Delve into the powerful world of data cleaning with Microsoft Power BI in this detailed guide. You'll learn how to connect, transform, and optimize data from various sources, setting a strong foundation for insightful data-driven decisions. Equip yourself with the skills to master data quality, leverage DAX and Power Query, and produce actionable insights with improved efficiency. What this Book will help me do Master connecting to various data sources and importing data effectively into Power BI. Learn to use the Query Editor to clean and transform data efficiently. Understand how to use the M language to perform advanced data transformations. Gain expertise in creating optimized data models and handling relationships within Power BI. Explore insights-driven exploratory data analysis using Power BI's powerful tools. Author(s) None Frazer is an experienced data professional with a deep knowledge of business intelligence tools and analytics processes. With a strong background in data science and years of hands-on experience using Power BI, Frazer brings practical advice to help users improve their data preparation and analysis skills. Known for creating resources that are both comprehensive and approachable, Frazer is dedicated to empowering readers in their data journey. Who is it for? This book is ideal for data analysts, business intelligence professionals, and business analysts who work regularly with data. If you are someone with a basic understanding of BI tools and concepts looking to deepen their skills, especially in Power BI, this book will guide you effectively. It will also help data scientists and other professionals interested in data cleaning to build a robust basis for data quality and analysis. Whether you're addressing common data challenges or seeking to enhance your BI capabilities, this guide is tailored to accommodate your needs.

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

Dive into the wonders of Microsoft Fabric, the ultimate solution for mastering data analytics in the AI era. Through engaging real-world examples and hands-on scenarios, this book will equip you with all the tools to design, build, and maintain analytics systems for various use cases like lakehouses, data warehouses, real-time analytics, and data science. What this Book will help me do Understand and utilize the key components of Microsoft Fabric for modern analytics. Build scalable and efficient data analytics solutions with medallion architecture. Implement real-time analytics and machine learning models to derive actionable insights. Monitor and administer your analytics platform for high performance and security. Leverage AI-powered assistant Copilot to boost analytics productivity. Author(s) Arshad Ali and None Schacht bring years of expertise in data analytics and system architecture to this book. Arshad is a seasoned professional specialized in AI-integrated analytics platforms, while None Schacht has a proven track record in deploying enterprise data solutions. Together, they provide deep insights and practical knowledge with a structured and approachable teaching method. Who is it for? Ideal for data professionals such as data analysts, engineers, scientists, and AI/ML experts aiming to enhance their data analytics skills and master Microsoft Fabric. It's also suited for students and new entrants to the field looking to establish a firm foundation in analytics systems. Requires a basic understanding of SQL and Spark.

Practical methods for analyzing your data with graphs, revealing hidden connections and new insights. Graphs are the natural way to represent and understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. In Graph Algorithms for Data Science you will learn: Labeled-property graph modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. About the Technology A graph, put simply, is a network of connected data. Graphs are an efficient way to identify and explore the significant relationships naturally occurring within a dataset. This book presents the most important algorithms for graph data science with examples from machine learning, business applications, natural language processing, and more. About the Book Graph Algorithms for Data Science shows you how to construct and analyze graphs from structured and unstructured data. In it, you’ll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project. This cutting-edge book also demonstrates how you can create graphs that optimize input for AI models using node embedding. What's Inside Creating knowledge graphs Node classification and link prediction workflows NLP techniques for graph construction About the Reader For data scientists who know machine learning basics. Examples use the Cypher query language, which is explained in the book. About the Author Tomaž Bratanič works at the intersection of graphs and machine learning. Arturo Geigel was the technical editor for this book. Quotes Undoubtedly the quickest route to grasping the practical applications of graph algorithms. Enjoyable and informative, with real-world business context and practical problem-solving. - Roger Yu, Feedzai Brilliantly eases you into graph-based applications. - Sumit Pal, Independent Consultant I highly recommend this book to anyone involved in analyzing large network databases. - Ivan Herreros, talentsconnect Insightful and comprehensive. The author’s expertise is evident. Be prepared for a rewarding journey. - Michal Štefaňák, Volke

Learn and explore the capabilities of Microsoft Fabric, the latest evolution in cloud analytics suites. This book will help you understand how users can leverage Microsoft Office equivalent experience for performing data management and advanced analytics activity. The book starts with an overview of the analytics evolution from on premises to cloud infrastructure as a service (IaaS), platform as a service (PaaS), and now software as a service (SaaS version) and provides an introduction to Microsoft Fabric. You will learn how to provision Microsoft Fabric in your tenant along with the key capabilities of SaaS analytics products and the advantage of using Fabric in the enterprise analytics platform. OneLake and Lakehouse for data engineering is discussed as well as OneLake for data science. Author Ghosh teaches you about data warehouse offerings inside Microsoft Fabric and the new data integration experience which brings Azure Data Factory and Power Query Editor of Power BI together in a single platform. Also demonstrated is Real-Time Analytics in Fabric, including capabilities such as Kusto query and database. You will understand how the new event stream feature integrates with OneLake and other computations. You also will know how to configure the real-time alert capability in a zero code manner and go through the Power BI experience in the Fabric workspace. Fabric pricing and its licensing is also covered. After reading this book, you will understand the capabilities of Microsoft Fabric and its Integration with current and upcoming Azure OpenAI capabilities. What You Will Learn Build OneLake for all data like OneDrive for Microsoft Office Leverage shortcuts for cross-cloud data virtualization in Azure and AWS Understand upcoming OpenAI integration Discover new event streaming and Kusto query inside Fabric real-time analytics Utilize seamless tooling for machine learning and data science Who This Book Is For Citizen users and experts in the data engineering and data science fields, along with chief AI officers

Faster websites offer a better user experience and typically have higher conversion rates. It can be challenging to know where to invest to meaningfully improve a website's speed. Investing correctly to improve speed starts with understanding how to correctly measure speed and knowing how to use those measurements to identify the biggest opportunities. Speed Metrics Guidehelps marketers, SEOs, business leaders, designers, and everybody else involved in website performance select the right metrics to use to optimize their website's speed. Each chapter examines a specific metric, discusses what it measures, why the metric matters and what tactics will help improve that metric. What You'll Learn The best metrics and tools to help you measure website speed, including Google's Core Web Vitals How and when to best use each metric Where each metric fits within the website loading process How to use each metric to find different ways of improving website speed Who This book Is For Non-technical audience, including marketers, SEOs, designers, and UX professionals.

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

She Can STEM highlights the curiosity, creativity, and perseverance of 50 leading historical and modern physicists, chemists, biologists, and ecologists, pairing each biography with a hands-on project.

You Will Learn Python! Zed Shaw has created the world's most reliable system for learning Python. Follow it and you will succeed--just like the millions of beginners Zed has taught to date! You bring the discipline, persistence, and attention; the author supplies the masterful knowledge you need to succeed. In Learn Python the Hard Way, Fifth Edition, you'll learn Python by working through 60 lovingly crafted exercises. Read them. Type in the code. Run it. Fix your mistakes. Repeat. As you do, you'll learn how a computer works, how to solve problems, and how to enjoy programming . . . even when it's driving you crazy. Install a complete Python environment Organize and write code Fix and break code Basic mathematics Strings and text Interact with users Work with files Looping and logic Object-oriented programming Data structures using lists and dictionaries Modules, classes, and objects Python packaging Automated testing Basic SQL for Data Science Web scraping Fixing bad data (munging) The "Data" part of "Data Science" It'll be frustrating at first. But if you keep trying, you'll get it--and it'll feel amazing! This course will reward you for every minute you put into it. Soon, you'll know one of the world's most powerful, popular programming languages. You'll be a Python programmer. This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven't written code in years Aspiring Data Scientists or academics who need to learn to code Seasoned professionals looking for a fast, simple crash course in Python for Data Science Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

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

From a cluster of interconnected HTML pages to online service platforms, websites are constantly changing in form and function. These transformations have led, on the one hand, to human and social sciences renewing or inventing analytical methodologies; and on the other hand, to a reconsideration of the practices of non-specialists and digital professionals. The Web factory is equally included on the agenda of communication training, according to an alternative approach that is complementary to the one that has been implemented for computer scientists. From these two perspectives and drawing upon several case studies, Analyzing Websites presents epistemological and methodological contributions from researchers in Information and Communication Sciences exploring websites as sociotechnical, semi-discursive and communicational devices. This study covers website design as well as their integration into the digital strategies of organizations in the public, associative and private sectors.

Principles of Data Science offers an end-to-end introduction to data science fundamentals, blending key mathematical concepts with practical programming. You'll learn how to clean and prepare data, construct predictive models, and leverage modern tools like pre-trained models for NLP and computer vision. By integrating theory and practice, this book sets the foundation for impactful data-driven decision-making. What this Book will help me do Develop a solid understanding of foundational statistics and machine learning. Learn how to clean, transform, and visualize data for impactful analysis. Explore transfer learning and pre-trained models for advanced AI tasks. Understand ethical implications, biases, and governance in AI and ML. Gain the knowledge to implement complete data pipelines effectively. Author(s) Sinan Ozdemir is an experienced data scientist, educator, and author with a deep passion for making complex topics accessible. With a background in computer science and applied statistics, Sinan has taught data science at leading institutions and authored multiple books on the topic. His practical approach to teaching combines real-world examples with insightful explanations, ensuring learners gain both competence and confidence. Who is it for? This book is ideal for beginners in data science who want to gain a comprehensive understanding of the field. If you have a background in programming or mathematics and are eager to combine these skills to analyze and extract insights from data, this book will guide you. Individuals working with machine learning or AI who need to solidify their foundational knowledge will find it invaluable. Some familiarity with Python is recommended to follow along seamlessly.

"MATLAB for Machine Learning" is your comprehensive guide to leveraging MATLAB's powerful tools and toolbox for machine learning and deep learning tasks. Through this book, you will explore practical applications and processes that streamline the development of machine learning models while tackling real-world problems effectively. What this Book will help me do Gain proficiency in utilizing MATLAB's Machine Learning Toolbox for developing machine learning algorithms. Learn how to handle data preprocessing, from data cleansing to visualization, within MATLAB. Explore and implement foundational to advanced machine learning techniques, such as classification and regression models. Comprehend and apply the principles of neural networks for pattern recognition and cluster analysis. Dive into advanced concepts of deep learning, including convolutional networks, natural language processing, and time series analysis, using MATLAB's inbuilt functionality. Author(s) Giuseppe Ciaburro is an expert in the field of machine learning and MATLAB programming. With a robust academic background in data science and years of experience in applying these principles across domains, Giuseppe provides a clear and approachable pathway for learners in his writing. Who is it for? This book is ideal for machine learning professionals, data scientists, and engineers specializing in fields such as deep learning, computer vision, and natural language processing. It is suitable for those with a fundamental understanding of programming concepts who seek to apply MATLAB in solving complex learning problems. A prior familiarity with MATLAB basics will be advantageous.

Learn statistics by analyzing professional basketball data! In this action-packed book, you’ll build your skills in exploratory data analysis by digging into the fascinating world of NBA games and player stats using the R language. Statistics Slam Dunk is an engaging how-to guide for statistical analysis with R. Each chapter contains an end-to-end data science or statistics project delving into NBA data and revealing real-world sporting insights. Written by a former basketball player turned business intelligence and analytics leader, you’ll get practical experience tidying, wrangling, exploring, testing, modeling, and otherwise analyzing data with the best and latest R packages and functions. In Statistics Slam Dunk you’ll develop a toolbox of R programming skills including: Reading and writing data Installing and loading packages Transforming, tidying, and wrangling data Applying best-in-class exploratory data analysis techniques Creating compelling visualizations Developing supervised and unsupervised machine learning algorithms Executing hypothesis tests, including t-tests and chi-square tests for independence Computing expected values, Gini coefficients, z-scores, and other measures If you’re looking to switch to R from another language, or trade base R for tidyverse functions, this book is the perfect training coach. Much more than a beginner’s guide, it teaches statistics and data science methods that have tons of use cases. And just like in the real world, you’ll get no clean pre-packaged data sets in Statistics Slam Dunk. You’ll take on the challenge of wrangling messy data to drill on the skills that will make you the star player on any data team. About the Technology Statistics Slam Dunk is a data science manual with a difference. Each chapter is a complete, self-contained statistics or data science project for you to work through—from importing data, to wrangling it, testing it, visualizing it, and modeling it. Throughout the book, you’ll work exclusively with NBA data sets and the R language, applying best-in-class statistics techniques to reveal fun and fascinating truths about the NBA. About the Book Is losing basketball games on purpose a rational strategy? Which hustle statistics have an impact on wins and losses? Does spending more on player salaries translate into a winning record? You’ll answer all these questions and more. Plus, R’s visualization capabilities shine through in the book’s 300 plots and charts, including Pareto charts, Sankey diagrams, Cleveland dot plots, and dendrograms. What's Inside Transforming, tidying, and wrangling data Applying best-in-class exploratory data analysis techniques Developing supervised and unsupervised machine learning algorithms Executing hypothesis tests and effect size tests About the Reader For readers who know basic statistics. No advanced knowledge of R—or basketball—required. About the Author Gary Sutton is a former basketball player who has built and led high-performing business intelligence and analytics organizations across multiple verticals. Quotes In this journey of exploration, every computer scientist will find a valuable ally in understanding the language of data. - Kim Lokøy, areo Transcends other R titles by revealing the hidden narratives that lie within the numbers. - Christian Sutton, Shell International Exploration and Production Seamlessly blending theory and practical insights, this book serves as an indispensable guide for those venturing into the field of data analytics. - Juan Delgado, Sodexo BRS

This book, "Extending Dynamics 365 Finance and Operations Apps with Power Platform", is your comprehensive guide to mastering the integration of Power Platform tools with Dynamics 365 Finance and Operations (F&O). Dive into practical scenarios and learn how to leverage tools like Power Automate, Power Apps, and Power BI to maximize efficiency and streamline operations. What this Book will help me do Learn to extend Dynamics 365 F&O functionalities using Dataverse and other Power Platform tools. Understand practical integration techniques including dual-write and virtual tables. Explore automation of workflows and tasks with Power Automate in an F&O context. Utilize Power BI for creating insightful reports with Azure Data Lake Storage. Gain insights into governance and lifecycle management for the Power Platform in your F&O projects. Author(s) Adrià Ariste Santacreu, a seasoned expert in Power Platform and Microsoft Dynamics, shares his rich industry experience through this book. He has implemented numerous real-world solutions integrating Power Platform with Dynamics 365, and his expertise is evident in the clear and actionable guidance provided herein. Adrià's approach helps both novices and experts discover the true potential of these technologies. Who is it for? This book is ideal for Dynamics 365 Finance & Operations consultants and Power Platform professionals seeking to extend their knowledge and capabilities. It is particularly suitable for technical and solutions architects who aim to implement innovative solutions for business requirements using the Power Platform. If you aim to leverage low-code/no-code tools for dynamics projects, this book has you covered.

How to Use SPSS® is designed with the novice computer user in mind and for people who have no previous experience using SPSS. Each chapter is divided into short sections that describe the statistic being used, important underlying assumptions, and how to interpret the results and express them in a research report.

Make some headway in the notoriously tough subject of business statistics Business Statistics For Dummies helps you understand the core concepts and principles of business statistics, and how they relate to the business world. This book tracks to a typical introductory course offered at the undergraduate, so you know you’ll find all the content you need to pass your class and get your degree. You’ll get an introduction to statistical problems and processes common to the world of global business and economics. Written in clear and simple language, Business Statistics For Dummies gives you an introduction to probability, sampling techniques and distributions, and drawing conclusions from data. You’ll also discover how to use charts and graphs to visualize the most important properties of a data set. Grasp the core concepts, principles, and methods of business statistics Learn tricky concepts with simplified explanations and illustrative graphs See how statistics applies in the real world, thanks to concrete examples Read charts and graphs for a better understanding of how businesses operate Business Statistics For Dummies is a lifesaver for students studying business at the college level. This guide is also useful for business professionals looking for a desk reference on this complicated topic.

Start a brand-new career in data analytics with no-nonsense advice from a self-taught data analytics consultant In How to Become a Data Analyst: My Low-Cost, No Code Roadmap for Breaking into Tech, data analyst and analytics consultant Annie Nelson walks you through how she took the reins and made a dramatic career change to unlock new levels of career fulfilment and enjoyment. In the book, she talks about the adaptability, curiosity, and persistence you’ll need to break free from the 9-5 grind and how data analytics—with its wide variety of skills, roles, and options—is the perfect field for people looking to refresh their careers. Annie offers practical and approachable data portfolio-building advice to help you create one that’s manageable for an entry-level professional but will still catch the eye of employers and clients. You’ll also find: Deep dives into the learning journey required to step into a data analytics role Ways to avoid getting lost in the maze of online courses and certifications you can find online—while still obtaining the skills you need to be competitive Explorations of the highs and lows of Annie’s career-change journey and job search—including what was hard, what was easy, what worked well, and what didn’t Strategies for using ChatGPT to help you in your job search A must-read roadmap to a brand-new and exciting career in data analytics, How to Become a Data Analyst is the hands-on tutorial that shows you exactly how to succeed.

Discover how to navigate the world of Web3 data with 'Data Science for Web3,' an expertly crafted guide by Gabriela Castillo Areco. Through practical examples, industry insights, and real-world use cases, you will learn the skills needed to analyze blockchain data and extract actionable business insights. What this Book will help me do Understand blockchain transactions and data structures to build robust datasets. Leverage on-chain and off-chain data for valuable Web3 business insights. Create DeFi- and NFT-specific datasets for targeted analysis. Develop machine learning models tailored for blockchain use cases. Apply data science techniques to innovate in the Web3 ecosystem. Author(s) Gabriela Castillo Areco is a seasoned data scientist and an expert in blockchain analytics. With years of experience in the technology and finance sectors, Gabriela brings a practical perspective to understanding intricate data within the emerging Web3 paradigm. Her engaging approach makes technical concepts accessible and actionable. Who is it for? This book is ideal for data professionals such as analysts, scientists, or engineers, aiming to harness the potential of blockchain analytics. It's also suitable for business professionals exploring data-driven opportunities within Web3. Whether you're a beginner or an experienced learner with some Python background, this book will meet you at your level.

Many books have been written about software testing, but most of them discuss the general framework of testing from a traditional perspective. Unfortunately, traditional test design techniques are often ineffective and unreliable for revealing the various kinds of faults that may occur. This book introduces three new software testing techniques: Two-Phase Model-Based Testing, the Action-State Testing, and the General Predicate Testing, all of which work best when applied with efficient fault revealing capabilities. You’ll start with a short recap of software testing, focusing on why risk analysis is obligatory, how to classify bugs practically, and how fault-based testing can be used for improving test design. You’ll then see how action-state testing merges the benefits of state transition testing and use case testing into a unified approach. Moving on you’ll look at general predicate testing and how it serves as an extension of boundary value analysis, encompassing morecomplex predicates. Two-phase model-based testing represents an advanced approach where the model does not necessarily need to be machine-readable; human readability suffices. The first phase involves a high-level model from which abstract tests are generated. Upon manual execution of these tests, the test code is generated. Rather than calculating output values, they are merely checked for conformity. The last part of this book contains a chapter on how developers and testers can help each other and work as a collaborative team. What You'll Learn Apply efficient test design techniques for detecting domain faults Work with modeling techniques that combine all the advantages of state transition testing and uses case testing Grasp the two-phase model-based testing technique Use test design efficiently to find almost all the bugs in an application Who This Book Is For Software developers, QA engineers, and, business analysts

Learn Grafana 10.x is your essential guide to mastering the art of data visualization and monitoring through interactive dashboards. Whether you're starting from scratch or updating your knowledge to Grafana 10.x, this book walks you through installation, implementation, data transformation, and effective visualization techniques. What this Book will help me do Install and configure Grafana 10.x for real-time data visualization and analytics. Create and manage insightful dashboards with Grafana's enhanced features. Integrate Grafana with diverse data sources such as Prometheus, InfluxDB, and Elasticsearch. Set up dynamic templated dashboards and alerting systems for proactive monitoring. Implement Grafana's user authentication mechanisms for enhanced security. Author(s) None Salituro is a seasoned expert in data analytics and observability platforms with extensive experience working with time-series data using Grafana. Their practical teaching approach and passion for sharing insights make this book an invaluable resource for both newcomers and experienced users. Who is it for? This book is perfect for business analysts, data visualization enthusiasts, and developers interested in analyzing and monitoring time-series data. Whether you're a newcomer or have some background knowledge, this book offers accessible guidance and advanced tips suitable for all levels. If you're aiming to efficiently build and utilize Grafana dashboards, this is the book for you.

Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy. Put its advanced techniques into practice with this hands-on guide. In Bayesian Optimization in Action you will learn how to: Train Gaussian processes on both sparse and large data sets Combine Gaussian processes with deep neural networks to make them flexible and expressive Find the most successful strategies for hyperparameter tuning Navigate a search space and identify high-performing regions Apply Bayesian optimization to cost-constrained, multi-objective, and preference optimization Implement Bayesian optimization with PyTorch, GPyTorch, and BoTorch Bayesian Optimization in Action shows you how to optimize hyperparameter tuning, A/B testing, and other aspects of the machine learning process by applying cutting-edge Bayesian techniques. Using clear language, illustrations, and concrete examples, this book proves that Bayesian optimization doesn’t have to be difficult! You’ll get in-depth insights into how Bayesian optimization works and learn how to implement it with cutting-edge Python libraries. The book’s easy-to-reuse code samples let you hit the ground running by plugging them straight into your own projects. About the Technology In machine learning, optimization is about achieving the best predictions—shortest delivery routes, perfect price points, most accurate recommendations—in the fewest number of steps. Bayesian optimization uses the mathematics of probability to fine-tune ML functions, algorithms, and hyperparameters efficiently when traditional methods are too slow or expensive. About the Book Bayesian Optimization in Action teaches you how to create efficient machine learning processes using a Bayesian approach. In it, you’ll explore practical techniques for training large datasets, hyperparameter tuning, and navigating complex search spaces. This interesting book includes engaging illustrations and fun examples like perfecting coffee sweetness, predicting weather, and even debunking psychic claims. You’ll learn how to navigate multi-objective scenarios, account for decision costs, and tackle pairwise comparisons. What's Inside Gaussian processes for sparse and large datasets Strategies for hyperparameter tuning Identify high-performing regions Examples in PyTorch, GPyTorch, and BoTorch About the Reader For machine learning practitioners who are confident in math and statistics. About the Author Quan Nguyen is a research assistant at Washington University in St. Louis. He writes for the Python Software Foundation and has authored several books on Python programming. Quotes Using a hands-on approach, clear diagrams, and real-world examples, Quan lifts the veil off the complexities of Bayesian optimization. - From the Foreword by Luis Serrano, Author of Grokking Machine Learning This book teaches Bayesian optimization, starting from its most basic components. You’ll find enough depth to make you comfortable with the tools and methods and enough code to do real work very quickly. - From the Foreword by David Sweet, Author of Experimentation for Engineers Combines modern computational frameworks with visualizations and infographics you won’t find anywhere else. It gives readers the confidence to apply Bayesian optimization to real world problems! - Ravin Kumar, Google

This textbook provides students with the skills to analyse business case studies from a strategic perspective.

Use data, analytics and metrics to make better HR decisions for your business.

Migrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Naïve Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary toapply these methods using PySpark, the industry standard for building scalable ML data pipelines. What You Will Learn Master the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systems Understand the differences between PySpark, scikit-learn, and pandas Perform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySpark Distinguish between the pipelines of PySpark and scikit-learn Who This Book Is For Data scientists, data engineers, and machine learning practitioners who have some familiarity with Python, but who are new to distributed machine learning and the PySpark framework.