Analytics projects are frequently long, drawn-out affairs, requiring multiple teams and skills to clean, join, and eventually turn data into analysis for timely decision-making. Alteryx Designer changes all of that. With this low-code, self-service, drag-and-drop workflow platform, new and experienced data and business analysts can deliver results in hours instead of weeks. This practical book shows you how to master all areas of Alteryx Designer quickly. Author and Alteryx ACE Joshua Burkhow starts with the basics of building a workflow, then introduces more than 200 tools for working with intermediate and advanced analytics functionality. With Alteryx Designer's all-in-one toolkit, you'll migrate from legacy analytics software or Excel with ease. Ready to work with data quickly and efficiently? This guide gets you started. Learn the fundamentals of cleaning, prepping, and analyzing data with Alteryx Designer Install, navigate, and quickly become competent with the Alteryx Designer layout and functionality Construct accurate, performant, reliable, and well-documented workflows that automate business processes Learn intermediate techniques using spatial analytics, reporting, and in-database tools Dive into advanced Alteryx capabilities, including predictive and machine learning tools Get introduced to the entire Alteryx Analytic Process Automation (APA) Platform

Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining and analytics. Users will find the authors’ research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data. The book's authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. In addition, a number of descriptive learning tasks are included. Presents the foundational concepts of data science along with advanced concepts and real-life applications for applied learning Includes coverage of a number of key topics such as data quality and pre-processing, proximity and validation, predictive data science, descriptive data science, ensemble learning, association rule mining, Big Data analytics, as well as incremental and distributed learning Provides updates on key applications of data science techniques in areas such as Computational Biology, Network Intrusion Detection, Natural Language Processing, Software Clone Detection, Financial Data Analysis, and Scientific Time Series Data Analysis Covers computer program code for implementing descriptive and predictive algorithms

This book is your practical and comprehensive guide to learning Google Cloud Platform (GCP) for data science, using only the free tier services offered by the platform. Data science and machine learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications. GCP offers a range of data science services that can be used to store, process, and analyze large datasets, and train and deploy machine learning models. The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics. Each chapter provides step-by-step instructions and examples illustrating how to use GCP services for data science and big data projects. Readers will learn how to set up a Google Colaboratory account and run Jupyternotebooks, access GCP services and data from Colaboratory, use BigQuery for data analytics, and deploy machine learning models using Vertex AI. The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL. What You Will Learn Set up a GCP account and project Explore BigQuery and its use cases, including machine learning Understand Google Cloud AI Platform and its capabilities Use Vertex AI for training and deploying machine learning models Explore Google Cloud Dataproc and its use cases for big data processing Create and share data visualizations and reports with Looker Data Studio Explore Google Cloud Dataflow and its use cases for batch and stream data processing Run data processing pipelines on Cloud Dataflow Explore Google Cloud Storageand its use cases for data storage Get an introduction to Google Cloud SQL and its use cases for relational databases Get an introduction to Google Cloud Pub/Sub and its use cases for real-time data streaming Who This Book Is For Data scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their data science and big data projects

The 2nd edition of Consumer Behaviour and Analytics provides a consumer behaviour textbook for the new marketing reality. In a world of Big Data, machine learning and artificial intelligence, this key text reviews the issues, research and concepts essential for navigating this new terrain.

A step-by-step guide for business leaders who need to manage successful big data projects Leading in Analytics: The Critical Tasks for Executives to Master in the Age of Big Data takes you through the entire process of guiding an analytics initiative from inception to execution. You’ll learn which aspects of the project to pay attention to, the right questions to ask, and how to keep the project team focused on its mission to produce relevant and valuable project. As an executive, you can’t control every aspect of the process. But if you focus on high-impact factors that you can control, you can ensure an effective outcome. This book describes those factors and offers practical insight on how to get them right. Drawn from best-practice research in the field of analytics, the Manageable Tasks described in this book are specific to the goal of implementing big data tools at an enterprise level. A dream team of analytics and business experts have contributed their knowledge to show you how to choose the right business problem to address, put together the right team, gather the right data, select the right tools, and execute your strategic plan to produce an actionable result. Become an analytics-savvy executive with this valuable book. Ensure the success of analytics initiatives, maximize ROI, and draw value from big data Learn to define success and failure in analytics and big data projects Set your organization up for analytics success by identifying problems that have big data solutions Bring together the people, the tools, and the strategies that are right for the job By learning to pay attention to critical tasks in every analytics project, non-technical executives and strategic planners can guide their organizations to measurable results.

This book, Alteryx Designer Cookbook, provides over 60 practical and detailed recipes that guide you in conquering data accessibility, preparation, and insights generation through Alteryx Designer. You will learn how to manipulate, blend, and analyze data sources effectively, improving your analytical productivity. What this Book will help me do Master efficient methods for cleaning, preparing, and shaping data accurately. Combine multiple data sources seamlessly using Alteryx Designer's blending tools. Implement essential data transformations such as pivoting and restructuring for analyses. Create reusable, automated solutions for repeated tasks using Alteryx macros. Generate rich, data-driven reports to enhance business intelligence efficiently. Author(s) None Guisande is an experienced data analytics professional with years of hands-on expertise in implementing data workflows using Alteryx Designer. Passionate about simplifying complex operations, None brings a practical approach to teaching, ensuring that readers can apply their skills immediately. Who is it for? This book is ideal for data analysts, professionals in business intelligence, and anyone proficient in Alteryx Designer's basics looking to deepen their understanding. If you aim to enhance your productivity and manual data tasks into efficient automated workflows, this book is a perfect fit.

Discover the utility of your organization’s data with Microsoft Power BI In Beginning Power BI for Business Users: Learning to Turn Data into Insights, accomplished data professional and business intelligence expert Paul Fuller delivers an intuitive and accessible handbook for professionals seeking to use Microsoft’s Power BI to access, analyze, understand, report, and act on the data available to their organizations. In the book, you’ll discover Power BI’s robust feature set, learn to ingest and model data, visualize and report on that data, and even use the DAX scripting language to unlock still more utility from Microsoft’s popular program. Beginning with general principles geared to readers with no or little experience with reporting or data analytics tools, the author walks you through how to manipulate common, publicly available data sources—including Excel files and relational databases. You’ll also learn to: Use the included and tested sample code to work through the helpful examples included by the author Conduct data orchestration and visualization to better understand and gain insights from your data An essential resource for business analysts and Excel power users reaching the limits of that program’s capabilities, Beginning Power BI for Business Users will also benefit data analysts who seek to prepare reports for their organizations using Microsoft’s flexible and intuitive software.

Machine Learning with Qlik Sense introduces practical applications of machine learning within the Qlik platform. Through this book, you will gain a thorough understanding of fundamental ML concepts, learn to apply these within Qlik Sense, and see how to use predictive analytics to solve real-world problems. The hands-on examples ensure you can translate learnings into actionable insights. What this Book will help me do Understand the key principles of machine learning and how to apply them using the Qlik platform. Develop skills in data preprocessing and analysis to prepare datasets for machine learning models. Learn to validate and interpret machine learning models and evaluate their performance. Master advanced visualization techniques for presenting insights derived from data. Apply newfound knowledge to practical business problems through real-world use-case examples. Author(s) Hannu Ranta is an expert in data analytics and has extensive experience utilizing the Qlik platform to derive actionable insights from data. With years of practical exposure and a focus on teaching, Hannu brings a clear and structured approach to using machine learning for analytics. His writing seeks to empower readers to achieve practical solutions using Qlik's powerful tools. Who is it for? This book is perfect for data analysts, data scientists, or anyone working in data analytics who wants to incorporate machine learning into their skill set. It is especially suited to those with a basic familiarity with Qlik tools or data analysis concepts. Beginners in machine learning can also benefit because the book starts from foundational concepts and builds step-by-step.

Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This third edition is fully updated for the latest version of Python and its related libraries, and includes coverage of social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulation Author Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Third Edition is an invaluable reference with its examples of storing, accessing, and analyzing data. What You'll Learn Understand the core concepts of data analysis and the Python ecosystem Go in depth with pandas for reading, writing, and processing data Use tools and techniques for data visualization and image analysis Examine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorch Who This Book Is For Experienced Python developers who need to learn about Pythonic tools for data analysis

This comprehensive book on Tableau 2023 is your practical guide to mastering data visualization and business intelligence techniques. You will explore the latest features of Tableau, learn how to create insightful dashboards, and gain proficiency in integrating analytics and machine learning workflows. By the end, you'll have the skills to address a variety of analytics challenges using Tableau. What this Book will help me do Master the latest Tableau 2023 features and use cases to tackle analytics challenges. Develop and implement ETL workflows using Tableau Prep Builder for optimized data preparation. Integrate Tableau with programming languages such as Python and R to enhance analytics. Create engaging, visually impactful dashboards for effective data storytelling. Understand and apply data governance to ensure data quality and compliance. Author(s) Marleen Meier is an experienced data visualization expert and Tableau consultant with over a decade of experience helping organizations transform data into actionable insights. Her approach integrates her technical expertise and a keen eye for design to make analytics accessible rather than overwhelming. Her passion for teaching others to use visualization tools effectively shines through in her writing. Who is it for? This book is ideal for business analysts, BI professionals, or data analysts looking to enhance their Tableau expertise. It caters to both newcomers seeking to understand the foundations of Tableau and experienced users aiming to refine their skills in advanced analytics and data visualization. If your goal is to leverage Tableau as a strategic tool in your organization's BI projects, this book is for you.

Explore the comprehensive world of UX analytics with "Unleashing the Power of UX Analytics." This book uncovers proven techniques to collect, analyze, and interpret crucial data for enhancing user experiences. Through practical insights and methodologies, you'll master the art of creating empathetic, data-informed designs that meet user needs effectively. What this Book will help me do Master the techniques for effective qualitative and quantitative user data analysis. Learn to implement and interpret key UX metrics and KPIs to guide design processes. Understand and apply design thinking to bridge user goals with business objectives. Discover how to utilize and integrate UX analytics tools and methodologies. Learn strategies for presenting research findings and making impactful data-driven recommendations. Author(s) Jeff Hendrickson is a seasoned UX designer, researcher, and analytics expert with over a decade of experience in the field. His approach emphasizes empathy and user needs in driving design solutions, combining his background in psychology and data analysis. Jeff's ability to translate complex analytics into actionable insights makes his writing relatable and pragmatic for professionals. Who is it for? This book is perfect for UX researchers, product managers, and designers who aim to deepen their understanding of UX analytics. Whether you're an experienced professional or just beginning your journey, you'll gain the tools to make informed, user-centric decisions. If you're eager to enhance your design impact through analytics, this book is for you.

Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work. Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need. Learn the core principles and benefits of data observability Use data observability to detect, troubleshoot, and prevent data issues Follow the book's recipes to implement observability in your data projects Use data observability to create a trustworthy communication framework with data consumers Learn how to educate your peers about the benefits of data observability

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.

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.

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.

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! ...

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.

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.

Over the past 10 years, data analytics and data visualization have become essential components of an enterprise information strategy. And yet, the adoption of data analytics has remained remarkably static, reaching no more than 30% of potential users. This book explores the most important techniques for taking that adoption further: embedding analytics into the workflow of our everyday operations. Authors Donald Farmer and Jim Horbury show business users how to improve decision making without becoming analytics specialists. You'll explore different techniques for exchanging data, insights, and events between analytics platforms and hosting applications. You'll also examine issues including data governance and regulatory compliance and learn best practices for deploying and managing embedded analytics at scale. Learn how data analytics improves business decision making and performance Explore advantages and disadvantages of different embedded analytics platforms Develop a strategy for embedded analytics in an organization or product Define the architecture of an embedded solution Select vendors, platforms, and tools to implement your architecture Hire or train developers and architects to build the embedded solutions you need Understand how embedded analytics interacts with traditional analytics

Welcome to this step-by-step guide for Excel users, data analysts, and finance specialists. It is designed to take you through practical report and development scenarios, including both the approach and the technical challenges. This book will equip you with an understanding of the overall Power Platform use case for addressing common business challenges. While Power BI continues to be an excellent tool of choice in the BI space, Power Platform is the real game changer. Using an integrated architecture, a small team of citizen developers can build solutions for all kinds of business problems. For small businesses, Power Platform can be used to build bespoke CRM, Finance, and Warehouse management tools. For large businesses, it can be used to build an integration point for existing systems to simplify reporting, operation, and approval processes. The author has drawn on his15 years of hands-on analytics experience to help you pivot from the traditional Excel-based reporting environment. By using different business scenarios, this book provides you with clear reasons why a skill is important before you start to dive into the scenarios. You will use a fast prototyping approach to continue to build exciting reporting, automation, and application solutions and improve them while you acquire new skill sets. The book helps you get started quickly with Power BI. It covers data visualization, collaboration, and governance practices. You will learn about the most practical SQL challenges. And you will learn how to build applications in PowerApps and Power Automate. The book ends with an integrated solution framework that can be adapted to solve a wide range of complex business problems. What You Will Learn Develop reporting solutions and business applications Understand the Power Platform licensing and development environment Apply Data ETL and modeling in Power BI Use Data Storytelling and dashboard design to better visualize data Carry out data operations with SQL and SharePoint lists Develop useful applications using Power Apps Develop automated workflows using Power Automate Integrate solutions with Power BI, Power Apps, and Power Automate to build enterprise solutions Who This Book Is For Next-generation data specialists, including Excel-based users who want to learn Power BI and build internal apps; finance specialists who want to take a different approach to traditional accounting reports; and anyone who wants to enhance their skill set for the future job market.

Expert Data Modeling with Power BI, Second Edition, serves as your comprehensive guide to mastering data modeling using Power BI. With clear explanations, actionable examples, and a focus on hands-on learning, this book takes you through the concepts and advanced techniques that will enable you to build high-performing data models tailored to real-world requirements. What this Book will help me do Master time intelligence and virtual tables in DAX to enhance your data models. Understand best practices for creating efficient Star Schemas and preparing data in Power Query. Deploy advanced modeling techniques such as calculation groups, aggregations, and incremental refresh. Manage complex data models and streamline them to improve performance. Leverage data marts and data flows within Power BI for modularity and scalability. Author(s) Soheil Bakhshi is a seasoned expert in data visualization and analytics with extensive experience in leveraging Power BI for business intelligence solutions. Passionate about educating others, he combines practical insights and technical knowledge to make learning accessible and effective. His approachable writing style reflects his commitment to helping readers succeed. Who is it for? This book is ideal for business intelligence professionals, data analysts, or report developers with basic knowledge of Power BI and experience with Star Schema concepts. Whether you're looking to refine your data modeling skills or expand your expertise in advanced features, this guide aims to help you achieve your goals efficiently.

This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You’ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy. Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics. What You Will Learn Master the mathematical foundations required for business analytics Understand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given task Use R and Python to develop descriptive models, predictive models, and optimize models Interpret and recommend actions based on analytical model outcomes Who This Book Is For Software professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.

"Applied Geospatial Data Science with Python" introduces readers to the power of integrating geospatial data into data science workflows. This book equips you with practical methods for processing, analyzing, and visualizing spatial data to solve real-world problems. Through hands-on examples and clear, actionable advice, you will master the art of spatial data analysis using Python. What this Book will help me do Learn to process, analyze, and visualize geospatial data using Python libraries. Develop a foundational understanding of GIS and geospatial data science principles. Gain skills in building geospatial AI and machine learning models for specific use cases. Apply geospatial data workflows to practical scenarios like optimization and clustering. Create a portfolio of geospatial data science projects relevant across different industries. Author(s) David S. Jordan is an experienced data scientist with years of expertise in GIS and geospatial analytics. With a passion for making complex topics accessible, David leverages his deep technical knowledge to provide practical, hands-on instruction. His approach emphasizes real-world applications and encourages learners to develop confidence as they work with geospatial data. Who is it for? This book is perfect for data scientists looking to integrate geospatial data analysis into their existing workflows, and GIS professionals seeking to expand into data science. If you already have a basic knowledge of Python for data analysis or data science and want to explore how to work effectively with geospatial data to drive impactful solutions, this is the book for you.

Data Wrangling with R guides you through mastering data preparation in the R programming language using tidyverse libraries. You will learn techniques to load, explore, transform, and visualize data effectively, gaining the skills needed for data modeling and insights extraction. What this Book will help me do Understand how to use R and tidyverse libraries to handle data wrangling tasks. Learn methods to work with diverse data types like numbers, strings, and dates. Gain proficiency in building visual representations of data using ggplot2. Build and validate your first predictive model for useful insights. Create an interactive web application with Shiny in R. Author(s) Gustavo Santos is an experienced data scientist specializing in R programming and data visualization. With a background in statistics and several years of professional experience in industry and academia, Gustavo excels at translating complex data analytics concepts into practical skills. His approach to teaching is hands-on and example-driven, aiming to empower readers to excel in real-world applications. Who is it for? If you are a data scientist, data analyst, or even a beginner programmer who wants to enhance their data manipulation and visualization skills, this book is perfect for you. Familiarity with R or a general understanding of programming concepts is suggested but not mandatory. It caters to professionals looking to refine their data wrangling workflow and to students aspiring to break into data-centered fields. By the end, you'll be ready to apply data wrangling and visualization tools in your projects.