'Hands-On Data Analysis with Pandas' guides you to gain expertise in the Python pandas library for data analysis and manipulation. With practical, real-world examples, you'll learn to analyze datasets, visualize data trends, and implement machine learning models for actionable insights. What this Book will help me do Understand and implement data analysis techniques with Python. Develop expertise in data manipulation using pandas and NumPy. Visualize data effectively with pandas visualization tools and seaborn. Apply machine learning techniques with Python libraries. Combine datasets and handle complex data workflows efficiently. Author(s) Stefanie Molin is a software engineer and data scientist with extensive experience in analytics and Python. She has worked with large data-driven systems and has a strong focus on teaching data analysis effectively. Stefanie's books are known for their practical, hands-on approach to solving real data problems. Who is it for? This book is perfect for aspiring data scientists, data analysts, and Python developers. Readers with beginner to intermediate skill levels in Python will find it accessible and informative. It is designed for those seeking to build practical data analysis skills. If you're looking to add data science and pandas to your toolkit, this book is ideal.

Want to use the power of R sooner rather than later? Don’t have time to plow through wordy texts and online manuals? Use this book for quick, simple code to get your projects up and running. It includes code and examples applicable to many disciplines. Written in everyday language with a minimum of complexity, each chapter provides the building blocks you need to fit R’s astounding capabilities to your analytics, reporting, and visualization needs. CRAN Recipes recognizes how needless jargon and complexity get in your way. Busy professionals need simple examples and intuitive descriptions; side trips and meandering philosophical discussions are left for other books. Here R scripts are condensed, to the extent possible, to copy-paste-run format. Chapters and examples are structured to purpose rather than particular functions (e.g., “dirty data cleanup” rather than the R package name “janitor”). Everyday language eliminatesthe need to know functions/packages in advance. What You Will Learn Carry out input/output; visualizations; data munging; manipulations at the group level; and quick data exploration Handle forecasting (multivariate, time series, logistic regression, Facebook’s Prophet, and others) Use text analytics; sampling; financial analysis; and advanced pattern matching (regex) Manipulate data using DPLYR: filter, sort, summarize, add new fields to datasets, and apply powerful IF functions Create combinations or subsets of files using joins Write efficient code using pipes to eliminate intermediate steps (MAGRITTR) Work with string/character manipulation of all types (STRINGR) Discover counts, patterns, and how to locate whole words Do wild-card matching, extraction, and invert-match Work with dates using LUBRIDATE Fix dirty data; attractive formatting; bad habits to avoid Who This Book Is For Programmers/data scientists with at least some prior exposure to R.

Bootstrapping is a conceptually simple statistical technique to increase the quality of estimates, conduct robustness checks and compute standard errors for virtually any statistic. This book provides an intelligible and compact introduction for students, scientists and practitioners. It not only gives a clear explanation of the underlying concepts but also demonstrates the application of bootstrapping using Python and Stata.

Data analytics may seem daunting, but if you're an experienced Excel user, you have a unique head start. With this hands-on guide, intermediate Excel users will gain a solid understanding of analytics and the data stack. By the time you complete this book, you'll be able to conduct exploratory data analysis and hypothesis testing using a programming language. Exploring and testing relationships are core to analytics. By using the tools and frameworks in this book, you'll be well positioned to continue learning more advanced data analysis techniques. Author George Mount, founder and CEO of Stringfest Analytics, demonstrates key statistical concepts with spreadsheets, then pivots your existing knowledge about data manipulation into R and Python programming. This practical book guides you through: Foundations of analytics in Excel: Use Excel to test relationships between variables and build compelling demonstrations of important concepts in statistics and analytics From Excel to R: Cleanly transfer what you've learned about working with data from Excel to R From Excel to Python: Learn how to pivot your Excel data chops into Python and conduct a complete data analysis

Perform fast interactive analytics against different data sources using the Trino high-performance distributed SQL query engine. With this practical guide, you'll learn how to conduct analytics on data where it lives, whether it's Hive, Cassandra, a relational database, or a proprietary data store. Analysts, software engineers, and production engineers will learn how to manage, use, and even develop with Trino. Initially developed by Facebook, open source Trino is now used by Amazon, Google, LinkedIn, Lyft, Netflix, Pinterest, Salesforce, Shopify, and many other companies. Matt Fuller, Manfred Moser, and Martin Traverso show you how a single Trino query can combine data from multiple sources to allow for analytics across your entire organization. Get started: Explore Trino's use cases and learn about tools that will help you connect to Trino and query data Go deeper: Learn Trino's internal workings, including how to connect to and query data sources with support for SQL statements, operators, functions, and more Put Trino in production: Secure Trino, monitor workloads, tune queries, and connect more applications; learn how other organizations apply Trino

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

Improve your organization's PR process and quantify its impact on business objectives with this guide to using purpose-built tools, new data sources and strategic insights.

Harness data within the supply chain using this accessible guide on how to examine, evaluate and apply business analytics models.

Write automated unit tests for the ABAP language. This book teaches programmers using simple examples and metaphors and explains the underlying concepts of writing effective automated unit tests. Many, if not most, ABAP programmers learned their programming and testing skills before the ABAP development environment provided an automated unit testing facility. Automated Unit Testing with ABAP: A Practical Approach offers hope and salvation to ABAP programmers who continue to toil with antiquated manual unit testing processes, taking them by the hand and lifting them out of that dungeon of despair with a modern and proven alternative. It begins by explaining how the xUnit family of automated testing frameworks provides a quick and effective means of insuring high-quality software. It then focuses on the ABAP Unit Testing Facility, the xUnit framework applicable specifically to the ABAP language, showing how it can be used to bring ABAP applications underautomated testing control, from old legacy applications to those newly written. Whereas xUnit testing has been widely accepted with developers writing in many other programming languages, it is an unfortunate fact in the ABAP community that many programmers still are unfamiliar with xUnit concepts and do not know how to begin implementing automated unit testing into their development process. This book demonstrates how to refactor programs so they become designed for testability, showing how to use process encapsulation and test isolation to facilitate automated testing, including a thorough explanation of test-driven development and the use of test doubles. The book: Shows how to write automated unit tests for ABAP Instills ABAP programmers with the confidence to refactor poorly written code Explains how an automated testing harness facilitates rapid software development Teaches how to utilize test-driven development (TDD) withABAP Offers advice and tips on the best ways to write automated unit tests What You Will Learn Become familiar with the xUnit approach to testing Know the ABAP statements that interfere with running automated unit tests and how to accommodate them Understand what it means to isolate code for testing and how this is achieved Gain the confidence to refactor poorly written code Make ABAP programs designed for testability Reap the benefits of spending less time manually unit testing ABAP programs Use test-driven development (TDD) with ABAP programming Use configurable test doubles in ABAP Who This Book Is For ABAP programmers who remain unfamiliar with the automated unit testing facility and those who already use it butwant to improve their skill writing and using automated tests. The book addresses the reluctance and trepidation felt by procedural ABAP programmers who need to know some object-oriented concepts to use this facility, expands their horizons, and helps them step through the doorway leading to a different approach to program design.

Dive into the intricacies of data cleaning, a crucial aspect of any data science and machine learning pipeline, with 'Cleaning Data for Effective Data Science.' This comprehensive guide walks you through tools and methodologies like Python, R, and command-line utilities to prepare raw data for analysis. Learn practical strategies to manage, clean, and refine data encountered in the real world. What this Book will help me do Understand and utilize various data formats such as JSON, SQL, and PDF for data ingestion and processing. Master key tools like pandas, SciPy, and Tidyverse to manipulate and analyze datasets efficiently. Develop heuristics and methodologies for assessing data quality, detecting bias, and identifying irregularities. Apply advanced techniques like feature engineering and statistical adjustments to enhance data usability. Gain confidence in handling time series data by employing methods for de-trending and interpolating missing values. Author(s) David Mertz has years of experience as a Python programmer and data scientist. Known for his engaging and accessible teaching style, David has authored numerous technical articles and books. He emphasizes not only the technicalities of data science tools but also the critical thinking that approaches solutions creatively and effectively. Who is it for? 'Cleaning Data for Effective Data Science' is designed for data scientists, software developers, and educators dealing with data preparation. Whether you're an aspiring data enthusiast or an experienced professional looking to refine your skills, this book provides essential tools and frameworks. Prior programming knowledge, particularly in Python or R, coupled with an understanding of statistical fundamentals, will help you make the most of this resource.

Master the fundamentals of SPSS with this newly updated and instructive resource The newly and thoroughly revised Second Edition of SPSS Essentials delivers a comprehensive guide for students in the social sciences who wish to learn how to use the Statistical Package for the Social Sciences (SPSS) for the effective collection, management, and analysis of data. The accomplished researchers and authors provide readers with the practical nuts and bolts of SPSS usage and data entry, with a particular emphasis on managing and manipulating data. The book offers an introduction to SPSS, how to navigate it, and a discussion of how to understand the data the reader is working with. It also covers inferential statistics, including topics like hypothesis testing, one-sample Z-testing, T-testing, ANOVAs, correlations, and regression. Five unique appendices round out the text, providing readers with discussions of dealing with real-world data, troubleshooting, advanced data manipulations, and new workbook activities. SPSS Essentials offers a wide variety of features, including: A revised chapter order, designed to match the pacing and content of typical undergraduate statistics classes An explanation of when particular inferential statistics are appropriate for use, given the nature of the data being worked with Additional material on understanding your data sample, including discussions of SPSS output and how to find the most relevant information A companion website offering additional problem sets, complete with answers Perfect for undergraduate students of the social sciences who are just getting started with SPSS, SPSS Essentials also belongs on the bookshelves of advanced placement high school students and practitioners in social science who want to brush up on the fundamentals of this powerful and flexible software package.

Advances in Longitudinal Survey Methodology Explore an up-to-date overview of best practices in the implementation of longitudinal surveys from leading experts in the field of survey methodology Advances in Longitudinal Survey Methodology delivers a thorough review of the most current knowledge in the implementation of longitudinal surveys. The book provides a comprehensive overview of the many advances that have been made in the field of longitudinal survey methodology over the past fifteen years, as well as extending the topic coverage of the earlier volume, “Methodology of Longitudinal Surveys”, published in 2009. This new edited volume covers subjects like dependent interviewing, interviewer effects, panel conditioning, rotation group bias, measurement of cognition, and weighting. New chapters discussing the recent shift to mixed-mode data collection and obtaining respondents’ consent to data linkage add to the book’s relevance to students and social scientists seeking to understand modern challenges facing data collectors today. Readers will also benefit from the inclusion of: A thorough introduction to refreshment sampling for longitudinal surveys, including consideration of principles, sampling frame, sample design, questionnaire design, and frequency An exploration of the collection of biomarker data in longitudinal surveys, including detailed measurements of ill health, biological pathways, and genetics in longitudinal studies An examination of innovations in participant engagement and tracking in longitudinal surveys, including current practices and new evidence on internet and social media for participant engagement. An invaluable source for post-graduate students, professors, and researchers in the field of survey methodology, Advances in Longitudinal Survey Methodology will also earn a place in the libraries of anyone who regularly works with or conducts longitudinal surveys and requires a one-stop reference for the latest developments and findings in the field.

Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting—from the basics all the way to leading-edge models—will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting. Events around the book Link to a De Gruyter Online Event in which the author Nicolas Vandeput together with Stefan de Kok, supply chain innovator and CEO of Wahupa; Spyros Makridakis, professor at the University of Nicosia and director of the Institute For the Future (IFF); and Edouard Thieuleux, founder of AbcSupplyChain, discuss the general issues and challenges of demand forecasting and provide insights into best practices (process, models) and discussing how data science and machine learning impact those forecasts. The event will be moderated by Michael Gilliland, marketing manager for SAS forecasting software: https://youtu.be/1rXjXcabW2s

Machine reading comprehension (MRC) is a cutting-edge technology in natural language processing (NLP). MRC has recently advanced significantly, surpassing human parity in several public datasets. It has also been widely deployed by industry in search engine and quality assurance systems. Machine Reading Comprehension: Algorithms and Practice performs a deep-dive into MRC, offering a resource on the complex tasks this technology involves. The title presents the fundamentals of NLP and deep learning, before introducing the task, models, and applications of MRC. This volume gives theoretical treatment to solutions and gives detailed analysis of code, and considers applications in real-world industry. The book includes basic concepts, tasks, datasets, NLP tools, deep learning models and architecture, and insight from hands-on experience. In addition, the title presents the latest advances from the past two years of research. Structured into three sections and eight chapters, this book presents the basis of MRC; MRC models; and hands-on issues in application. This book offers a comprehensive solution for researchers in industry and academia who are looking to understand and deploy machine reading comprehension within natural language processing. Presents the first comprehensive resource on machine reading comprehension (MRC) Performs a deep-dive into MRC, from fundamentals to latest developments Offers the latest thinking and research in the field of MRC, including the BERT model Provides theoretical discussion, code analysis, and real-world applications of MRC Gives insight from research which has led to surpassing human parity in MRC

Tableau Prep Cookbook is your practical guide to mastering Tableau Prep Builder for data preparation. Through real-world examples, you will learn techniques to clean, combine, and transform your data, enabling you to create robust pipelines for analytics and insights. Gain hands-on experience with concepts like data cleaning, advanced calculations, and preparing data for Business Intelligence tools. What this Book will help me do Master cleaning and combining data sources for analysis using Tableau Prep. Learn to create and deploy workflows for data preparation within your organization. Develop proficiency in building robust datasets for BI and analytics applications. Apply advanced techniques like scripting and custom calculations in Tableau Prep. Get hands-on experience by working through realistic, practical data scenarios. Author(s) None Kleine is an experienced data analytics professional with a passion for empowering organizations through robust data pipelines. Drawing from years of experience in BI tools and data preparation, None presents Tableau Prep Cookbook with a clear, actionable approach to learning. Their expertise ensures that readers gain practical skills to use Tableau Prep effectively. Who is it for? This book is perfect for data analysts, business intelligence professionals, and Tableau users looking to add Tableau Prep to their skills. If you're starting with beginner knowledge in data preparation or are looking to enhance your ability to manage data workflows, this book is designed for you. Gain the skills you need to prepare data effectively using Tableau Prep and elevate your analytics capabilities.

Transform the way your business works with easy-to-build apps. With this updated and expanded second edition, you can build business apps that work with your company's systems and databases, without having to enlist the expertise of costly, professionally trained software developers. In this new edition, business applications expert Tim Leung offers step-by-step guidance on how you can improve all areas of your business. He shows how you can replace manual or paper processes with modern apps that run on phone or tablet devices. For administrative and back-office operations, he covers how to build apps with workflow and dashboard capabilities. To facilitate collaboration with customers and clients, you’ll learn how to build secure web portals with data entry capabilities, including how to customize those portals with code. This hands-on new edition has 10 new chapters—including coverage on model-driven and portal apps, artificial intelligence, building components using the Power Apps Component Framework, using PowerShell for administration, and more—complete with context, explanatory screenshots, and non-technical terminology. What You Will Learn Create offline capable mobile apps and responsive web apps Carry out logic, data access, and data entry through formulas Embellish apps with charting, file handling, photo, barcode, and location features Set up Common Data Service, SharePoint, and SQL data sources Use AI to predict outcomes, recognize images, and analyze sentiment Integrate apps with external web services and automate tasks with Power Automate Build reusable code and canvas components, make customizations with JavaScript Transfer apps and data, and secure, administer, and monitor Power Apps environments Who This Book Is For Beginners and non-developers, and assumes no prior knowledge of Power Apps

Delve into the art of time series forecasting with the comprehensive power of Facebook Prophet. This tool enables users to develop precise forecasting models with simplicity and effectiveness. Through this book, you'll explore Prophet's core functionality and advanced configurations, equipping yourself with the knowledge to proficiently model and predict data trends. What this Book will help me do Build intuitive and effective forecasting models using Facebook Prophet. Understand the role and implementation of seasonality and holiday effects in time series data. Identify and address outliers and special data events effectively. Optimize forecasts using advanced techniques like hyperparameter tuning and additional regressors. Evaluate and deploy forecasting models in production settings for practical applications. Author(s) Greg Rafferty is a seasoned data science professional with extensive experience in time series forecasting. Having worked on diverse forecasting projects, Greg brings a unique perspective that integrates practicality and depth. His approachable writing style makes complex topics accessible and actionable. Who is it for? This book is tailored for data scientists, analysts, and developers seeking to enhance their forecasting capabilities using Python. If you have a grounding in Python and a basic understanding of forecasting principles, you will find this book a valuable resource to sharpen your expertise and achieve new forecasting precision.

Extending Microsoft Power Apps with Power Apps Component Framework is your ultimate guide to mastering the creation and deployment of advanced code components within the Microsoft Power Apps environment. You'll explore the framework's fundamentals and advanced techniques through hands-on examples, enabling you to leverage its full capabilities. What this Book will help me do Understand the Power Apps Component Framework and its development lifecycle. Develop custom controls using modern web development technologies. Debug and troubleshoot components effectively with the help of tools like Fiddler. Implement advanced concepts like authentication profiles and data caching. Deploy and configure components across both model-driven and canvas apps. Author(s) None Naglekar is a seasoned software developer specializing in Microsoft Power Platform and has been crafting enterprise solutions using Power Apps for several years. With a strong background in both platform extensibility and modern web development practices, None brings extensive practical knowledge to this book. Their teaching approach focuses on clarity and real-world applications, making complex topics approachable and actionable. Who is it for? This book is written for developers with some experience in Power Apps and web development who are looking to enhance their skills in extending Power Apps functionality. Ideal for professionals aiming to build advanced custom components using the Power Apps Component Framework, this book helps you turn ideas into deployable solutions. It's suited for programmers who want to deepen their understanding of extending Microsoft's low-code platform.

Get up and running with SAS using Ron Cody’s easy-to-follow, step-by-step guide. Aimed at beginners, Getting Started with SAS Programming: Using SAS Studio in the Cloud uses short examples to teach SAS programming from the basics to more advanced topics in the point-and-click interactive environment of SAS Studio. To begin, you will learn how to register for SAS OnDemand for Academics, an online delivery platform for teaching and learning statistical analysis that provides free access to SAS software via the cloud. The first part of the book shows you how to use SAS Studio built-in tasks to produce a report, summarize data, and create charts and graphs. It also describes how you can perform basic statistical tests using the interactive point-and-click environment. The second part of the book uses easy-to-follow examples to show you how to write your own SAS programs and how to use SAS procedures to perform a variety of tasks. This part of the book also explains how to read data from a variety of sources: text files, Excel workbooks, and CSV files. In order to get familiar with the SAS Studio environment, this book also shows you how to access dozens of interesting data sets that are included with the SAS OnDemand for Academics platform.

Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and computer science, bringing with it a new era of tremendous opportunities and challenges due to plenty of easily available criminal data for further analysis. This book provides innovative insights that will help obtain interventions to undertake emerging dynamic scenarios of criminal activities. Furthermore, it presents emerging issues, challenges and management strategies in public safety and crime control development across various domains. The book will play a vital role in improvising human life to a great extent. Researchers and practitioners working in the fields of data mining, machine learning and artificial intelligence will greatly benefit from this book, which will be a good addition to the state-of-the-art approaches collected for intelligent data analytics. It will also be very beneficial for those who are new to the field and need to quickly become acquainted with the best performing methods. With this book they will be able to compare different approaches and carry forward their research in the most important areas of this field, which has a direct impact on the betterment of human life by maintaining the security of our society. No other book is currently on the market which provides such a good collection of state-of-the-art methods for intelligent data analytics-based models for terror threat prediction, as intelligent data analytics is a newly emerging field and research in data mining and machine learning is still in the early stage of development.