Start your journey with SAS. Have you just accepted a new job as a data analyst and need to learn SAS fast? Or perhaps you want to make a career change into programming and you’re not sure where to begin. The Simple Guide to SAS: From Null to Novice is the perfect book to get you started. Written for individuals with no prior programming experience, this book teaches the basics of learning SAS using hands-on examples and step-by-step explanations in a short, easy-to-understand guide. Topics covered in this book include: DATA and PROC Steps DATA Step Processing Setting Up SAS Libraries Importing and Exporting Data Viewing and Summarizing Data Sorting and De-duplicating Data Filtering Data and Conditional Logic SAS Functions Formatting Variables Combining and Aggregating Data The Simple Guide to SAS provides solutions to common business problems, identifies pitfalls to avoid, and includes sample code with data for readers to practice their knowledge.

The Complete Power BI Interview Guide is your companion to mastering Power BI roles and acing data analyst interviews. With hands-on skills, expert tips, and targeted preparation strategies, this resource equips you to excel in interviews and certifications while navigating the competitive job market. What this Book will help me do Create a powerful professional brand to optimize your resume and online presence. Master essential Power BI skills including data modeling, DAX programming, and visualization. Prepare effectively for interviews with industry-relevant questions, answers, and insights. Gain an edge in the market by understanding hiring procedures and negotiation tactics. Develop comprehensive analytics solutions exemplified with real-world case studies. Author(s) Sandielly Ortega Polanco, Gogula Aryalingam, and Abu Bakar Nisar Alvi bring years of collective experience in data analytics, Power BI, and career mentorship. Their insights are drawn from extensive professional practice and their passion for empowering future data analysts. Together, they provide an approachable and practical guide to securing roles in the competitive landscape of data analytics. Who is it for? This book is ideal for aspiring data analysts, business intelligence developers, or those shifting into Power BI roles who wish to enhance their knowledge and refine their strategies for interview success. It speaks to both newcomers to the field and seasoned professionals aiming to elevate their expertise.

Advance your Power BI skills by adding AI to your repertoire at a practice level. With this practical book, business-oriented software engineers and developers will learn the terminologies, practices, and strategy necessary to successfully incorporate AI into your business intelligence estate. Jen Stirrup, CEO of AI and BI leadership consultancy Data Relish, and Thomas Weinandy, research economist at Upside, show you how to use data already available to your organization. Springboarding from the skills that you already possess, this book adds AI to your organization's technical capability and expertise with Microsoft Power BI. By using your conceptual knowledge of BI, you'll learn how to choose the right model for your AI work and identify its value and validity. Use Power BI to build a good data model for AI Demystify the AI terminology that you need to know Identify AI project roles, responsibilities, and teams for AI Use AI models, including supervised machine learning techniques Develop and train models in Azure ML for consumption in Power BI Improve your business AI maturity level with Power BI Use the AI feedback loop to help you get started with the next project

In "Extending Power BI with Python and R," you'll learn how to enhance your Power BI reports and analyses by leveraging the advanced analytical capabilities of Python and R. From working with large datasets to creating sophisticated visuals, this book provides practical instructions on powerful techniques that unlock new possibilities in Power BI. What this Book will help me do Configure and optimize Python and R integration in Power BI for enhanced performance. Implement advanced data transformation techniques to overcome Power BI limitations. Develop advanced visualizations using the Grammar of Graphics in Python and R. Analyze data leveraging powerful Python and R algorithms, including machine learning models. Secure your Power BI data with anonymization and pseudonymization techniques. Author(s) None Zavarella is a data analytics expert with years of practical experience in business intelligence and data analytics. With a passion for enhancing data tools with programming languages like Python and R, they bring practical knowledge and technical acumen to this comprehensive resource. They aim to make complex concepts approachable to their readers. Who is it for? This book is aimed at professionals such as business analysts, business intelligence specialists, and data scientists who leverage Power BI for their data solutions. Readers should have a working knowledge of Power BI basics and a desire to extend its capabilities. A familiarity with Python and R programming basics is also beneficial for following the advanced techniques presented.

Master the art and science of analytics engineering with 'Fundamentals of Analytics Engineering.' This book takes you on a comprehensive journey from understanding foundational concepts to implementing end-to-end analytics solutions. You'll gain not just theoretical knowledge but practical expertise in building scalable, robust data platforms to meet organizational needs. What this Book will help me do Design and implement effective data pipelines leveraging modern tools like Airbyte, BigQuery, and dbt. Adopt best practices for data modeling and schema design to enhance system performance and develop clearer data structures. Learn advanced techniques for ensuring data quality, governance, and observability in your data solutions. Master collaborative coding practices, including version control with Git and strategies for maintaining well-documented codebases. Automate and manage data workflows efficiently using CI/CD pipelines and workflow orchestrators. Author(s) Dumky De Wilde, alongside six co-authors-experienced professionals from various facets of the analytics field-delivers a cohesive exploration of analytics engineering. The authors blend their expertise in software development, data analysis, and engineering to offer actionable advice and insights. Their approachable ethos makes complex concepts understandable, promoting educational learning. Who is it for? This book is a perfect fit for data analysts and engineers curious about transitioning into analytics engineering. Aspiring professionals as well as seasoned analytics engineers looking to deepen their understanding of modern practices will find guidance. It's tailored for individuals aiming to boost their career trajectory in data engineering roles, addressing fundamental to advanced topics.

Dive into the comprehensive world of data transformation with "The Definitive Guide to Power Query (M)". This book empowers you with the knowledge and skills necessary to effectively utilize Power Query for complex data transformation tasks. You will develop expertise in practical techniques, advanced M language concepts, and optimization strategies. What this Book will help me do Understand the fundamentals of Power Query and its functionalities. Learn to perform complex data transformations using various Power Query functions. Gain insight into advanced M language structures such as custom functions and nested expressions. Develop skills in error handling and debugging to streamline your data processes. Master performance optimization techniques for efficient data handling with Power Query. Author(s) Gregory Deckler, Rick de Groot, and Melissa de Korte are seasoned professionals in business intelligence and data analytics. With years of experience using Power Query, they bring a wealth of knowledge and practical insight into tackling real-world data problems. Their combined expertise ensures a clear and immersive learning experience for readers, guiding them through fundamental to advanced topics. Who is it for? This book is ideal for business analysts, data professionals, and power users who wish to advance their data transformation capabilities. If you're someone with foundational experience in Power Query looking to become proficient or an industry professional aiming to optimize workflows, this book is tailored to suit your goals.

This fully updated second edition is an essential introduction to inferential statistics. It is the first introductory statistics text to use an estimation approach with meta-analysis from the start and also to explain the new and exciting Open Science practices, which encourage replication and enhance the trustworthiness of research.

This book highlights how optimized big data applications can be used for patient monitoring and clinical diagnosis. In fact, IoT-based applications are data-driven and mostly employ modern optimization techniques. The book also explores challenges, opportunities, and future research directions, discussing the stages of data collection and pre-processing, as well as the associated challenges and issues in data handling and setup.

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem and follow the steps in the solution. This book has something for everyone interested in machine learning. It also has material that will allow those with an interest in other technology areas to see how machine learning and MATLAB can help them solve problems in their areas of expertise. The chapter on data representation and MATLAB graphics includes new data types and additional graphics. Chapters on fuzzy logic, simple neural nets, and autonomous driving have new examples added. And there is a new chapter on spacecraft attitude determination using neural nets. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow you to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What You Will Learn Write code for machine learning, adaptive control, and estimation using MATLAB Use MATLAB graphics and visualization tools for machine learning Become familiar with neural nets Build expert systems Understand adaptive control Gain knowledge of Kalman Filters Who This Book Is For Software engineers, control engineers, university faculty, undergraduate and graduate students, hobbyists.

Microsoft 365 Excel introduces enhanced features that transform how business dashboards are built and maintained. This book guides you through creating dynamic, interactive dashboards that leverage these modern capabilities. From understanding the essential principles of effective dashboard design to mastering the latest tools like Power Query and dynamic array functions, you'll make the most of Excel's full potential. What this Book will help me do Understand the purpose and advantages of effective dashboards in business analytics. Use advanced Excel functions and tools such as Power Query and dynamic arrays to handle complex data workflows. Design visually engaging dashboards using charts and data visualizations that communicate key insights. Optimize dashboards for automation and real-time data updates, saving time and effort. Apply best practices and techniques for creating professional-grade Excel dashboards. Author(s) Michael Olafusi is a skilled data analyst and expert in Microsoft Excel, with years of experience leveraging Excel for business intelligence and analytics solutions. He enjoys teaching Excel users how to elevate their skills to create functional and visually impactful tools. Michael's approach combines clarity and practical advice, helping readers build proficiency and confidence. Who is it for? This book is perfect for Excel users who want to create professional dashboards for business decision support. It's especially useful for data analysts, financial analysts, business analysts, and those in similar roles. It requires a basic familiarity with Excel's interface and is ideal for those seeking to enhance their data presentation skills and automate repetitive reporting tasks.

"Cracking the Data Science Interview" is your ultimate resource for preparing for roles in the competitive field of data science. With this book, you'll explore essential topics such as Python, SQL, statistics, and machine learning, as well as learn practical skills for building portfolios and acing interviews. Follow its guidance and you'll be equipped to stand out in any data science interview. What this Book will help me do Confidently explain complex statistical and machine learning concepts. Develop models and deploy them while ensuring version control and efficiency. Learn and apply scripting skills in shell and Bash for productivity. Master Git workflows to handle collaborative coding in projects. Perfectly tailor portfolios and resumes to land data science opportunities. Author(s) Leondra R. Gonzalez, with years of data science and mentorship experience, co-authors this book with None Stubberfield, a seasoned expert in technology and machine learning. Together, they integrate their expertise to provide practical advice for navigating the data science job market. Who is it for? If you're preparing for data science interviews, this book is for you. It's ideal for candidates with a foundational knowledge of Python, SQL, and statistics looking to refine and expand their technical and professional skills. Professionals transitioning into data science will also find it invaluable for building confidence and succeeding in this rewarding field.

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.

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