Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well. Source code is available at github.com/Apress/beg-data-science-r4. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language.

Discover how to leverage the full potential of Domo, a robust cloud-based business intelligence platform, in your organization. This comprehensive guide walks you through data integration, transformation, visualization, and governance techniques, enabling you to deliver impactful, data-driven results quickly and effectively. What this Book will help me do Understand and utilize Domo's cloud data architecture for comprehensive data analysis. Seamlessly acquire and manage data using Domo connectors and tools. Create and customize dashboards that communicate data insights effectively. Build and deploy Python applications and machine learning models on Domo. Securely govern your organization's data with robust Domo features. Author(s) The author, None Burtenshaw, is an expert in business intelligence and data platforms. With years of experience working with data integration tools, their writing combines technical thoroughness with practical insights. They aim to empower professionals with the skills to excel in data-driven decision making, reflecting their passion for making technology accessible and actionable. Who is it for? This book is ideal for business intelligence professionals, including developers and analysts, looking to elevate their understanding of Domo. It is suited for those with a fundamental knowledge of data platforms seeking advanced skills in data management and visualization. BI managers will gain insights into governance and security, while analysts will find inspiration for data storytelling. If you're aiming to master the possibilities of Domo, this book is for you.

The Pandas Workshop offers a detailed journey into the world of data analysis using Python and the pandas library. Throughout the book, you'll build skills in accessing, transforming, visualizing, and modeling data, all while focusing on real-world data science challenges. You will gain the knowledge and confidence needed to dissect and derive insights from complex datasets. What this Book will help me do Understand how to access and load data from various formats including databases and web-based sources. Manipulate and transform data for analysis using efficient pandas techniques. Create insightful visualizations using Matplotlib integrated with pandas for clearer data presentation. Build predictive and descriptive data models and glean data-driven insights. Handle and analyze time-series data to uncover trends and seasonal effects in data patterns. Author(s) Blaine Bateman, Saikat Basak, Thomas Joseph, and William So collectively bring diverse expertise in data analysis, programming, and teaching. Their goal is to make cutting-edge data science techniques accessible through clear explanations and practical exercises, helping learners from varied backgrounds master the pandas library. Who is it for? This book is best suited for novice to intermediate programmers and data enthusiasts who are already familiar with Python but are new to the pandas library. Ideal readers are those interested in honing their skills in data analysis and visualization, as well as leveraging data for informed decision-making. Whether you're an analyst, aspiring data scientist, or business professional seeking to strengthen your analytical toolkit, this book provides beneficial insights and techniques.

Use business intelligence to power corporate growth, increase efficiency, and improve corporate decision making. With this practical book featuring hands-on examples in Power BI with basic Python and R code, you'll explore the most relevant AI use cases for BI, including improved forecasting, automated classification, and AI-powered recommendations. And you'll learn how to draw insights from unstructured data sources like text, document, and image files. Author Tobias Zwingmann helps BI professionals, business analysts, and data analytics understand high-impact areas of artificial intelligence. You'll learn how to leverage popular AI-as-a-service and AutoML platforms to ship enterprise-grade proofs of concept without the help of software engineers or data scientists. Learn how AI can generate business impact in BI environments Use AutoML for automated classification and improved forecasting Implement recommendation services to support decision-making Draw insights from text data at scale with NLP services Extract information from documents and images with computer vision services Build interactive user frontends for AI-powered dashboard prototypes Implement an end-to-end case study for building an AI-powered customer analytics dashboard

A thought-provoking and startlingly insightful reworking of the science of prediction In Prediction Revisited: The Importance of Observation, a team of renowned experts in the field of data-driven investing delivers a ground-breaking reassessment of the delicate science of prediction for anyone who relies on data to contemplate the future. The book reveals why standard approaches to prediction based on classical statistics fail to address the complexities of social dynamics, and it provides an alternative method based on the intuitive notion of relevance. The authors describe, both conceptually and with mathematical precision, how relevance plays a central role in forming predictions from observed experience. Moreover, they propose a new and more nuanced measure of a prediction’s reliability. Prediction Revisited also offers: Clarifications of commonly accepted but less commonly understood notions of statistics Insight into the efficacy of traditional prediction models in a variety of fields Colorful biographical sketches of some of the key prediction scientists throughout history Mutually supporting conceptual and mathematical descriptions of the key insights and methods discussed within With its strikingly fresh perspective grounded in scientific rigor, Prediction Revisited is sure to earn its place as an indispensable resource for data scientists, researchers, investors, and anyone else who aspires to predict the future from the data-driven lessons of the past.

R is the most powerful tool you can use for statistical analysis. This definitive guide smooths R’s steep learning curve with practical solutions and real-world applications for commercial environments. In R in Action, Third Edition you will learn how to: Set up and install R and RStudio Clean, manage, and analyze data with R Use the ggplot2 package for graphs and visualizations Solve data management problems using R functions Fit and interpret regression models Test hypotheses and estimate confidence Simplify complex multivariate data with principal components and exploratory factor analysis Make predictions using time series forecasting Create dynamic reports and stunning visualizations Techniques for debugging programs and creating packages R in Action, Third Edition makes learning R quick and easy. That’s why thousands of data scientists have chosen this guide to help them master the powerful language. Far from being a dry academic tome, every example you’ll encounter in this book is relevant to scientific and business developers, and helps you solve common data challenges. R expert Rob Kabacoff takes you on a crash course in statistics, from dealing with messy and incomplete data to creating stunning visualizations. This revised and expanded third edition contains fresh coverage of the new tidyverse approach to data analysis and R’s state-of-the-art graphing capabilities with the ggplot2 package. About the Technology Used daily by data scientists, researchers, and quants of all types, R is the gold standard for statistical data analysis. This free and open source language includes packages for everything from advanced data visualization to deep learning. Instantly comfortable for mathematically minded users, R easily handles practical problems without forcing you to think like a software engineer. About the Book R in Action, Third Edition teaches you how to do statistical analysis and data visualization using R and its popular tidyverse packages. In it, you’ll investigate real-world data challenges, including forecasting, data mining, and dynamic report writing. This revised third edition adds new coverage for graphing with ggplot2, along with examples for machine learning topics like clustering, classification, and time series analysis. What's Inside Clean, manage, and analyze data Use the ggplot2 package for graphs and visualizations Techniques for debugging programs and creating packages A complete learning resource for R and tidyverse About the Reader Requires basic math and statistics. No prior experience with R needed. About the Author Dr. Robert I Kabacoff is a professor of quantitative analytics at Wesleyan University and a seasoned data scientist with more than 20 years of experience. Quotes Kabacoff has outdone himself by significantly improving on the already excellent previous edition. - Alain Lompo, ISO-Gruppe R in Action has been my go-to reference on R for years. The third edition contains timely updates on the tidyverse and other new tools. I would recommend this book without hesitation. - Daniel Kenney-Jung MD, Department of Pediatrics, Duke University Outstandingly well-written. The best book on R programming that I have ever read. - Kelvin Meeks, International Technology Ventures Takes the reader through a series of essential methods from basic to complex. The only R book you will ever need. - Martin Perry, Microsoft

Explore the comprehensive world of data science with "Building Data Science Solutions with Anaconda." This book covers essential topics like managing environments with Anaconda, detecting and overcoming bias, and ensuring model interpretability. Delve into practical tools and solutions, all explained in an approachable way to help you become proficient in data science workflows. What this Book will help me do Master environment management for data science projects using Anaconda and conda. Detect and mitigate dataset biases to ensure fair and ethical machine learning models. Learn advanced data science techniques with tools like NumPy, pandas, and Jupyter Notebooks. Understand and explain your machine learning models using LIME and SHAP. Grow your expertise in selecting and fine-tuning AI/ML algorithms for diverse applications. Author(s) None Meador combines extensive expertise in data science with a thorough understanding of Anaconda tools and open source software. With a background in engineering and AI model management, None provides an insightful perspective on the field. Their practical and analogy-driven approach makes technical concepts accessible to learners of any level. Who is it for? This book is ideal for data analysts, aspiring machine learning engineers, and data science professionals who wish to deepen their knowledge and make the most of Anaconda's capabilities. A prior understanding of Python and basic data science principles is assumed. If you're looking to optimize your data science workflows and gain hands-on practice, this book is for you.

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

Take a concise approach to learning how DAX, the function language of Power BI and PowerPivot, works. This book focuses on explaining the core concepts of DAX so that ordinary folks can gain the skills required to tackle complex data analysis problems. But make no mistake, this is in no way an introductory book on DAX. A number of the topics you will learn, such as the concepts of context transition and table expansion, are considered advanced and challenging areas of DAX. While there are numerous resources on DAX, most are written with developers in mind, making learning DAX appear an overwhelming challenge, especially for those who are coming from an Excel background or with limited coding experience. The reality is, to hit the ground running with DAX, it’s not necessary to wade through copious pages on rarified DAX functions and the technical aspects of the language. There are just a few mandatory concepts that must be fully understood before DAX can be mastered. Knowledge of everything else in DAX is built on top of these mandatory aspects. Author Alison Box has been teaching and working with DAX for over eight years, starting with DAX for PowerPivot, the Excel add-in, before moving into the Power BI platform. The guide you hold in your hands is an outcome of these years of experience explaining difficult concepts in a way that people can understand. Over the years she has refined her approach, distilling down the truth of DAX which is “you can take people through as many functions as you like, but it’s to no avail if they don’t truly understand how it all works.” You will learn to use DAX to gain powerful insights into your data by generating complex and challenging business intelligence calculations including, but not limited to: Calculations to control the filtering of information to gain better insight into the data that matters to you Calculations across dates such as comparing data for thesame period last year or the previous period Finding rolling averages and rolling totals Comparing data against targets and KPIs or against average and maximum values Using basket analysis, such as “of customers who bought product X who also bought product Y” Using “what if” analysis and scenarios Finding “like for like” sales Dynamically showing TopN/BottomN percent of customers or products by sales Finding new and returning customers or sales regions in each month or each year Who This Book Is For Excel users and non-technical users of varying levels of ability or anyone who wants to learn DAX for Power BI but lacks the confidence to do so

Discover how to enhance your data analysis with 'Artificial Intelligence with Power BI,' a resource designed to teach you how to leverage Power BI's AI capabilities. You will learn practical methods for enriching your analytics with forecasting, anomaly detection, and machine learning, equipping you to create intelligent, insightful BI reports. What this Book will help me do Learn how to apply AI capabilities such as forecasting and anomaly detection to enrich your reports and drive actionable insights. Explore data preparation techniques optimized for AI, ensuring your datasets are structured for advanced analytics. Develop skills to integrate Azure Machine Learning and Cognitive Services into Power BI, expanding your analytical toolset. Understand how to build Q&A interfaces and integrate Natural Language Processing into your BI solutions. Gain expertise in training and deploying your own machine learning models to achieve tailored insights and predictive analytics. Author(s) None Diepeveen is an experienced data analyst and Power BI expert with a passion for making advanced analytics accessible to professionals. With years of hands-on experience working in the data analytics field, they deliver insights using intuitive, practical approaches through clear and engaging tutorials. Who is it for? This book is ideal for data analysts and BI developers who aim to expand their analytics capabilities with AI. Readers should already be familiar with Power BI and are looking for a resource to teach them how to incorporate predictive and advanced AI techniques into their reporting workflow. Whether you're seeking to gain a professional edge or enhance your organization's data storytelling and insights, this guide is perfect for you.

The Tableau Workshop offers a comprehensive, hands-on guide to mastering data visualization with Tableau. Through practical exercises and engaging examples, you will learn how to prepare, analyze, and visualize data to uncover valuable business insights. By completing this book, you will confidently understand the key concepts and tools needed to create impactful data-driven visual stories. What this Book will help me do Master the use of Tableau Desktop and Tableau Prep for data visualization tasks. Gain the ability to prepare and process data for effective analysis. Learn to choose and utilize the most appropriate chart types for different scenarios. Develop the skills to create interactive dashboards that engage stakeholders. Understand how to perform calculations to extract deeper insights from data. Author(s) Sumit Gupta, None Pinto, Shweta Savale, JC Gillet None, and None Cherven are experts in the field of data analytics and visualization. With diverse backgrounds in business intelligence and hands-on experience with industry tools like Tableau, they bring valuable insights to this book. Their collaborative effort offers practical, real-world knowledge tailored to help learners excel in Tableau and data visualization. With their passion for making technical concepts accessible, they guide readers step by step through their learning journey. Who is it for? This book is ideal for professionals, analysts, or students looking to delve into the world of data visualization with Tableau. Whether you're a complete beginner seeking foundational knowledge, or an intermediate user aiming to refine your skills, this book offers the practical insights you need. It's designed for those who want to master Tableau tools, explore meaningful data insights, and effectively communicate them through engaging dashboards and stories.

This book aims to provide theoretical and empirical frameworks and highlights the challenges and solutions with using Big Data for Corporate Social Responsibility (CSR) and Sustainability in the field of digital transformation and tourism.

"Microsoft Power BI Performance Best Practices" is a thorough guide to mastering efficiently operating Power BI solutions. This book walks you through optimizing every layer of a Power BI project, from data transformations to architecture, equipping you with the ability to create robust and scalable analytics solutions. What this Book will help me do Understand how to set realistic performance goals for Power BI projects and implement ongoing performance monitoring. Apply effective architectural and configuration strategies to improve Power BI solution efficiency. Learn practices for constructing and optimizing data models and implementing Row-Level Security effectively. Utilize tools like DAX Studio and VertiPaq Analyzer to detect and resolve common performance bottlenecks. Gain deep knowledge of Power BI Premium and techniques for handling large-scale data solutions using Azure. Author(s) Bhavik Merchant is a recognized expert in business intelligence and analytics solutions. With extensive experience in designing and implementing Power BI solutions across industries, he brings a pragmatic approach to solving performance issues in Power BI. Bhavik's writing style reflects his passion for teaching, ensuring readers gain practical knowledge they can directly apply to their work. Who is it for? This book is designed for data analysts, BI developers, and data professionals who have foundational knowledge of Power BI and aim to elevate their skills to construct high-performance analytics solutions. It is particularly suited to individuals seeking guidance on best practices and tools for optimizing Power BI applications.

The Kaggle Book is an essential guide for anyone aiming to excel in data science through Kaggle competitions. With expert advice from Kaggle Grandmasters, you'll learn practical techniques for handling data, creating robust models, and improving your ranking in competitions. This book is packed with insights on advanced topics like ensembling, validation, and evaluation metrics. What this Book will help me do Master the Kaggle platform, including its Notebooks, Datasets, and Discussion capabilities. Enhance model performance using techniques like feature engineering, AutoML, and ensembling strategies. Apply advanced validation schemes to improve the reliability of your predictions. Tackle diverse competition types, including NLP, computer vision, and optimization challenges. Build a professional portfolio to showcase your data science expertise and attract career opportunities. Author(s) Konrad Banachewicz and Luca Massaron, authoritative Kaggle Grandmasters, bring their wealth of experience in competitive data science to this book. They have collectively competed in numerous Kaggle challenges and possess deep insights into what differentiates successful Kagglers. Their guidance combines practicality with expertise, making this book a must-have for aspiring data scientists looking to make an impact. Who is it for? This book is tailored for data analysts and scientists interested in enhancing their Kaggle performance, as well as those new to Kaggle who wish to explore competitive data science. It suits individuals with basic knowledge of machine learning, aiming to develop and demonstrate their skills further. The content is valuable for practitioners aiming to build a professional profile or secure roles in the tech industry.

BIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology. Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformatician’s important tools and examines how they are used to evaluate biological data and advance disease knowledge. The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information. Audience The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues.

It’s time for some truly “Excel-lent” spreadsheet reporting Beneath the seemingly endless rows and columns of cells, the latest version of Microsoft Excel boasts an astonishing variety of features and capabilities. But how do you go about tapping into some of that power without spending all of your days becoming a spreadsheet guru? It’s easy. You grab a copy of the newest edition of Excel Dashboards & Reports For Dummies and get ready to blow the pants off your next presentation audience! With this book, you’ll learn how to transform those rows and columns of data into dynamic reports, dashboards, and visualizations. You’ll draw powerful new insights from your company’s numbers to share with your colleagues – and seem like the smartest person in the room while you’re doing it. Excel Dashboards & Reports For Dummies offers: Complete coverage of the latest version of Microsoft Excel provided in the Microsoft 365 subscription Strategies to automate your reporting so you don’t have to manually crunch the numbers every week, month, quarter, or year Ways to get new perspectives on old data, visualizing it so you can find solutions no one else has seen before If you’re ready to make your company’s numbers and spreadsheets dance, it’s time to get the book that’ll have them moving to your tune in no time. Get Excel Dashboards & Reports For Dummies today.

INTERNET OF MEDICAL THINGS (IOMT) Providing an essential addition to the reference material available in the field of IoMT, this timely publication covers a range of applied research on healthcare, biomedical data mining, and the security and privacy of health records. With their ability to collect, analyze and transmit health data, IoMT tools are rapidly changing healthcare delivery. For patients and clinicians, these applications are playing a central part in tracking and preventing chronic illnesses — and they are poised to evolve the future of care. In this book, the authors explore the potential applications of a wave of sensor-based tools—including wearables and stand-alone devices for remote patient monitoring—and the marriage of internet-connected medical devices with patient information that ultimately sets the IoMT ecosystem apart. This book demonstrates the connectivity between medical devices and sensors is streamlining clinical workflow management and leading to an overall improvement in patient care, both inside care facilities and in remote locations.

Create stunning and complex visualizations using the amazing Charticulator custom visuals in Power BI. Charticulator offers users immense power to generate visuals and graphics. To a beginner, there are myriad settings and options that can be combined in what feels like an unlimited number of combinations, giving it the unfair label, “the DAX of the charting world”. This is not true. This book is your start-to-finish guide to using Charticulator, a custom visualization software that Microsoft integrated into Power BI Desktop so that Power BI users can create incredibly powerful, customized charts and graphs. You will learn the concepts that underpin the software, journeying through every building block of chart design, enabling you to combine these parts to create spectacular visuals that represent the story of your data. Unlike other custom Power BI visuals, Charticulator runs in a separate application window within Power BI with its own interface and requires a different set of interactions and associated knowledge. This book covers the ins and outs of all of them. What You Will Learn Generate inspirational and technically competent visuals with no programming or other specialist technical knowledge Create charts that are not restricted to conventional chart types such as bar, line, or pie Limit the use of diverse Power BI custom visuals to one Charticulator custom visual Alleviate frustrations with the limitations of default chart types in Power BI, such as being able to plot data on only one categorical axis Use a much richer set of options to compare different sets of data Re-use your favorite or most often used chart designs with Charticulator templates Who This Book Is For The average Power BI user. It assumes no prior knowledge on the part of the reader other than being able to open Power BI desktop, import data, and create a simple Power BI visual. User experiences may vary, from people attending a Power BI training course to those with varying skills and abilities, from SQL developers and advanced Excel users to people with limited data analysis experience and technical skills.

Compared to other functions of an organization, data science is highly speculative. Data science teams are often tasked with last-minute must-have deliverables that are well beyond their ability to produce. Data might be missing or have no signal, or the data models themselves might be impractical. This hands-on reference guides team leaders through the types of challenges you might face and the tools you need to work through them. Author Jacqueline Nolis, head of data science at Saturn Cloud, helps team leaders think through the various issues you'll encounter when running a data science team. You'll learn ways to set up your team, manage data scientists to promote their success, and collaborate with external stakeholders. Once you finish this report, you'll be ready to work through the challenges your current team faces or start a new data science team in an organization that needs one. Determine the scope of work before choosing your team of data scientists and support positions Successfully manage your relationship with stakeholders by providing your team with clear, achievable goals Create an environment to help data scientists and other team members succeed Choose a technical infrastructure for your team, including programming languages, databases, and deployment models

This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements.