Take a look at our Data Visualization books. Shulph carries a great selection of Data Visualization books, and we are always adding more.
Get to grips with the basics of JupyterLab and its web interface with the help of this quick start guide Key Features Manage JupyterLab kernels, code consoles, and terminals, and share your work over the cloud Organize your data solutions within JupyterLab Install and configure extensions on JupyterLab using easy-to-follow examples Book Description JupyterLab is a web-based interface and the natural evolution of Jupyter Notebook. This guide will take you through the core commands and functionalities of JupyterLab and help you enhance your JupyterLab productivity. Starting with the installation of JupyterLab, this book will give you an overview of its features and the variety of problems it solves. You'll see how you can work with external files inside the platform, and understand how to use the code console and terminal. This will help you have distinct control over the scripts you work with. As you progress, you'll get to grips with the extensions available in JupyterLab, and gain insights into adding extensions to introduce new functionality in the interface. This book also covers widget operations within your document, different design patterns in JupyterLab, and the various methods for exchanging Notebooks. Additionally, you'll explore how to host JupyterLab Notebooks on GitHub. By the end of this Jupyter book, you'll have become well-versed with all the components of JupyterLab and be able to use it collaboratively within your data science teams. What you will learn Install JupyterLab and work with Jupyter Notebooks Host JupyterLab Notebooks on GitHub and access GitHub resources in your Notebooks Explore different methods for exchanging Notebooks Discover ways in which multiple users can access the same Notebook Publish your Notebooks with nbviewer and convert them into different formats Attach and operate widgets within your Notebooks using a JupyterLab document Use JupyterLab to work collaboratively with multiple data scientists in your teams Who this book is for This book is for data scientists and data analysts who are new to JupyterLab as well as for existing Jupyter users who want to get acquainted with its impressive features. Although not necessary, basic knowledge of Python will be helpful.
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 Description The 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 for Kibana 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.
Design interactive graphics and visuals for your data-driven applications using the popular open-source Chart.js data visualization library. Key Features Harness the power of JavaScript, HTML, and CSS to create interactive visualizations Display quantitative information efficiently in the form of attractive charts by using Chart.js A practical guide for creating data-driven applications using open-source JavaScript library Book Description Chart.js is a free, open-source data visualization library, maintained by an active community of developers in GitHub, where it rates as the second most popular data visualization library. If you want to quickly create responsive Web-based data visualizations for the Web, Chart.js is a great choice. This book guides the reader through dozens of practical examples, complete with code you can run and modify as you wish. It is a practical hands-on introduction to Chart.js. If you have basic knowledge of HTML, CSS and JavaScript you can learn to create beautiful interactive Web Canvas-based visualizations for your data using Chart.js. This book will help you set up Chart.js in a Web page and show how to create each one of the eight Chart.js chart types. You will also learn how to configure most properties that override Chart's default styles and behaviors. Practical applications of Chart.js are exemplified using real data files obtained from public data portals. You will learn how to load, parse, filter and select the data you wish to display from those files. You will also learn how to create visualizations that reveal patterns in the data. This book is based on Chart.js version 2.7.3 and ES2015 JavaScript. By the end of the book, you will be able to create beautiful, efficient and interactive data visualizations for the Web using Chart.js. What you will learn Learn how to create interactive and responsive data visualizations using Chart.js Learn how to create Canvas-based graphics without Canvas programming Create composite charts and configure animated data updates and transitions Efficiently display quantitative information using bar and line charts, scatterplots, and pie charts Learn how to load, parse, and filter external files in JSON and CSV formats Understand the benefits of using a data visualization framework Who this book is for The ideal target audience of this book includes web developers and designers, data journalists, data scientists and artists who wish to create interactive data visualizations for the Web. Basic knowledge of HTML, CSS, and JavaScript is required. No Canvas knowledge is necessary.
Explore the power of D3.js 5 and its integration with web technologies for building rich and interactive data visualization solutions Key Features Explore the latest D3.js 5 for creating charts, plots, and force-directed graphics Practical guide for creating interactive graphics and data-driven apps with JavaScript Build Real-time visualization and transition on web using SVG with D3.js Book Description This book is a practical hands-on introduction to D3 (Data-driven Documents): the most popular open-source JavaScript library for creating interactive web-based data visualizations. Based entirely on open web standards, D3 provides an integrated collection of tools for efficiently binding data to graphical elements. If you have basic knowledge of HTML, CSS and JavaScript you can use D3.js to create beautiful interactive web-based data visualizations. D3 is not a charting library. It doesn't contain any pre-defined chart types, but can be used to create whatever visual representations of data you can imagine. The goal of this book is to introduce D3 and provide a learning path so that you obtain a solid understanding of its fundamental concepts, learn to use most of its modules and functions, and gain enough experience to create your own D3 visualizations. You will learn how to create bar, line, pie and scatter charts, trees, dendograms, treemaps, circle packs, chord/ribbon diagrams, sankey diagrams, animated network diagrams, and maps using different geographical projections. Fundamental concepts are explained in each chapter and then applied to a larger example in step-by-step tutorials, complete with full code, from hundreds of examples you can download and run. This book covers D3 version 5 and is based on ES2015 JavaScript. What you will learn Learn to use D3.js version 5 and web standards to create beautiful interactive data-driven visualizations for the web Bind data to DOM elements, applying different scales, color schemes and configuring smooth animated transitions for data updates Generate data structures and layouts for many popular chart formats Apply interactive behaviors to any chart Create thematic maps based on GIS data using different geographical projections with interactive behaviors Load, parse and transform data from JSON and CSV formats Who this book is for The book is intended for web developers, web designers, data scientists, artists, and any developer who wish to create interactive data visualization for the Web using D3. The book assumes basic knowledge of HTML, CSs, and JavaScript.
Use artificial intelligence and machine learning on AWS to create engaging applications Key Features Explore popular AI and ML services with their underlying algorithms Use the AWS environment to manage your AI workflow Reinforce key concepts with hands-on exercises using real-world datasets Book Description Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are highly scalable. From natural language processing (NLP) applications, such as language translation and understanding news articles and other text sources, to creating chatbots with both voice and text interfaces, you will learn all that there is to know about using AWS to your advantage. You will also understand how to process huge numbers of images fast and create machine learning models. By the end of this book, you will have developed the skills you need to efficiently use AWS in your machine learning and artificial intelligence projects. What you will learn Get up and running with machine learning on the AWS platform Analyze unstructured text using AI and Amazon Comprehend Create a chatbot and interact with it using speech and text input Retrieve external data via your chatbot Develop a natural language interface Apply AI to images and videos with Amazon Rekognition Who this book is for Machine Learning with AWS is ideal for data scientists, programmers, and machine learning enthusiasts who want to learn about the artificial intelligence and machine learning capabilities of Amazon Web Services.
Perform advanced data manipulation tasks using pandas and become an expert data analyst. Key Features Manipulate and analyze your data expertly using the power of pandas Work with missing data and time series data and become a true pandas expert Includes expert tips and techniques on making your data analysis tasks easier Book Description pandas is a popular Python library used by data scientists and analysts worldwide to manipulate and analyze their data. This book presents useful data manipulation techniques in pandas to perform complex data analysis in various domains. An update to our highly successful previous edition with new features, examples, updated code, and more, this book is an in-depth guide to get the most out of pandas for data analysis. Designed for both intermediate users as well as seasoned practitioners, you will learn advanced data manipulation techniques, such as multi-indexing, modifying data structures, and sampling your data, which allow for powerful analysis and help you gain accurate insights from it. With the help of this book, you will apply pandas to different domains, such as Bayesian statistics, predictive analytics, and time series analysis using an example-based approach. And not just that; you will also learn how to prepare powerful, interactive business reports in pandas using the Jupyter notebook. By the end of this book, you will learn how to perform efficient data analysis using pandas on complex data, and become an expert data analyst or data scientist in the process. What you will learn Speed up your data analysis by importing data into pandas Keep relevant data points by selecting subsets of your data Create a high-quality dataset by cleaning data and fixing missing values Compute actionable analytics with grouping and aggregation in pandas Master time series data analysis in pandas Make powerful reports in pandas using Jupyter notebooks Who this book is for This book is for data scientists, analysts and Python developers who wish to explore advanced data analysis and scientific computing techniques using pandas. Some fundamental understanding of Python programming and familiarity with the basic data analysis concepts is all you need to get started with this book.
Expert Choice to build Business Intelligence landscapes and dashboards for Enterprises Key Features - In-depth knowledge of Power BI, demonstrated through step-by-step exercises. - Covers data modelling, visualization, and implementing security with complete hands-on training. - Includes a project that simulates a realistic business environment from start to finish. Description Mastering Power BI covers the entire Power BI implementation process. The readers will be able to understand all the concepts covered in this book, from data modelling to creating powerful - visualizations. This book begins with the concepts and terminology such as Star-Schema, dimensions and facts. It explains about multi-table dataset and demonstrates how to load these tables into Power BI. It shows how to load stored data in various formats and create relationships. Readers will also learn more about Data Analysis Expressions (DAX). This book is a must for the developers wherein they learn how to extend the usability of Power BI, to explore meaningful and hidden data insights. Throughout the book, you keep on learning about the concepts, techniques and expert practices on loading and shaping data, visualization design and security implementation. What you will learn - Learn about Business Intelligence (BI) concepts and its contribution in business analytics. - Learn to connect, load, and transform data from disparate data sources. - Start creating and executing powerful DAX calculations. - Design various visualizations to prepare insightful reports and dashboards. Who this book is for This book is for anyone interested in learning how to use Power BI desktop or starting a career in Business Intelligence and Analytics. While this covers all the fundamentals, it is recommended that the reader be familiar with MS-Excel and database concepts. Table of Contents 1. Understanding the Basics 2. Connect and Shape 3. Optimize your datamodel 4. Data Analysis Expressions (DAX) 5. Visualizations in Power BI 6. Power BI Service 7. Securing your application About the Authors Chandraish Sinha is the Founder/President of Ohio Computer Academy, a company dedicated to IT education. An IT trainer at heart, Chandraish resonates with his company’s slogan Inspire, Educate & Evolve. He is a Business Intelligence learner and explorer. He has implemented multiple large and medium scale BI solutions. In his 22 years of career, Chandraish has worked with a variety of dashboarding applications such as, Power BI, Tableau, QlikView, Qlik Sense, IBM Cognos, Business Objects and Actuate. He is passionate about data and explores applications that provide better data insights. He has also authored multiple books on Tableau and QlikView. Checkout his Amazon author profile amazon.com/author/chandraishsinha Blog links: https://ohiocomputeracademy.com/category/power-bi/ LinkedIn Profile: www.linkedin.com/in/chandraishsinha
Understand data analysis pipelines using machine learning algorithms and techniques with this practical guideKey FeaturesPrepare and clean your data to use it for exploratory analysis, data manipulation, and data wranglingDiscover supervised, unsupervised, probabilistic, and Bayesian machine learning methodsGet to grips with graph processing and sentiment analysisBook DescriptionData analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines.Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask.By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.What you will learnExplore data science and its various process modelsPerform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing valuesCreate interactive visualizations using Matplotlib, Seaborn, and BokehRetrieve, process, and store data in a wide range of formatsUnderstand data preprocessing and feature engineering using pandas and scikit-learnPerform time series analysis and signal processing using sunspot cycle dataAnalyze textual data and image data to perform advanced analysisGet up to speed with parallel computing using DaskWho this book is forThis book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.
Explore the different data mining techniques using the libraries and packages offered by Python Key Features Grasp the basics of data loading, cleaning, analysis, and visualization Use the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data mining Your one-stop guide to build efficient data mining pipelines without going into too much theory Book Description Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle. What you will learn Explore the methods for summarizing datasets and visualizing/plotting data Collect and format data for analytical work Assign data points into groups and visualize clustering patterns Learn how to predict continuous and categorical outputs for data Clean, filter noise from, and reduce the dimensions of data Serialize a data processing model using scikit-learn's pipeline feature Deploy the data processing model using Python's pickle module Who this book is for Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.
Build your data science skills. Start data visualization Using Python. Right away. Become a good data analyst by creating quality data visualizations using Python. Key Features - Exciting coverage on loads of Python libraries, including Matplotlib, Seaborn, Pandas, and Plotly. - Tons of examples, illustrations, and use-cases to demonstrate visual storytelling of varied datasets. - Covers a strong fundamental understanding of exploratory data analysis (EDA), statistical modeling, and data mining. Description Data visualization plays a major role in solving data science challenges with various capabilities it offers. This book aims to equip you with a sound knowledge of Python in conjunction with the concepts you need to master to succeed as a data visualization expert. The book starts with a brief introduction to the world of data visualization and talks about why it is important, the history of visualization, and the capabilities it offers. You will learn how to do simple Python-based visualization with examples with progressive complexity of key features. The book starts with Matplotlib and explores the power of data visualization with over 50 examples. It then explores the power of data visualization using one of the popular exploratory data analysis-oriented libraries, Pandas. The book talks about statistically inclined data visualization libraries such as Seaborn. The book also teaches how we can leverage bokeh and Plotly for interactive data visualization. Each chapter is enriched and loaded with 30+ examples that will guide you in learning everything about data visualization and storytelling of mixed datasets. What you will learn - Learn to work with popular Python libraries and frameworks, including Seaborn, Bokeh, and Plotly. - Practice your data visualization understanding across numerous datasets and real examples. - Learn to visualize geospatial and time-series datasets. - Perform correlation and EDA analysis using Pandas and Matplotlib. Who this book is for This book is for all data analytics professionals, data scientists, and data mining hobbyists who want to be strong data visualizers by learning all the popular Python data visualization libraries. Prior working knowledge of Python is assumed. Table of Contents 1. Introduction to Data Visualization 2. Why Data Visualization 3. Various Data Visualization Elements and Tools 4. Using Matplotlib with Python 5. Using NumPy and Pandas for Plotting 6. Using Seaborn for Visualization 7. Using Bokeh with Python 8. Using Plotly, Folium, and Other Tools for Data Visualization 9. Hands-on Examples and Exercises, Case Studies, and Further Resources About the Authors Kallur Rahman is an IT industry leader with over 2 decades of experience in software development, testing, program/ project management, and management consultancy. He has been a developer, designer, technical architect, test program manager, delivery unit head, IT Services, and COE/Factory Services leader of various complexity spanning telecommunications, Life Sciences, Retail, and Healthcare Industries. He has a master’s degree in Business Administration preceded by an Engineering degree in Computer Science. He has counseled CxO level executives in market-leading corporations for testing, business and technology transformation programs. As a thought-leader, he is a frequent invitee at several industry events spanning technical and domain-focused themes. He is a believer in “Knowledge is Power” and is passionate about authoring and sharing his knowledge. He has published over 200 articles across LinkedIn, DevOps.Com, and other leading magazines. He is additionally an active quizzing aficionado who engages and contributes at corporate level quizzing. LinkedIn Bio: https://www.linkedin.com/in/kalilurrahman/