplotlib 3 and Python with our latest offering -- Matplotlib 3.0 Cookbook What you will learn Deal with non-trivial and unusual plots Understanding Basemap methods Customize and represent data in 3D Construct Non-Cartesian and vector plots Design interactive plots using Jupyter Notebook Make movies for enhanced data representation Who this book is forThis book is aimed at individuals who want to explore data visualization techniques. A basic knowledge of Matplotlib and Python is required.

A fast paced guide that will help you learn about Apache Hadoop 3 and its ecosystem Key Features Set up, configure and get started with Hadoop to get useful insights from large data sets Work with the different components of Hadoop such as MapReduce, HDFS and YARN Learn about the new features introduced in Hadoop 3 Book DescriptionApache Hadoop is a widely used distributed data platform. It enables large datasets to be efficiently processed instead of using one large computer to store and process the data. This book will get you started with the Hadoop ecosystem, and introduce you to the main technical topics, including MapReduce, YARN, and HDFS. The book begins with an overview of big data and Apache Hadoop. Then, you will set up a pseudo Hadoop development environment and a multi-node enterprise Hadoop cluster. You will see how the parallel programming paradigm, such as MapReduce, can solve many complex data processing problems. The book also covers the important aspects of the big data software development lifecycle, including quality assurance and control, performance, administration, and monitoring. You will then learn about the Hadoop ecosystem, and tools such as Kafka, Sqoop, Flume, Pig, Hive, and HBase. Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark. By the end of the book, you will be well versed with different configurations of the Hadoop 3 cluster. What you will learn Store and analyze data at scale using HDFS, MapReduce and YARN Install and configure Hadoop 3 in different modes Use Yarn effectively to run different applications on Hadoop based platform Understand and monitor how Hadoop cluster is managed Consume streaming data using Storm, and then analyze it using Spark Explore Apache Hadoop ecosystem components, such as Flume, Sqoop, HBase, Hive, and Kafka Who this book is forAspiring Big Data professionals who want to learn the essentials of Hadoop 3 will find this book to be useful. Existing Hadoop users who want to get up to speed with the new features introduced in Hadoop 3 will also benefit from this book. Having knowledge of Java programming will be an added advantage.

Build, manage, and configure high-performing, reliable NoSQL database for your applications with Cassandra Key Features Write programs more efficiently using Cassandra's features with the help of examples Configure Cassandra and fine-tune its parameters depending on your needs Integrate Cassandra database with Apache Spark and build strong data analytics pipeline Book DescriptionWith ever-increasing rates of data creation, the demand for storing data fast and reliably becomes a need. Apache Cassandra is the perfect choice for building fault-tolerant and scalable databases. Mastering Apache Cassandra 3.x teaches you how to build and architect your clusters, configure and work with your nodes, and program in a high-throughput environment, helping you understand the power of Cassandra as per the new features. Once you've covered a brief recap of the basics, you'll move on to deploying and monitoring a production setup and optimizing and integrating it with other software. You'll work with the advanced features of CQL and the new storage engine in order to understand how they function on the server-side. You'll explore the integration and interaction of Cassandra components, followed by discovering features such as token allocation algorithm, CQL3, vnodes, lightweight transactions, and data modelling in detail. Last but not least you will get to grips with Apache Spark. By the end of this book, you'll be able to analyse big data, and build and manage high-performance databases for your application. What you will learn Write programs more efficiently using Cassandra's features more efficiently Exploit the given infrastructure, improve performance, and tweak the Java Virtual Machine (JVM) Use CQL3 in your application in order to simplify working with Cassandra Configure Cassandra and fine-tune its parameters depending on your needs Set up a cluster and learn how to scale it Monitor a Cassandra cluster in different ways Use Apache Spark and other big data processing tools Who this book is forMastering Apache Cassandra 3.x is for you if you are a big data administrator, database administrator, architect, or developer who wants to build a high-performing, scalable, and fault-tolerant database. Prior knowledge of core concepts of databases is required.

Web Scraping techniques are getting more popular, since data is as valuable as oil in 21st century. Through this book get some key knowledge about using XPath, regEX; web scraping libraries for R like rvest and RSelenium technologies. Key Features Techniques, tools and frameworks for web scraping with R Scrape data effortlessly from a variety of websites Learn how to selectively choose the data to scrape, and build your dataset Book DescriptionWeb scraping is a technique to extract data from websites. It simulates the behavior of a website user to turn the website itself into a web service to retrieve or introduce new data. This book gives you all you need to get started with scraping web pages using R programming. You will learn about the rules of RegEx and Xpath, key components for scraping website data. We will show you web scraping techniques, methodologies, and frameworks. With this book's guidance, you will become comfortable with the tools to write and test RegEx and XPath rules. We will focus on examples of dynamic websites for scraping data and how to implement the techniques learned. You will learn how to collect URLs and then create XPath rules for your first web scraping script using rvest library. From the data you collect, you will be able to calculate the statistics and create R plots to visualize them. Finally, you will discover how to use Selenium drivers with R for more sophisticated scraping. You will create AWS instances and use R to connect a PostgreSQL database hosted on AWS. By the end of the book, you will be sufficiently confident to create end-to-end web scraping systems using R. What you will learn Write and create regEX rules Write XPath rules to query your data Learn how web scraping methods work Use rvest to crawl web pages Store data retrieved from the web Learn the key uses of Rselenium to scrape data Who this book is forThis book is for R programmers who want to get started quickly with web scraping, as well as data analysts who want to learn scraping using R. Basic knowledge of R is all you need to get started with this book.

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering. Key Features Build your first machine learning model using scikit-learn Train supervised and unsupervised models using popular techniques such as classification, regression and clustering Understand how scikit-learn can be applied to different types of machine learning problems Book DescriptionScikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions. What you will learn Learn how to work with all scikit-learn's machine learning algorithms Install and set up scikit-learn to build your first machine learning model Employ Unsupervised Machine Learning Algorithms to cluster unlabelled data into groups Perform classification and regression machine learning Use an effective pipeline to build a machine learning project from scratch Who this book is forThis book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.

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.

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

Build attractive, insightful, and powerful visualizations to gain quality insights from your data Key Features Master Matplotlib for data visualization Customize basic plots to make and deploy figures in cloud environments Explore recipes to design various data visualizations from simple bar charts to advanced 3D plots Book DescriptionMatplotlib provides a large library of customizable plots, along with a comprehensive set of backends. Matplotlib 3.0 Cookbook is your hands-on guide to exploring the world of Matplotlib, and covers the most effective plotting packages for Python 3.7. With the help of this cookbook, you'll be able to tackle any problem you might come across while designing attractive, insightful data visualizations. With the help of over 150 recipes, you'll learn how to develop plots related to business intelligence, data science, and engineering disciplines with highly detailed visualizations. Once you've familiarized yourself with the fundamentals, you'll move on to developing professional dashboards with a wide variety of graphs and sophisticated grid layouts in 2D and 3D. You'll annotate and add rich text to the plots, enabling the creation of a business storyline. In addition to this, you'll learn how to save figures and animations in various formats for downstream deployment, followed by extending the functionality offered by various internal and third-party toolkits, such as axisartist, axes_grid, Cartopy, and Seaborn. By the end of this book, you'll be able to create high-quality customized plots and deploy them on the web and on supported GUI applications such as Tkinter, Qt 5, and wxPython by implementing real-world use cases and examples. What you will learn Develop simple to advanced data visualizations in Matplotlib Use the pyplot API to quickly develop and deploy different plots Use object-oriented APIs for maximum flexibility with the customization of figures Develop interactive plots with animation and widgets Use maps for geographical plotting Enrich your visualizations using embedded texts and mathematical expressions Embed Matplotlib plots into other GUIs used for developing applications Use toolkits such as axisartist, axes_grid1, and cartopy to extend the base functionality of Matplotlib Who this book is forThe Matplotlib 3.0 Cookbook is for you if you are a data analyst, data scientist, or Python developer looking for quick recipes for a multitude of visualizations. This book is also for those who want to build variations of interactive visualizations.

Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts such as SVM, KNN classifiers, and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book DescriptionGetting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learn Get up and running with the Jupyter ecosystem Identify potential areas of investigation and perform exploratory data analysis Plan a machine learning classification strategy and train classification models Use validation curves and dimensionality reduction to tune and enhance your models Scrape tabular data from web pages and transform it into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is forApplied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.

Leverage the power of Tableau to get actionable business insights and make better business decisions Key Features Explore all the new features of Tableau 2018.x Begin your Tableau journey by understanding its core functionalities Apply advanced techniques such as data blending and parameters to make the best of Tableau Book DescriptionTableau is one of the leading business intelligence tools used worldwide, in organizations of every scale. In its latest release, Tableau 2018 promises richer and more useful features related to visual analytics, reporting, dashboarding, and a host of other data visualization aspects. Getting Started with Tableau 2018.x will get you up and running with these features. The book starts with all the new functionalities of the different Tableau 2018 versions, along with concrete examples of how to use them. However, if you're new to Tableau, don't worry! The rest of the book will guide you through each major aspect of Tableau with examples. You'll learn how to connect to data, build a data source, visualize your data, build a dashboard, and share it online. In the final chapters, you'll also learn advanced techniques such as creating a cross-database join, data blending, and more. By the end of the book, you will have a firm understanding of how to effectively use Tableau to create quick, cost-effective, and business-efficient business intelligence solutions. What you will learn Discover new functionalities such as density, extensions, and transparency introduced in Tableau 2018.x Connect tables and make transformations such as pivoting the field and splitting columns Build an efficient data source for your analysis Design insightful data visualization using different mark types and properties Build 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 forIf you're a beginner or an existing BI professional looking to get the most out of Tableau 2018's latest features, this book is for you. You'll also find this book useful if you're an aspiring analyst who wants to learn the capabilities of Tableau to answer business-specific questions. No prior knowledge or experience with Tableau or any other Business Intelligence tool is necessary.

Learn how to quickly generate business intelligence, insights and create interactive dashboards for digital storytelling through various data sources with Redash Key Features Learn the best use of visualizations to build powerful interactive dashboards Create and share visualizations and data in your organization Work with different complexities of data from different data sources Book DescriptionData exploration and visualization is vital to Business Intelligence, the backbone of almost every enterprise or organization. Redash is a querying and visualization tool developed to simplify how marketing and business development departments are exposed to data. If you want to learn to create interactive dashboards with Redash, explore different visualizations, and share the insights with your peers, then this is the ideal book for you. The book starts with essential Business Intelligence concepts that are at the heart of data visualizations. You will learn how to find your way round Redash and its rich array of data visualization options for building interactive dashboards. You will learn how to create data storytelling and share these with peers. You will see how to connect to different data sources to process complex data, and then visualize this data to reveal valuable insights. By the end of this book, you will be confident with the Redash dashboarding tool to provide insight and communicate data storytelling. What you will learn Install Redash and troubleshoot installation errors Manage user roles and permissions Fetch data from various data sources Visualize and present data with Redash Create active alerts based on your data Understand Redash administration and customization Export, share and recount stories with Redash visualizations Interact programmatically with Redash through the Redash API Who this book is forThis book is intended for Data Analysts, BI professionals and Data Developers, but can be useful to anyone who has a basic knowledge of SQL and a creative mind. Familiarity with basic BI concepts will be helpful, but no knowledge of Redash is required.