Modern systems contain multi-core CPUs and GPUs that have the potential for parallel computing. But many scientific Python tools were not designed to leverage this parallelism. With this short but thorough resource, data scientists and Python programmers will learn how the Dask open source library for parallel computing provides APIs that make it easy to parallelize PyData libraries including NumPy, pandas, and scikit-learn. Authors Holden Karau and Mika Kimmins show you how to use Dask computations in local systems and then scale to the cloud for heavier workloads. This practical book explains why Dask is popular among industry experts and academics and is used by organizations that include Walmart, Capital One, Harvard Medical School, and NASA. With this book, you'll learn: What Dask is, where you can use it, and how it compares with other tools How to use Dask for batch data parallel processing Key distributed system concepts for working with Dask Methods for using Dask with higher-level APIs and building blocks How to work with integrated libraries such as scikit-learn, pandas, and PyTorch How to use Dask with GPUs

How can you maximize data collaboration across your organization without having to build integrations between individual applications, systems, and other data sources? Data collaboration architectures that don't depend on integrations aren't a new idea, but they've assumed greater urgency as organizations increasingly struggle to manage the ever-growing numbers of data sources that exist inside their IT estates. In this report, Cinchy cofounders Dan DeMers and Karanjot Jaswal show CIOs, CTOs, CDOs, and other IT leaders how to rethink their organization's approach to data architectures, data management, and data governance. You'll learn about different approaches to creating data platforms that liberate and autonomize data, enable agile data management, apply consistent data access controls, and maximize visibility without requiring application-specific integrations. With this report, you'll discover: Why data integration is often handled piecemeal—combining one app with another rather than integrating all apps together How data collaboration platforms enable data sharing across all apps, systems, and sources without application-specific integrations Four major platforms you can use to make data available to all applications and services: Cinchy, K2View, Microsoft Dataverse, and The Modern Data Company Principles and practices for deploying the data collaboration platform of your choice Dan DeMers is the CEO and cofounder of Cinchy. Karanjot Jaswal is cofounder and CTO of Cinchy.

The explosive growth in volume and varieties of data generated by the seemingly endless arrays of digital systems and applications is rapidly elevating the importance of being able to utilize data; in fact, data analytic literacy is becoming as important now, at the onset of the Digital Era, as rudimentary literacy and numeracy were throughout the Industrial Era. And yet, what constitutes data analytic literacy is poorly understood. To some, data analytic literacy is the ability to use basic statistics, to others it is data science ‘light’, and to still others it is just general familiarity with common data analytic outcomes. Exploring the scope and the structure of rudimentary data analytic competencies is at the core of this book which takes the perspective that data analytics is a new and distinct domain of knowledge and practice. It offers application-minded framing of rudimentary data analytic competencies built around conceptually sound and practically meaningful processes and mechanics of systematically transforming messy and heterogeneous data into informative insights. Data Analytic Literacy is meant to offer an easy-to-follow overview of the critical elements of the reasoning behind basic data manipulation and analysis approaches and steps, coupled with the commonly used data analytic and data communication techniques and tools. It offers an all-inclusive guide to developing basic data analytic competencies.

This book provides detailed guidance around architecting and deploying Power BI reporting solutions, including help and best practices for sharing and security. You’ll find chapters on dataflows, shared datasets, composite model and DirectQuery connections to Power BI datasets, deployment pipelines, XMLA endpoints, and many other important features related to the overall Power BI architecture that are new since the first edition. You will gain an understanding of what functionality each of the Power BI components provide (such as Dataflow, Shared Dataset, Datamart, thin reports, and paginated reports), so that you can make an informed decision about what components to use in your solution. You will get to know the pros and cons of each component, and how they all work together within the larger Power BI architecture. Commonly encountered problems you will learn to handle include content unexpectedly changing while users are in the process of creating reports and building analyses, methods of sharing analyses that don’t cover all the requirements of your business or organization, and inconsistent security models. Detailed examples help you to understand and choose from among the different methods available for sharing and securing Power BI content so that only intended recipients can see it. The knowledge provided in this book will allow you to choose an architecture and deployment model that suits the needs of your organization. It will also help ensure that you do not spend your time maintaining your solution, but on using it for its intended purpose: gaining business value from mining and analyzing your organization’s data. What You Will Learn Architect Power BI solutions that are reliable and easy to maintain Create development templates and structures in support of reusability Set up and configure the Power BI gateway as a bridge between on-premises data sourcesand the Power BI cloud service Select a suitable connection type—Live Connection, DirectQuery, Scheduled Refresh, or Composite Model—for your use case Choose the right sharing method for how you are using Power BI in your organization Create and manage environments for development, testing, and production Secure your data using row-level and object-level security Save money by choosing the right licensing plan Who This Book Is For Data analysts and developers who are building reporting solutions around Power BI, as well as architects and managers who are responsible for the big picture of how Power BI meshes with an organization’s other systems, including database and data warehouse systems.

DATA WRANGLING Written and edited by some of the world’s top experts in the field, this exciting new volume provides state-of-the-art research and latest technological breakthroughs in data wrangling, its theoretical concepts, practical applications, and tools for solving everyday problems. Data wrangling is the process of cleaning and unifying messy and complex data sets for easy access and analysis. This process typically includes manually converting and mapping data from one raw form into another format to allow for more convenient consumption and organization of the data. Data wrangling is increasingly ubiquitous at today’s top firms. Data cleaning focuses on removing inaccurate data from your data set whereas data wrangling focuses on transforming the data’s format, typically by converting “raw” data into another format more suitable for use. Data wrangling is a necessary component of any business. Data wrangling solutions are specifically designed and architected to handle diverse, complex data at any scale, including many applications, such as Datameer, Infogix, Paxata, Talend, Tamr, TMMData, and Trifacta. This book synthesizes the processes of data wrangling into a comprehensive overview, with a strong focus on recent and rapidly evolving agile analytic processes in data-driven enterprises, for businesses and other enterprises to use to find solutions for their everyday problems and practical applications. Whether for the veteran engineer, scientist, or other industry professional, this book is a must have for any library.

The concept of deep machine learning is easier to understand by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the cycle-to-cycle variations.

Next-Generation Sequencing Data Analysis walks readers through NGS data analysis step-by-step for a wide range of NGS applications.

Practice making sense of data with IBM’s SPSS Statistics software SPSS Statistics Workbook For Dummies gives you the practice you need to navigate the leading statistical software suite. Data management and analysis, advanced analytics, business intelligence—SPSS is a powerhouse of a research platform, and this book helps you master the fundamentals and analyze data more effectively. You’ll work through practice problems that help you understand the calculations you need to perform, complete predictive analyses, and produce informative graphs. This workbook gives you hands-on exercises to hone your statistical analysis skills with SPSS Statistics 28. Plus, explanations and insider tips help you navigate the software with ease. Practical and easy-to-understand, in classic Dummies style. Practice organizing, analyzing, and graphing data Learn to write, edit, and format SPSS syntax Explore the upgrades and features new to SPSS 28 Try your hand at advanced data analysis procedures For academics using SPSS for research, business analysts and market researchers looking to extract valuable insights from data, and anyone with a hankering for more stats practice.

Lead your demand planning process to excellence and deliver real value to your supply chain. In Demand Forecasting Best Practices you’ll learn how to: Lead your team to improve quality while reducing workload Properly define the objectives and granularity of your demand planning Use intelligent KPIs to track accuracy and bias Identify areas for process improvement Help planners and stakeholders add value Determine relevant data to collect and how best to collect it Utilize different statistical and machine learning models An expert demand forecaster can help an organization avoid overproduction, reduce waste, and optimize inventory levels for a real competitive advantage. Demand Forecasting Best Practices teaches you how to become that virtuoso demand forecaster. This one-of-a-kind guide reveals forecasting tools, metrics, models, and stakeholder management techniques for delivering more effective supply chains. Everything you learn has been proven and tested in a live business environment. Discover author Nicolas Vandeput’s original five step framework for demand planning excellence and learn how to tailor it to your own company’s needs. Illustrations and real-world examples make each concept easy to understand and easy to follow. You’ll soon be delivering accurate predictions that are driving major business value. About the Technology An expert demand forecaster can help an organization avoid overproduction, reduce waste, and optimize inventory levels for a real competitive advantage. This book teaches you how to become that virtuoso demand forecaster. About the Book Demand Forecasting Best Practices reveals forecasting tools, metrics, models, and stakeholder management techniques for managing your demand planning process efficiently and effectively. Everything you learn has been proven and tested in a live business environment. Discover author Nicolas Vandeput’s original five step framework for demand planning excellence and learn how to tailor it to your own company’s needs. Illustrations and real-world examples make each concept easy to understand and easy to follow. You’ll soon be delivering accurate predictions that are driving major business value. What's Inside Enhance forecasting quality while reducing team workload Utilize intelligent KPIs to track accuracy and bias Identify process areas for improvement Assist stakeholders in sales, marketing, and finance Optimize statistical and machine learning models About the Reader For demand planners, sales and operations managers, supply chain leaders, and data scientists. About the Author Nicolas Vandeput is a supply chain data scientist, the founder of consultancy company SupChains in 2016, and a teacher at CentraleSupélec, France. Quotes This new book continues to push the FVA mindset, illustrating practices that drive the efficiency and effectiveness of the business forecasting process. - Michael Gilliland, Editor-in-Chief, Foresight: Journal of Applied Forecasting A must-read for any SCM professional, data scientist, or business owner. It's practical, accessible, and packed with valuable insights. - Edouard Thieuleux, Founder of AbcSupplyChain An exceptional resource that covers everything from basic forecasting principles to advanced forecasting techniques using artificial intelligence and machine learning. The writing style is engaging, making complex concepts accessible to both beginners and experts. - Daniel Stanton, Mr. Supply Chain® Nicolas did it again! Demand Forecasting Best Practices provides practical and actionable advice for improving the demand planning process. - Professor Spyros Makridakis, The Makridakis Open Forecasting Center, Institute For the Future (IFF), University of Nicosia This book is now my companion on all of our planning and forecasting projects. A perfect foundation for implementation and also to recommend process improvements. - Werner Nindl, Chief Architect – CPM Practice Director, Pivotal Drive This author understands the nuances of forecasting, and is able to explain them well. - Burhan Ul Haq, Director of Products, Enablers Both broader and deeper than I expected. - Maxim Volgin, Quantitative Marketing Manager, KLM Great book with actionable insights. - Simon Tschöke, Head of Research, German Edge Cloud

Dive into the exciting world of data science with this practical introduction. Packed with essential skills and useful examples, Dive Into Data Science will show you how to obtain, analyze, and visualize data so you can leverage its power to solve common business challenges. With only a basic understanding of Python and high school math, you’ll be able to effortlessly work through the book and start implementing data science in your day-to-day work. From improving a bike sharing company to extracting data from websites and creating recommendation systems, you’ll discover how to find and use data-driven solutions to make business decisions. Topics covered include conducting exploratory data analysis, running A/B tests, performing binary classification using logistic regression models, and using machine learning algorithms. You’ll also learn how to: •Forecast consumer demand •Optimize marketing campaigns •Reduce customer attrition •Predict website traffic •Build recommendation systems With this practical guide at your fingertips, harness the power of programming, mathematical theory, and good old common sense to find data-driven solutions that make a difference. Don’t wait; dive right in!

Understand the essentials of people analytics and learn how to find, collect, analyse and apply data to add business value with the second edition of this guide.

A companion to Remote Sensing and Digital Image Processing with R, this lab manual covers examples of natural resource data analysis applications including practical, problem-solving exercises and case studies that use the free and open-source platform R.

Time series data is at the heart of many applications, from finance and system monitoring to weather forecasting and medical data analysis. "Time Series Indexing" offers a hands-on guide to implementing and leveraging the iSAX indexing technique in Python to efficiently manage, search, and analyze time series data. What this Book will help me do Gain the know-how to implement algorithms like SAX and iSAX with illustrative Python examples. Learn to construct robust time series indexes tailored to real-world data sets. Understand the theoretical underpinnings of time series processing and indexing techniques. Explore and employ visualization techniques to interpret time series structures and insights. Gain the skills to adapt iSAX methodologies to other programming environments and practices. Author(s) Mihalis Tsoukalos is an accomplished developer and author specializing in Python programming and data processing techniques. With years of experience translating complex academic research into practical applications, Mihalis excels at bridging the gap between theory and practice. His writing approach ensures readers grasp both the foundational principles and the hands-on methods needed to succeed. Who is it for? This book best suits researchers, analysts, and developers who work with time series data and seek to elevate their proficiency in indexing and managing such data. It is perfect for professionals with a foundational knowledge of Python and programming concepts. This material also supports learners eager to derive actionable insights from theory-heavy academic research.

Master the Fundamentals of Modern Visual Analytics--and Craft Compelling Visual Narratives in Tableau! Do you need to persuade or inform people? Do you have data? Then you need to master visual analytics and visual storytelling. Today, the #1 tool for telling visual stories with data is Tableau, and demand for Tableau skills is soaring. In Visual Analytics Fundamentals, renowned visual storyteller and analytics professor Lindy Ryan introduces all the fundamental visual analytics knowledge, cognitive and perceptual concepts, and hands-on Tableau techniques you'll need. Ryan puts core analytics and visual concepts upfront, so you'll always know exactly what you're trying to accomplish and can apply this knowledge with any tool. Building on this foundation, she presents classroom-proven guided exercises for translating ideas into reality with Tableau 2022. Youll learn how to organize data and structure analysis with stories in mind, embrace exploration and visual discovery, and articulate your findings with rich data, well-curated visualizations, and skillfully crafted narrative frameworks. Ryan's insider tips take you far beyond the basics--and you'll rely on her expert checklists for years to come. Communicate more powerfully by applying scientific knowledge of the human brain Get started with the Tableau platform and Tableau Desktop 2022 Connect data and quickly prepare it for analysis Ask questions that help you keep data firmly in context Choose the right charts, graphs, and maps for each project--and avoid the wrong ones Craft storyboards that reflect your message and audience Direct attention to what matters most Build data dashboards that guide people towards meaningful outcomes Master advanced visualizations, including timelines, Likert scales, and lollipop charts This book has only one prerequisite: your desire to communicate insights from data in ways that are memorable and actionable. It's for executives and professionals sharing important results, students writing reports or presentations, teachers cultivating data literacy, journalists making sense of complex trends. . . . practically everyone! Don't even have Tableau? Download your free trial of Tableau Desktop and let's get started! ...

Digital Pricing Strategy provides a best practice overview of how companies design, analyse and execute digital pricing strategies.

Incredibly useful, knowledge graphs help organizations keep track of medical research, cybersecurity threat intelligence, GDPR compliance, web user engagement, and much more. They do so by storing interlinked descriptions of entities—objects, events, situations, or abstract concepts—and encoding the underlying information. How do you create a knowledge graph? And how do you move it from theory into production? Using hands-on examples, this practical book shows data scientists and data engineers how to build their own knowledge graphs. Authors Jesús Barrasa and Jim Webber from Neo4j illustrate common patterns for building knowledge graphs that solve many of today's pressing knowledge management problems. You'll quickly discover how these graphs become increasingly useful as you add data and augment them with algorithms and machine learning. Learn the organizing principles necessary to build a knowledge graph Explore how graph databases serve as a foundation for knowledge graphs Understand how to import structured and unstructured data into your graph Follow examples to build integration-and-search knowledge graphs Learn what pattern detection knowledge graphs help you accomplish Explore dependency knowledge graphs through examples Use examples of natural language knowledge graphs and chatbots Use graph algorithms and ML to gain insight into connected data

This volume provides a roadmap for the deployment of semantic technologies in the field of data science and enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame.

Discover how visualization turns data into action Tableau gives you the power to understand your data and put it in a format that is appealing and meaningful for everyone who needs to see it. Tableau For Dummies walks you through the steps to turn your data into a story that inspires action. This easy-to-understand guide offers insights from an enterprise data pro on how to transform data into a clear and memorable visual presentation. Navigate the Tableau user interface and connect to data sources Use drag-and-drop features to create stunning visualizations Work with templates, add graphs, and create clear charts Export your visualizations to multiple formats for easy sharing This is the perfect Dummies software guide for business professionals who need to better derive value from that all-important data.

Turn your R code into packages that others can easily install and use. With this fully updated edition, developers and data scientists will learn how to bundle reusable R functions, sample data, and documentation together by applying the package development philosophy used by the team that maintains the "tidyverse" suite of packages. In the process, you'll learn how to automate common development tasks using a set of R packages, including devtools, usethis, testthat, and roxygen2. Authors Hadley Wickham and Jennifer Bryan from Posit (formerly known as RStudio) help you create packages quickly, then teach you how to get better over time. You'll be able to focus on what you want your package to do as you progressively develop greater mastery of the structure of a package. With this book, you will: Learn the key components of an R package, including code, documentation, and tests Streamline your development process with devtools and the RStudio IDE Get tips on effective habits such as organizing functions into files Get caught up on important new features in the devtools ecosystem Learn about the art and science of unit testing, using features in the third edition of testthat Turn your existing documentation into a beautiful and user friendly website with pkgdown Gain an appreciation of the benefits of modern code hosting platforms, such as GitHub

Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverse—a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way. You'll understand how to: Visualize: Create plots for data exploration and communication of results Transform: Discover variable types and the tools to work with them Import: Get data into R and in a form convenient for analysis Program: Learn R tools for solving data problems with greater clarity and ease Communicate: Integrate prose, code, and results with Quarto

In Industry 4.0, intelligent analytics has a broader scope in terms of descriptive, predictive, and prescriptive sub-domains. To this end, the book will aim to review and highlight the challenges faced by Intelligent Analytics in Industry 4.0 and present the recent developments done to address those challenges.

Learn to use analytics more effectively, convert data into value and facilitate smarter and better decision-making for your organization.

Upgrade your career and organization with this handbook offering expert guidance on the business analysis role, supported by tools and templates.

Microsoft Power BI Machine Learning and OpenAI offers a comprehensive exploration into advanced data analytics and artificial intelligence using Microsoft Power BI. Through hands-on, workshop-style examples, readers will discover the integration of machine learning models and OpenAI features to enhance business intelligence. This book provides practical examples, real-world scenarios, and step-by-step guidance. What this Book will help me do Learn to apply machine learning capabilities within Power BI to create predictive analytics Understand how to integrate OpenAI services to build enhanced analytics workflows Gain hands-on experience in using R and Python for advanced data visualization in Power BI Master the skills needed to build and deploy SaaS auto ML models within Power BI Leverage Power BI's AI visuals and features to elevate data storytelling Author(s) Greg Beaumont, an expert in data science and business intelligence, brings years of experience in Power BI and analytics to this book. With a focus on practical applications, Greg empowers readers to harness the power of AI and machine learning to elevate their data solutions. As a consultant and trainer, he shares his deep knowledge to help readers unlock the full potential of their tools. Who is it for? This book is ideal for data analysts, BI professionals, and data scientists who aim to integrate machine learning and OpenAI into their workflows. If you're familiar with Power BI's fundamentals and are eager to explore its advanced capabilities, this guide is tailored for you. Perfect for professionals looking to elevate their analytics to a new level, combining data science concepts with Power BI's features.

Survey Development: A Theory-Driven Mixed Methods Approach provides both an overview of standard methods and tools for developing and validating surveys and a conceptual basis for survey development.