Dive deep into Microsoft Power BI with the second edition of 'Mastering Microsoft Power BI'. This comprehensive book equips you with the skills to transform business data into actionable insights using Power BI's latest features and techniques. From efficient data retrieval and transformation processes to creating interactive dashboards that tell impactful data stories, you will learn actionable knowledge every step of the way. What this Book will help me do Learn to master data collection and modeling using the Power Query M language Gain expertise in designing DirectQuery, import, and composite data models Understand how to create advanced analytics reports using DAX and Power BI visuals Learn to manage the Power BI environment as an administrator with Premium capacity Develop insightful, scalable, and visually impactful dashboards and reports Author(s) Greg Deckler, a seasoned Power BI expert and solution architect, and None Powell, an experienced BI consultant and data visualization specialist, bring their extensive practical knowledge to this book. Together, they share their real-world expertise and proven techniques applying Power BI's diverse capabilities. Who is it for? This book is ideal for business intelligence professionals and intermediate Power BI users. If you're looking to master data visualization, prepare insightful dashboards, and explore Power BI's full potential, this is for you. Basic understanding of BI concepts and familiarity with Power BI will ensure you get the most value.

BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.

THE GUIDE FOR ANYONE AFRAID TO LEARN STATISTICS & ANALYTICS UPDATED WITH NEW EXAMPLES & EXERCISES This book discusses statistics and analytics using plain language and avoiding mathematical jargon. If you thought you couldn't learn these data analysis subjects because they were too technical or too mathematical, this book is for you! This edition delivers more everyday examples and end-of-chapter exercises and contains updated instructions for using Microsoft Excel. You'll use downloadable data sets and spreadsheet solutions, template-based solutions you can put right to work. Using this book, you will understand the important concepts of statistics and analytics, including learning the basic vocabulary of these subjects. Create tabular and visual summaries and learn to avoid common charting errors Gain experience working with common descriptive statistics measures including the mean, median, and mode; and standard deviation and variance, among others Understand the probability concepts that underlie inferential statistics Learn how to apply hypothesis tests, using Z, t, chi-square, ANOVA, and other techniques Develop skills using regression analysis, the most commonly-used Inferential statistical method Explore results produced by predictive analytics software Choose the right statistical or analytic techniques for any data analysis task Optionally, read the Equation Blackboards, designed for readers who want to learn about the mathematical foundations of selected methods ...

Examine select retail business scenarios to learn basic mathematics, as well as probability and statistics required to analyze big data. This book focuses on useful and imperative applied analytics needed to build a retail business and explains mathematical concepts essential for decision making and communication in retail business environments. Everyone is a buyer or seller of products these days whether through a physical department store, Amazon, or their own business website. This book is a step-by-step guide to understanding and managing the mechanics of markups, markdowns, and basic statistics, math and computers that will help in your retail business. You'll tackle what to do with data once it is has accumulated and see how to arrange the data using descriptive statistics, primarily means, median, and mode, and then how to read the corresponding charts and graphs. Analytics for Retail is your path to creating visualrepresentations that powerfully communicate information and drive decisions. What You'll Learn Review standard statistical concepts to enhance your understanding of retail data Understand the concepts of markups, markdowns and profit margins, and probability Conduct an A/B testing email campaign with all the relevant analytics calculated and explained Who This Book Is For This is a primer book for anyone in the field of retail that needs to learn or refresh their skills or for a reader who wants to move in their company to a more analytical position.

This book is your ultimate companion to mastering Microsoft Power BI and becoming proficient in data analysis and visualization. With a focus on understanding and utilizing Power BI to its fullest extent, this guide also prepares you comprehensively for the PL-300 certification exam. You will go from the basics to advanced techniques enabling you to confidently analyze and present data. What this Book will help me do Understand and connect to various data sources using Power BI. Gain skills in transforming and preparing data for advanced analysis. Develop expertise in designing and optimizing data models. Learn to create insightful reports and dashboards to convey information clearly. Prepare for and succeed in the PL-300 certification exam with practice questions. Author(s) Authors None Edenfield and None Corcoran bring extensive experience in business intelligence and data analytics to this book. They have years of hands-on expertise with Power BI and a passion for teaching analytics in a practical and accessible way. Together, they aim to empower readers to master Power BI and achieve their certification goals. Who is it for? This book is perfect for data analysts, business intelligence professionals, and anyone aiming to deepen their knowledge of Microsoft Power BI. Beginners will find approachable content to quickly get started while experienced users will find detailed topics to refine their expertise. By covering exam preparation and practical applications, this guide benefits a wide range of learners who wish to get certified and excel in data-centric roles.

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.

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.

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.

Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines

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.

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

Dive into the world of reproducible data science with Pachyderm, a specialized platform designed for version-controlled data pipelines. By following this book, 'Reproducible Data Science with Pachyderm,' you'll gain the skills to implement robust, scalable machine learning workflows with Pachyderm 2.0, covering setup, integration, and advanced use cases. What this Book will help me do Build scalable, version-controlled data pipelines with Pachyderm's unique features. Understand the principles behind reproducible data science and implement them effectively. Deploy Pachyderm on AWS, Google Cloud, and Azure while integrating with popular tools. Create and manage end-to-end machine learning workflows, including hyperparameter tuning. Leverage advanced integrations, such as Pachyderm Notebooks and language clients like Python and Go. Author(s) Svetlana Karslioglu is a seasoned data scientist with extensive experience in constructing scalable machine learning and data processing systems. With years in both practical implementation and educational endeavors, she has a talent for breaking down complex concepts into accessible learning paths. Her approach is hands-on and results-oriented, aimed at empowering professionals to excel in the field of data science. Who is it for? This book is intended for data scientists, machine learning engineers, and data engineers who are keen to ensure reproducibility in their workflows. Ideal readers may have familiarity with data science basics and some exposure to Kubernetes and programming languages like Python. By studying the book, learners will establish confidence in implementing Pachyderm for scalable and reliable data pipelines.

This handy reference book detailing the intricacies of R covers version 4.x features, including numerous and significant changes to syntax, strings, reference counting, grid units, and more. Starting with the basic structure of R, the book takes you on a journey through the terminology used in R and the syntax required to make R work. You will find looking up the correct form for an expression quick and easy. Some of the new material includes information on RStudio, S4 syntax, working with character strings, and an example using the Twitter API. With a copy of the R 4 Quick Syntax Reference in hand, you will find that you are able to use the multitude of functions available in R and are even able to write your own functions to explore and analyze data. What You Will Learn Discover the modes and classes of R objects and how to use them Use both packaged and user-created functions in R Import/export data and create new data objects in R Create descriptive functions and manipulate objects in R Take advantage of flow control and conditional statements Work with packages such as base, stats, and graphics Who This Book Is For Those with programming experience, either new to R, or those with at least some exposure to R but who are new to the latest version.

The essential, intermediate and advanced topics of Simulink are covered in the book. The concept of multi-domain physical modeling concept and tools in Simulink are illustrated with examples for engineering systems and multimedia information. The combination of Simulink and numerical optimization methods provides new approaches for solving problems, where solutions are not known otherwise.

From an Alexa device in our home to an aircraft, Cyber-Physical Systems (CPS) have transformed the living standard of human beings. The data generated from the CPS provide tremendous insights into the behaviour and functioning of organisations.