Delve into the serverless world of Amazon Athena with the comprehensive book 'Serverless Analytics with Amazon Athena'. This guide introduces you to the power of Athena, showing you how to efficiently query data in Amazon S3 using SQL without the hassle of managing infrastructure. With clear instructions and practical examples, you'll master querying structured, unstructured, and semi-structured data seamlessly. What this Book will help me do Effectively query and analyze both structured and unstructured data stored in S3 using Amazon Athena. Integrate Athena with other AWS services to create powerful, secure, and cost-efficient data workflows. Develop ETL pipelines and machine learning workflows leveraging Athena's compatibility with AWS Glue. Monitor and troubleshoot Athena queries for consistent performance and build scalable serverless data solutions. Implement security best practices and optimize costs when managing your Athena-driven data solutions. Author(s) None Virtuoso, along with co-authors Mert Turkay Hocanin None and None Wishnick, brings a wealth of experience in cloud solutions, serverless technologies, and data engineering. They excel in demystifying complex technical topics and have a passion for empowering readers with practical skills and knowledge. Who is it for? This book is tailored for business intelligence analysts, application developers, and system administrators who want to harness Amazon Athena for seamless, cost-efficient data analytics. It suits individuals with basic SQL knowledge looking to expand their capabilities in querying and processing data. Whether you're managing growing datasets or building data-driven applications, this book provides the know-how to get it right.

Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: Techniques for computing and plotting probabilities Statistical analysis using Scipy How to organize datasets with clustering algorithms How to visualize complex multi-variable datasets How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. About the Technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the Book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's Inside Web scraping Organize datasets with clustering algorithms Visualize complex multi-variable datasets Train a decision tree machine learning algorithm About the Reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the Author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Quotes Valuable and accessible… a solid foundation for anyone aspiring to be a data scientist. - Amaresh Rajasekharan, IBM Corporation Really good introduction of statistical data science concepts. A must-have for every beginner! - Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland A full-fledged tutorial in data science including common Python libraries and language tricks! - Jean-François Morin, Laval University This book is a complete package for understanding how the data science process works end to end. - Ayon Roy, Internshala

Do you have a method for seeing all the data that passes through your organization? The need to democratize access to data and analytics, automate complex and tedious business processes, and amplify human output has led to analytic process automation (APA). Thousands of organizations across nearly every business and industry vertical use this software to accelerate data-driven business outcomes. This report examines the power of APA using technology, business, and real-world examples. If you're a technical business, analytics, or business intelligence leader, you'll learn how to use APA to tackle complex problems, increase productivity, and improve efficiency. You'll discover what APA means for your business and for you. This report explores: The importance of data: understand how data is transformed into information and insights for making business decisions Gathering data with APA: learn how APA differs from your current process Data democratization: grant data access to employees and empower them to analyze specific tasks and performance Data reporting: learn how APA blends data tables, fields, and values to help you search for insights at a granular level Analytics: explore new tools that use AI and ML to improve the analytic process

From a global pandemic to extreme weather, the events of 2020 and 2021 have caused organizations to make quick and constant adjustments to their strategy and operations. This transformation is likely to continue and have a major impact on analytics. Not only do responders to Experian's annual Global Data Management survey confirm more demand for data insights, but most of them also believe the lack of agility hurt their organization's responses to fast-changing business needs. With this O'Reilly report, you'll learn how organizations have begun to take new approaches to analytics for business reinvention and digital transformation. Chief analytics and data officers and data analytics, data science, data visualization leaders will explore converged analytics and find out how it differs from legacy and current analytics approaches. You'll see where your organization stands in its journey to convergence--and what you need to do next. This report helps you: Examine how three organizations in different industries and with different objectives have benefited from modern analytics Learn how analytics has evolved to support greater business agility at scale Examine the alignment of people, processes, tools, and data in converged analytics Learn the five stages of analytical competition and six dimensions for benchmarking maturity Explore practices that you can adopt to improve your analytics capabilities and your agility

The "Power Query Cookbook" is your comprehensive guide to mastering data preparation and transformation using Power Query. With this book, you'll learn to connect to data sources, reshape data to fit business requirements, and use both no-code transformations and custom M code solutions to unlock the full potential of your data. Step-by-step examples will guide you through optimizing dataflows in Power BI. What this Book will help me do Master connecting to various data sources and performing intuitive transformations using Power Query. Learn to reshape and enrich data to meet complex business requirements efficiently. Explore advanced capabilities of Power Query, including M code and online dataflows. Develop custom data transformations with a blend of GUI-based and M code techniques. Optimize the performance of Power BI Dataflows using best practices and diagnostics tools. Author(s) None Janicijevic is a seasoned expert in data analytics, specializing in Microsoft Power BI and Power Query. With years of experience in data engineering and a passion for teaching, None brings a clear, actionable, and results-driven approach to demystifying complex technical concepts. Their work empowers professionals with the tools they need to excel in data-driven decision-making. Who is it for? This book is designed for data analysts, business intelligence developers, and data engineers aiming to enhance their skills in data preparation using Power Query. If you have a basic understanding of Power BI and want to delve into integrating and optimizing data from multiple sources, this book is for you. It's ideal for professionals seeking practical insights and techniques to improve data transformations. Novices with some exposure to BI tools will also find the material accessible and rewarding.

Text As Data: Combining qualitative and quantitative algorithms within the SAS system for accurate, effective and understandable text analytics The need for powerful, accurate and increasingly automatic text analysis software in modern information technology has dramatically increased. Fields as diverse as financial management, fraud and cybercrime prevention, Pharmaceutical R&D, social media marketing, customer care, and health services are implementing more comprehensive text-inclusive, analytics strategies. Text as Data: Computational Methods of Understanding Written Expression Using SAS presents an overview of text analytics and the critical role SAS software plays in combining linguistic and quantitative algorithms in the evolution of this dynamic field. Drawing on over two decades of experience in text analytics, authors Barry deVille and Gurpreet Singh Bawa examine the evolution of text mining and cloud-based solutions, and the development of SAS Visual Text Analytics. By integrating quantitative data and textual analysis with advanced computer learning principles, the authors demonstrate the combined advantages of SAS compared to standard approaches, and show how approaching text as qualitative data within a quantitative analytics framework produces more detailed, accurate, and explanatory results. Understand the role of linguistics, machine learning, and multiple data sources in the text analytics workflow Understand how a range of quantitative algorithms and data representations reflect contextual effects to shape meaning and understanding Access online data and code repositories, videos, tutorials, and case studies Learn how SAS extends quantitative algorithms to produce expanded text analytics capabilities Redefine text in terms of data for more accurate analysis This book offers a thorough introduction to the framework and dynamics of text analytics—and the underlying principles at work—and provides an in-depth examination of the interplay between qualitative-linguistic and quantitative, data-driven aspects of data analysis. The treatment begins with a discussion on expression parsing and detection and provides insight into the core principles and practices of text parsing, theme, and topic detection. It includes advanced topics such as contextual effects in numeric and textual data manipulation, fine-tuning text meaning and disambiguation. As the first resource to leverage the power of SAS for text analytics, Text as Data is an essential resource for SAS users and data scientists in any industry or academic application.

Practical Data Science with Python guides you through the entire process of leveraging Python tools to analyze and gain insights from data. You'll start with foundational concepts and coding essentials, progressing through statistical analysis, machine learning techniques, and ethical considerations. What this Book will help me do Clean, prepare, and explore data using pandas and NumPy. Understand and implement machine learning models such as random forests and support vector machines. Perform statistical tests and analyze distributions to enhance data insights. Utilize SQL with Python for efficient data interaction. Generate automated reports and dashboards for data storytelling. Author(s) Nathan George has extensive professional experience as a data scientist and Python developer. He specializes in the application of machine learning and statistical methods to solve real-world problems. His writing combines technical depth with an approachable style, aiming to provide readers with actionable knowledge and skills. Who is it for? This book is perfect for data science beginners who have a basic understanding of Python and want to build practical data analysis skills. Students in analytics programs or professionals looking to transition into a data science role will find value in its approachable yet comprehensive coverage. Aspiring data analysts and career changers will gain firsthand exposure to Python-based data science best practices. If you're eager to develop practical, hands-on experience in the data science field, this is the guide for you.

Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially the last 25 years there has been an explosion of terms and methods born that automate and improve decision-making and operations.

In 'Data Science for Marketing Analytics', you'll embark on a journey that integrates the power of data analytics with strategic marketing. With a focus on practical application, this guide walks you through using Python to analyze datasets, implement machine learning models, and derive data-driven insights. What this Book will help me do Gain expertise in cleaning, exploring, and visualizing marketing data using Python. Build machine learning models to predict customer behavior and sales outcomes. Leverage unsupervised learning techniques for effective customer segmentation. Compare and optimize predictive models using advanced evaluation methods. Master Python libraries like pandas and Matplotlib for data manipulation and visualization. Author(s) Mirza Rahim Baig, Gururajan Govindan, and Vishwesh Ravi Shrimali combine their extensive expertise in data analytics and marketing to bring you this comprehensive guide. Drawing from years of applying analytics in real-world marketing scenarios, they provide a hands-on approach to learning data science tools and techniques. Who is it for? This book is perfect for marketing professionals and analysts eager to harness the capabilities of Python to enhance their data-driven strategies. It is also ideal for data scientists looking to apply their skills in marketing across various roles. While a basic understanding of data analysis and Python will help, all key concepts are introduced comprehensively for beginners.

By reading "Data Analytics Made Easy," you'll gain a solid understanding of data analysis and visualization without requiring coding skills. This book emphasizes practical knowledge and use cases, covering storytelling, automation, machine learning, and business dashboards with tools like KNIME and Power BI. What this Book will help me do Understand the fundamentals of data analytics and how to leverage data for business insights. Create and automate data workflows using the no-code KNIME Analytics Platform. Develop interactive dashboards and data visualizations with Microsoft Power BI. Learn the basics of machine learning and how to apply models for business use. Enhance presentations and influence decisions through effective data storytelling. Author(s) None De Mauro is an experienced author and professional in the field of data analytics. Passionate about making complex topics approachable, None specializes in explaining technical concepts in simpler terms, ensuring readers can easily grasp and apply them in their work. Who is it for? This book is perfect for professionals or beginners who want to work with and interpret data effectively. Ideal for individuals in business roles or management positions looking to enhance their skills in data analytics and build a foundational understanding of machine learning and visualization.

Elevate your reports with more user control and interactive elements Want to create exciting, user-friendly visualizations to bring greater intelligence to your organization? By mastering the full power of SAS Visual Analytics, you can add features that will enhance your reports and bring more depth and insight to your data. Interactive Reports in SAS Visual Analytics: Advanced Features and Customization is for experienced users who want to harness the advanced functionality of Visual Analytics on SAS Viya to create visualizations or augment existing reports. The book is full of real-world examples and step-by-step instructions to help you unlock the full potential of your reports. In this book, you will learn how to create interactive URL links to external websites use parameters to give the viewer more control add custom graphs and maps execute SAS code using SAS Viya jobs and more!

If you want to increase Tableau's value to your organization, this practical book has your back. Authors Ann Jackson and Luke Stanke guide data analysts through strategies for solving real-world analytics problems using Tableau. Starting with the basics and building toward advanced topics such as multidimensional analysis and user experience, you'll explore pragmatic and creative examples that you can apply to your own data. Staying competitive today requires the ability to quickly analyze and visualize data and make data-driven decisions. With this guide, data practitioners and leaders alike will learn strategies for building compelling and purposeful visualizations, dashboards, and data products. Every chapter contains the why behind the solution and the technical knowledge you need to make it work. Use this book as a high-value on-the-job reference guide to Tableau Visualize different data types and tackle specific data challenges Create compelling data visualizations, dashboards, and data products Learn how to generate industry-specific analytics Explore categorical and quantitative analysis and comparisons Understand geospatial, dynamic, statistical, and multivariate analysis Communicate the value of the Tableau platform to your team and to stakeholders

Public Policy Analytics teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. This book will appeal to researchers and data scientists at the intersection of data analytics and public policy, and readers who wish to understand how algorithms will affect the future of government.

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.

Discover a new, demand-centric framework for forecasting and demand planning In Consumption-Based Forecasting and Planning, thought leader and forecasting expert Charles W. Chase delivers a practical and novel approach to retail and consumer goods companies demand planning process. The author demonstrates why a demand-centric approach relying on point-of-sale and syndicated scanner data is necessary for success in the new digital economy. The book showcases short- and mid-term demand sensing and focuses on disruptions to the marketplace caused by the digital economy and COVID-19. You’ll also learn: How to improve demand forecasting and planning accuracy, reduce inventory costs, and minimize waste and stock-outs What is driving shifting consumer demand patterns, including factors like price, promotions, in-store merchandising, and unplanned and unexpected events How to apply analytics and machine learning to your forecasting challenges using proven approaches and tactics described throughout the book via several case studies. Perfect for executives, directors, and managers at retailers, consumer products companies, and other manufacturers, Consumption-Based Forecasting and Planning will also earn a place in the libraries of sales, marketing, supply chain, and finance professionals seeking to sharpen their understanding of how to predict future consumer demand.

Applying knowledge in the right context is the most powerful lever businesses can use to become agile, creative, and resilient. Knowledge graphs add context, meaning, and utility to business data. They drive intelligence into data for unparalleled automation and visibility into processes, products, and customers. Businesses use knowledge graphs to anticipate downstream effects, make decisions based on all relevant information, and quickly respond to dynamic markets. In this report for chief information and data officers, Jesus Barassa, Amy E. Hodler, and Jim Webber from Neo4j show how to use knowledge graphs to gain insights, reveal a flexible and intuitive representation of complex data relationships, and make better predictions based on holistic information. Explore knowledge graph mechanics and common organizing principles Build and exploit a connected representation of your enterprise data environment Use decisioning knowledge graphs to explore the advantages of adding relationships to data analytics and data science Conduct virtual testing using software versions of real-world processes Deploy knowledge graphs for more trusted data, higher accuracies, and better reasoning for contextual AI

Data science and analytics have emerged as the most desired fields in driving business decisions. Using the techniques and methods of data science, decision makers can uncover hidden patterns in their data, develop algorithms and models that help improve processes and make key business decisions. Data science is a data driven decision making approach that uses several different areas and disciplines with a purpose of extracting insights and knowledge from structured and unstructured data. The algorithms and models of data science along with machine learning and predictive modeling are widely used in solving business problems and predicting future outcomes. This book combines the key concepts of data science and analytics to help you gain a practical understanding of these fields. The four different sections of the book are divided into chapters that explain the core of data science. Given the booming interest in data science, this book is timely and informative.

Collect, analyze and leverage people data for improved business performance.

In a crowded field of data visualization and analytics tools, Tableau Desktop has emerged as the clear leader. This is partly due to its ease of use, but once you dive into Tableau's extensive feature set, you'll understand just how powerful and flexible this software can be for your business or organization. With this handy pocket reference, author Ryan Sleeper (Innovative Tableau) shows you how to translate the vast amounts of data into useful information. Tableau has done an amazing job of making valuable insights accessible to analysts and executives who would otherwise need to rely on IT. This book quickly guides you through Tableau Desktop's learning curve. You'll learn: How to shape data for use with Tableau Desktop How to create the most effective chart types Core concepts including discrete versus continuous Must-know technical features including filters, parameters, and sets Key syntax for creating the most useful analyses How to bring it all together with dashboardsAnd more!

Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis. Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, thispractical book provides complete examples and exercises in R and Python to help you gain more insight from your data--immediately. Understand the specifics of behavioral data Explore the differences between measurement and prediction Learn how to clean and prepare behavioral data Design and analyze experiments to drive optimal business decisions Use behavioral data to understand and measure cause and effect Segment customers in a transparent and insightful way