Dive into the fascinating world of graph theory and its applications with 'Modern Graph Theory Algorithms with Python.' Through Python programming and real-world case studies, this book equips you with the tools to transform data into graph structures, apply algorithms, and uncover insights, enabling effective solutions in diverse domains such as finance, epidemiology, and social networks. What this Book will help me do Understand how to wrangle a variety of data types into network formats suitable for analysis. Learn to use graph theory algorithms and toolkits such as NetworkX and igraph in Python. Apply network theory to predict and analyze trends, from epidemics to stock market dynamics. Explore the intersection of machine learning and graph theory through advanced neural network techniques. Gain expertise in database solutions with graph database querying and applications. Author(s) Colleen M. Farrelly, an experienced data scientist, and Franck Kalala Mutombo, a seasoned software engineer, bring years of expertise in network science and Python programming to every page of this book. Their professional experience includes working on cutting-edge problems in data analytics, graph theory, and scalable solutions for real-world issues. Combining their practical know-how, they deliver a resource aimed at both learning and applying techniques effectively. Who is it for? This book is tailored for data scientists, researchers, and analysts with an interest in using graph-based approaches for solving complex data problems. Ideal for those with a basic Python knowledge and familiarity with libraries like pandas and NumPy, the content bridges the gap between theory and application. It also provides insights into broad fields where network science can be impactful, contributing value to both students and professionals.

This book is intended as an introduction to Probability Theory and Mathematical Statistics for students in mathematics, the physical sciences, engineering, and related fields. It is based on the author’s 25 years of experience teaching probability and is squarely aimed at helping students overcome common difficulties in learning the subject. The focus of the book is an explanation of the theory, mainly by the use of many examples. Whenever possible, proofs of stated results are provided. All sections conclude with a short list of problems. The book also includes several optional sections on more advanced topics. This textbook would be ideal for use in a first course in Probability Theory. Contents: Probabilities Conditional Probabilities and Independence Random Variables and Their Distribution Operations on Random Variables Expected Value, Variance, and Covariance Normally Distributed Random Vectors Limit Theorems Introduction to Stochastic Processes Mathematical Statistics Appendix Bibliography Index

The book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis and related applications, arising from data science, operations research, engineering, machine learning or statistics. The chapters of this collaborative work represent a cross-section of current research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications. The published data analysis methodology includes the updated state-of-the-art rapidly developed theory and applications of data expansion, both of which go through outstanding changes nowadays. New approaches are expected to deliver and have been developed, including Artificial Intelligence.

This book covers the key principles, best practices, and practical techniques for designing and implementing visually compelling dashboards. It explores the various stages of the dashboard development process, from understanding user needs and defining goals, to selecting appropriate visual encodings, designing effective layouts, and employing interactive elements. It also addresses the critical aspect of data storytelling, examining how narratives and context can be woven into dashboards to deliver impactful insights and engage audiences. Visual Analytics for Dashboards is designed to cater to a wide range of readers, from beginners looking to grasp the fundamentals of visual analytics, to seasoned professionals seeking to enhance their dashboard design skills. For different types of readers, such as a data analyst, BI professional, data scientist, or simply someone interested in data visualization, this book aims to equip them with the knowledge and tools necessary to create impactful dashboards. What you’ll learn The principles of data visualization How to create effective dashboards Meet all the requirements for visual analytics/data visualization/dashboard courses Deepen understanding of data presentation and analysis How to use different kinds of tools for data analysis, such as scorecards and key performance indicators Who This Book Is For Business analysts, data analysts, BI professionals, end-users, executives, developers, as well as students in dashboards, data visualizations, and visual analytics courses.

One of the most influential data visualization books—updated with new techniques, technologies, and examples Visualize This demonstrates how to explain data visually, so that you can present and communicate information in a way that is appealing and easy to understand. Today, there is a continuous flow of data available to answer almost any question. Thoughtful charts, maps, and analysis can help us make sense of this data. But the data does not speak for itself. As leading data expert Nathan Yau explains in this book, graphics provide little value unless they are built upon a firm understanding of the data behind them. Visualize This teaches you a data-first approach from a practical point of view. You'll start by exploring what your data has to say, and then you'll design visualizations that are both remarkable and meaningful. With this book, you'll discover what tools are available to you without becoming overwhelmed with options. You'll be exposed to a variety of software and code and jump right into real-world datasets so that you can learn visualization by doing. You'll learn to ask and answer questions with data, so that you can make charts that are both beautiful and useful. Visualize This also provides you with opportunities to apply what you learn to your own data. This completely updated, full-color second edition: Presents a unique approach to visualizing and telling stories with data, from data visualization expert Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design Details tools that can be used to visualize data graphics for reports, presentations, and stories, for the web or for print, with major updates for the latest R packages, Python libraries, JavaScript libraries, illustration software, and point-and-click applications Contains numerous examples and descriptions of patterns and outliers and explains how to show them Information designers, analysts, journalists, statisticians, data scientists—as well as anyone studying for careers in these fields—will gain a valuable background in the concepts and techniques of data visualization, thanks to this legendary book.

This thoroughly updated and expanded second edition covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded

The problem of forecasting future values of economic and physical processes, the problem of restoring lost information, cleaning signals or other data observations from noise, is magnified in an information-laden word. Methods of stochastic processes estimation depend on two main factors. The first factor is construction of a model of the process being investigated. The second factor is the available information about the structure of the process under consideration. In this book, we propose results of the investigation of the problem of mean square optimal estimation (extrapolation, interpolation, and filtering) of linear functionals depending on unobserved values of stochastic sequences and processes with periodically stationary and long memory multiplicative seasonal increments. Formulas for calculating the mean square errors and the spectral characteristics of the optimal estimates of the functionals are derived in the case of spectral certainty, where spectral structure of the considered sequences and processes are exactly known. In the case where spectral densities of the sequences and processes are not known exactly while some sets of admissible spectral densities are given, we apply the minimax-robust method of estimation.

The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies. If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Explore ways that predictive analytics can provide direct input back to your business Understand mathematical tools commonly used in predictive analytics Learn the development frameworks used in predictive analytics applications Appreciate the role of predictive analytics in the machine learning process Examine industry implementations of predictive analytics Build, train, and retrain predictive models using Python and TensorFlow

This book is designed to meet the requirements of students and is intended for beginners to help them understand the concepts from the first principles. Spread across 16 chapters, it discusses the theoretical aspects that have been refined and updated to reflect the current developments in the subjects. It expounds on theoretical concepts that have immense practical applications, giving adequate proofs to establish significant theorems.

Book Contents –

Chapter 1: Probability Chapter 2: Random Variables (Discrete and Continuous) Chapter 3: Mathematical Expectation Chapter 4: Standard Discrete Distributions Chapter 5: Standard Continuous Distributions Chapter 6: Sampling Theory and Distribution Chapter 7: Testing of Hypothesis (Large Samples) Chapter 8: Test of Hypothesis (Small Samples) Chapter 9: Estimation Chapter 10: Curve Fitting Chapter 11: Correlation Chapter 12: Regression Chapter 13: Queuing Theory Chapter 14: Design of Experiments Chapter 15: Random Process Chapter 16: Advanced Random Process Appendix A, Appendix B, Appendix C, Appendix D Index

In today's data-driven world, understanding statistical models is crucial for effective analysis and decision making. Whether you're a beginner or an experienced user, this book equips you with the foundational knowledge to grasp and implement statistical models within Tableau. Gain the confidence to speak fluently about the models you employ, driving adoption of your insights and analysis across your organization. As AI continues to revolutionize industries, possessing the skills to leverage statistical models is no longer optional—it's a necessity. Stay ahead of the curve and harness the full potential of your data by mastering the ability to interpret and utilize the insights generated by these models. Whether you're a data enthusiast, analyst, or business professional, this book empowers you to navigate the ever-evolving landscape of data analytics with confidence and proficiency. Start your journey toward data mastery today. In this book, you will learn: The basics of foundational statistical modeling with Tableau How to prove your analysis is statistically significant How to calculate and interpret confidence intervals Best practices for incorporating statistics into data visualizations How to connect external analytics resources from Tableau using R and Python

Offering a pathway to vibrant organizations, this book integrates systems thinking, critical thinking, and design thinking, and provides the tools needed to proactively apply them in the social systems where we live and work.

Business Statistics, 3rd Edition 3rd Edition

Business Statistics: An Applied Orientation provides with a conceptual framework of business, develops skills in applying concepts into decision situations, and helps understand the nitty-gritty of business statistics. This book will also be useful to professionals who would like to acquire basic knowledge of business statistics that would help them analyze and interpret data.

Business Statistics offers readers a foundation in core statistical concepts using a perfect blend of theory and practical application. This book presents business statistics as value added tools in the process of converting data into useful information.

Valuable resource for researchers and postgraduate students from statistics, biostatistics, and other fields. It could be used as a textbook for a course on model-based clustering methods, and as a supplementary text for courses on data mining, semiparametric modeling, and high-dimensional data analysis.

This fully updated second edition is an essential introduction to inferential statistics. It is the first introductory statistics text to use an estimation approach with meta-analysis from the start and also to explain the new and exciting Open Science practices, which encourage replication and enhance the trustworthiness of research.

Microsoft 365 Excel introduces enhanced features that transform how business dashboards are built and maintained. This book guides you through creating dynamic, interactive dashboards that leverage these modern capabilities. From understanding the essential principles of effective dashboard design to mastering the latest tools like Power Query and dynamic array functions, you'll make the most of Excel's full potential. What this Book will help me do Understand the purpose and advantages of effective dashboards in business analytics. Use advanced Excel functions and tools such as Power Query and dynamic arrays to handle complex data workflows. Design visually engaging dashboards using charts and data visualizations that communicate key insights. Optimize dashboards for automation and real-time data updates, saving time and effort. Apply best practices and techniques for creating professional-grade Excel dashboards. Author(s) Michael Olafusi is a skilled data analyst and expert in Microsoft Excel, with years of experience leveraging Excel for business intelligence and analytics solutions. He enjoys teaching Excel users how to elevate their skills to create functional and visually impactful tools. Michael's approach combines clarity and practical advice, helping readers build proficiency and confidence. Who is it for? This book is perfect for Excel users who want to create professional dashboards for business decision support. It's especially useful for data analysts, financial analysts, business analysts, and those in similar roles. It requires a basic familiarity with Excel's interface and is ideal for those seeking to enhance their data presentation skills and automate repetitive reporting tasks.

Kibana 8.x - A Quick Start Guide to Data Analysis is an essential resource for anyone wanting to harness the robust capabilities of Kibana to analyze, visualize, and make sense of their data. Through clear explanations and practical exercises, this guide breaks down topics like creating dashboards, exploring datasets, and configuring Kibana's powerful features. What this Book will help me do Understand Kibana's interface and functionalities to manage Elasticsearch data. Learn how to create intuitive visualizations and customize dashboards. Explore features such as data discovery and real-time updates for analytics. Optimize and query datasets using ESQL and detailed analytics techniques. Master the process of embedding dashboards and exporting insights. Author(s) None Shah is an experienced data analytics professional with a deep understanding of the Elastic Stack, including Kibana and Elasticsearch. Having spent years working on big data projects, Shah is dedicated to helping technologists turn data into actionable insights. Her writing aims to simplify complex concepts into achievable learning milestones. Who is it for? This book is ideal for data analysts, data engineers, and anyone working extensively with Elasticsearch datasets. If you aim to gain hands-on experience with building interactive dashboards and visualizing data trends, this book is tailored for you. A foundational understanding of Elasticsearch would be beneficial but is not strictly required. Perfect for advancing decision-making with data insights.

If programming is magic, then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. This thoroughly updated third edition not only introduces you to web scraping but also serves as a comprehensive guide to scraping almost every type of data from the modern web. Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server's response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you're likely to encounter. Parse complicated HTML pages Develop crawlers with the Scrapy framework Learn methods to store the data you scrape Read and extract data from documents Clean and normalize badly formatted data Read and write natural languages Crawl through forms and logins Scrape JavaScript and crawl through APIs Use and write image-to-text software Avoid scraping traps and bot blockers Use scrapers to test your website

Entity resolution is a key analytic technique that enables you to identify multiple data records that refer to the same real-world entity. With this hands-on guide, product managers, data analysts, and data scientists will learn how to add value to data by cleansing, analyzing, and resolving datasets using open source Python libraries and cloud APIs. Author Michael Shearer shows you how to scale up your data matching processes and improve the accuracy of your reconciliations. You'll be able to remove duplicate entries within a single source and join disparate data sources together when common keys aren't available. Using real-world data examples, this book helps you gain practical understanding to accelerate the delivery of real business value. With entity resolution, you'll build rich and comprehensive data assets that reveal relationships for marketing and risk management purposes, key to harnessing the full potential of ML and AI. This book covers: Challenges in deduplicating and joining datasets Extracting, cleansing, and preparing datasets for matching Text matching algorithms to identify equivalent entities Techniques for deduplicating and joining datasets at scale Matching datasets containing persons and organizations Evaluating data matches Optimizing and tuning data matching algorithms Entity resolution using cloud APIs Matching using privacy-enhancing technologies