This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You’ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy. Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics. What You Will Learn Master the mathematical foundations required for business analytics Understand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given task Use R and Python to develop descriptive models, predictive models, and optimize models Interpret and recommend actions based on analytical model outcomes Who This Book Is For Software professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.

An Introduction to Economic Dynamics provides a framework for students to appreciate and understand the basic intuition behind economic models and to experiment with those models using simulation techniques in MATLAB.

This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems.

Discover how to effectively forecast time series data using Prophet, the versatile open-source tool developed by Meta. Whether you're a business analyst or a machine learning expert, this book provides comprehensive insights into creating, diagnosing, and refining forecasting models. By mastering Prophet, you'll be equipped to make accurate predictions that drive decisions. What this Book will help me do Master the core principles of using Prophet for time series forecasting. Ensure your forecasts are accurate and robust for better decision-making. Gain experience in handling real-world forecasting challenges, like seasonality and outliers. Learn how to fine-tune and optimize models using additional regressors. Understand productionalization of forecasting models to apply solutions at scale. Author(s) Greg Rafferty is a seasoned data scientist specializing in time series analysis and machine learning. With years of practical experience building forecasting models in industries ranging from finance to e-commerce, Greg is dedicated to teaching accessible and actionable approaches to data science. Through clear explanations and practical examples, he empowers readers to solve challenging forecasting problems with confidence. Who is it for? Ideal for data scientists, business analysts, machine learning engineers, and software developers seeking to enhance their forecasting skills with Prophet. Whether you're familiar with time series concepts or just starting to explore forecasting methods, this book helps you advance from fundamental understanding to practical application of state-of-the-art techniques for impactful results.

BIOINFORMATICS TOOLS FOR Pharmaceutical DRUG PRODUCT DLEVELOPMENT A timely book that details bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies, for drug development in the pharmaceutical and medical sciences industries. The book contains 17 chapters categorized into 3 sections. The first section presents the latest information on bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies. The following 2 sections include bioinformatics tools for the pharmaceutical sector and the healthcare sector. Bioinformatics brings a new era in research to accelerate drug target and vaccine design development, improving validation approaches as well as facilitating and identifying side effects and predicting drug resistance. As such, this will aid in more successful drug candidates from discovery to clinical trials to the market, and most importantly make it a more cost-effective process overall. Readers will find in this book: Applications of bioinformatics tools for pharmaceutical drug product development like process development, pre-clinical development, clinical development, commercialization of the product, etc.; The ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach; The broad and deep background, as well as updates, on recent advances in both medicine and AI/ML that enable the application of these cutting-edge bioinformatics tools. Audience The book will be used by researchers and scientists in academia and industry including drug developers, computational biochemists, bioinformaticians, immunologists, pharmaceutical and medical sciences, as well as those in artificial intelligence and machine learning.

This volume deals with two complementary topics. On one hand the book deals with the problem of determining the the probability distribution of a positive compound random variable, a problem which appears in the banking and insurance industries, in many areas of operational research and in reliability problems in the engineering sciences. On the other hand, the methodology proposed to solve such problems, which is based on an application of the maximum entropy method to invert the Laplace transform of the distributions, can be applied to many other problems. The book contains applications to a large variety of problems, including the problem of dependence of the sample data used to estimate empirically the Laplace transform of the random variable. Contents Introduction Frequency models Individual severity models Some detailed examples Some traditional approaches to the aggregation problem Laplace transforms and fractional moment problems The standard maximum entropy method Extensions of the method of maximum entropy Superresolution in maxentropic Laplace transform inversion Sample data dependence Disentangling frequencies and decompounding losses Computations using the maxentropic density Review of statistical procedures

Statistical Power Analysis explains the key concepts in statistical power analysis and illustrates their application in both tests of traditional null hypotheses. It provides readers with the tools to understand and perform power analyses for virtually all the statistical methods used in the social and behavioral sciences.

With hundreds of startups founded each year, the relatively new field of data-focused biotech—or TechBio—is growing rapidly. But without enough experienced practitioners to go around, most organizations hire data scientists with minimal biotech experience and lab scientists who've taken a crash course in data science. This arrangement is problematic. The way lab scientists and data scientists think and work is fundamentally different. But there is a solution. This report introduces biocode principles to help these scientists reframe the way they think about their role, their team's role, and the tools they use to fulfill those roles. Lab and data scientists alike will learn how to address the underlying issues so they can focus on solving these technology problems together. Each of the following chapters presents a vital biocode principle: "Defining Objectives" explores how to broaden the way teams view their work, shifting from purely technical objectives to organizational-level scientific objectives "Building Collaborations" encourages teams to focus their energy on collaboration with partner teams rather than guard their time for technical work "Deploying Tooling" covers ways to coordinate each team's work with the cadence of experiments and lab work

"The Kaggle Workbook" is an engaging and practical guide for anyone looking to excel in Kaggle competitions by learning from real past case studies and hands-on exercises. Inside, you'll dive deep into key data science concepts, explore how Kaggle Grandmasters tackle challenges, and apply new skills to your own projects. What this Book will help me do Master the methodology used in past Kaggle competitions for real-world applications. Discover and implement advanced data science techniques such as gradient boosting and NLP. Build a portfolio that demonstrates hands-on experience solving complex data problems. Learn time-series forecasting and computer vision by exploring detailed case studies. Develop a practical mindset for competitive data science problem solving. Author(s) Konrad Banachewicz and Luca Massaron bring their expertise as Kaggle Grandmasters to the pages of this book. With extensive experience in data science and collaborative problem-solving, they guide readers through practical exercises with a clear, approachable style. Their passion for sharing knowledge shines through in every chapter. Who is it for? "The Kaggle Workbook" is ideal for aspiring and experienced data scientists who want to sharpen their competitive data science skills. It caters to those with a foundational knowledge of data science and an interest in enhancing it through practical exercises. The book is a perfect fit for anyone aiming to succeed in Kaggle competitions, whether starting out or advancing further.

Data Wrangling with R guides you through mastering data preparation in the R programming language using tidyverse libraries. You will learn techniques to load, explore, transform, and visualize data effectively, gaining the skills needed for data modeling and insights extraction. What this Book will help me do Understand how to use R and tidyverse libraries to handle data wrangling tasks. Learn methods to work with diverse data types like numbers, strings, and dates. Gain proficiency in building visual representations of data using ggplot2. Build and validate your first predictive model for useful insights. Create an interactive web application with Shiny in R. Author(s) Gustavo Santos is an experienced data scientist specializing in R programming and data visualization. With a background in statistics and several years of professional experience in industry and academia, Gustavo excels at translating complex data analytics concepts into practical skills. His approach to teaching is hands-on and example-driven, aiming to empower readers to excel in real-world applications. Who is it for? If you are a data scientist, data analyst, or even a beginner programmer who wants to enhance their data manipulation and visualization skills, this book is perfect for you. Familiarity with R or a general understanding of programming concepts is suggested but not mandatory. It caters to professionals looking to refine their data wrangling workflow and to students aspiring to break into data-centered fields. By the end, you'll be ready to apply data wrangling and visualization tools in your projects.

Optimize the performance of your systems with practical experiments used by engineers in the world’s most competitive industries. In Experimentation for Engineers: From A/B testing to Bayesian optimization you will learn how to: Design, run, and analyze an A/B test Break the "feedback loops" caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization Clearly define business metrics used for decision-making Identify and avoid the common pitfalls of experimentation Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox of techniques for evaluating new features and fine-tuning parameters. You’ll start with a deep dive into methods like A/B testing, and then graduate to advanced techniques used to measure performance in industries such as finance and social media. Learn how to evaluate the changes you make to your system and ensure that your testing doesn’t undermine revenue or other business metrics. By the time you’re done, you’ll be able to seamlessly deploy experiments in production while avoiding common pitfalls. About the Technology Does my software really work? Did my changes make things better or worse? Should I trade features for performance? Experimentation is the only way to answer questions like these. This unique book reveals sophisticated experimentation practices developed and proven in the world’s most competitive industries that will help you enhance machine learning systems, software applications, and quantitative trading solutions. About the Book Experimentation for Engineers: From A/B testing to Bayesian optimization delivers a toolbox of processes for optimizing software systems. You’ll start by learning the limits of A/B testing, and then graduate to advanced experimentation strategies that take advantage of machine learning and probabilistic methods. The skills you’ll master in this practical guide will help you minimize the costs of experimentation and quickly reveal which approaches and features deliver the best business results. What's Inside Design, run, and analyze an A/B test Break the “feedback loops” caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization About the Reader For ML and software engineers looking to extract the most value from their systems. Examples in Python and NumPy. About the Author David Sweet has worked as a quantitative trader at GETCO and a machine learning engineer at Instagram. He teaches in the AI and Data Science master's programs at Yeshiva University. Quotes Putting an ‘improved’ version of a system into production can be really risky. This book focuses you on what is important! - Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland A must-have for anyone setting up experiments, from A/B tests to contextual bandits and Bayesian optimization. - Maxim Volgin, KLM Shows a non-mathematical programmer exactly what they need to write powerful mathematically-based testing algorithms. - Patrick Goetz, The University of Texas at Austin Gives you the tools you need to get the most out of your experiments. - Marc-Anthony Taylor, Raiffeisen Bank International

Use this book to study for the PL-300 Microsoft Power BI Data Analyst exam. The book follows the “Skills Measured” outline provided by Microsoft to help focus your study. Each topic area from the outline corresponds to an area covered by the exam, and the book helps you build a good base of knowledge in each area. Each topic is presented with a blend of practical explanations, theory, and best practices. Power BI is more than just the Power BI Desktop or the Power BI Service. It is two distinct applications and an online service that, together, enable business users to gather, shape, and analyze data to generate and present insights. This book clearly delineates the purpose of each component and explains the key concepts necessary to use each component effectively. Each chapter provides best practices and tips to help an inexperienced Power BI practitioner develop good habits that will support larger or more complex analyses. Manybusiness analysts come to Power BI with a wealth of experience in Excel and particularly with pivot tables. Some of this experience translates readily into Power BI concepts. This book leverages that overlap in skill sets to help seasoned Excel users overcome the initial learning curve in Power BI, but no prior knowledge of any kind is assumed, terminology is defined in non-technical language, and key concepts are explained using analogies and ideas from experiences common to any reader. After reading this book, you will have the background and capability to learn the skills and concepts necessary both to pass the PL-300 exam and become a confident Power BI practitioner. What You Will Learn Create user-friendly, responsive reports with drill-throughs, bookmarks, and tool tips Construct a star schema with relationships, ensuring that your analysis will be both accurate and responsive Publish reports and datasets to the Power BI Service, enabling the report (and the dataset) to be viewed and used by your colleagues Extract data from a variety of sources, enabling you to leverage the data that your organization has collected and stored in a variety of sources Schedule data refreshes for published datasets so your reports and dashboards stay up to date Develop dashboards with visuals from different reports and streaming content Who This Book Is For Power BI users who are planning to take the PL-300 exam, Power BI users who want help studying the topic areas listed in Microsoft’s outline for the PL-300 exam, and those who are not planning to take the exam but want to close any knowledge gaps they might have

In API Analytics for Product Managers, you will learn how to approach APIs as products to drive revenue and business growth. The book provides actionable insights on researching, strategizing, marketing, and evaluating the performance of APIs in SaaS contexts. What this Book will help me do Learn to develop long-term strategies for managing APIs as a product. Master the concepts of the API lifecycle and API maturity for better management. Understand and apply key metrics to measure activation, retention, and engagement of APIs. Design support models for APIs that ensure scalability and efficiency. Gain techniques for deriving actionable business insights from metrics analysis. Author(s) Deepa Goyal is an experienced product manager who specializes in API lifecycle management and analytics strategies. With years of industry experience, she has developed deep expertise in scaling and optimizing APIs to deliver business value. Her practical and results-oriented writing style makes complex topics accessible for professionals looking to enhance their API strategies. Who is it for? Ideal for product managers, engineers, and executives in SaaS companies looking to maximize the potential of APIs. This book is especially suited for individuals with foundational knowledge of APIs aiming to refine their analytical and strategic skills. Readers will gain actionable insights to track API performance effectively and implement metrics-driven decisions. It's a must-read for those focused on leveraging APIs for business growth.

"Democratizing Application Development with Betty Blocks" is a hands-on guide for learning the Betty Blocks no-code platform to develop impactful, dynamic business applications. This book introduces both basic and advanced concepts, empowering readers to create valuable IT solutions, from prototypes to complete applications. What this Book will help me do Understand the capabilities and low-code functionalities of Betty Blocks through engaging examples. Learn to create business applications using data models, workflows, and dynamic web components. Master rapid application development techniques to build prototypes and applications quickly. Discover how to use Betty Blocks' drag-and-drop interfaces for effective front-end design. Gain insight into collaborating as a citizen developer to deliver functional custom applications. Author(s) Reinier van Altena is an experienced professional in no-code application development, specializing in empowering users of varying technical skills to create business solutions. With practical insights derived from extensive use of platforms like Betty Blocks, Reinier shares approachable and actionable advice. His expertise bridges the gap between technology and innovation. Who is it for? This book is tailored for individuals interested in building business applications without prior coding knowledge. Ideal readers include citizen developers, business professionals, and anyone seeking to fulfill specific organizational IT needs through creativity and innovation. The book emphasizes learning fundamentals and advanced application-building strategies in an accessible manner.

With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using machine learning. Most of the exercises use R and Python, but rather than focus on coding algorithms, the book employs interactive interfaces to these tools to perform the analysis. Using the CRISP-DM data mining standard, the early chapters cover conducting the preparatory steps in data mining: translating business information needs into framed analytical questions and data preparation. The Jamovi and the JASP interfaces are used with R and the Orange3 data mining interface with Python. Where appropriate, Voyant and other open-source programs are used for text analytics. The techniques covered in this book range from basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analytics. Includes companion files with case study files, solution spreadsheets, data sets and charts, etc. from the book. Features: Covers basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analytics Uses R, Python, Jamovi and JASP interfaces, and the Orange3 data mining interface Includes companion files with the case study files from the book, solution spreadsheets, data sets, etc.

ADVANCES IN BUSINESS STATISTICS, METHODS AND DATA COLLECTION Advances in Business Statistics, Methods and Data Collection delivers insights into the latest state of play in producing establishment statistics, obtained from businesses, farms and institutions. Presenting materials and reflecting discussions from the 6 th International Conference on Establishment Statistics (ICES-VI), this edited volume provides a broad overview of methodology underlying current establishment statistics from every aspect of the production life cycle while spotlighting innovative and impactful advancements in the development, conduct, and evaluation of modern establishment statistics programs. Highlights include: Practical discussions on agile, timely, and accurate measurement of rapidly evolving economic phenomena such as globalization, new computer technologies, and the informal sector. Comprehensive explorations of administrative and new data sources and technologies, covering big (organic) data sources and methods for data integration, linking, machine learning and visualization. Detailed compilations of statistical programs’ responses to wide-ranging data collection and production challenges, among others caused by the Covid-19 pandemic. In-depth examinations of business survey questionnaire design, computerization, pretesting methods, experimentation, and paradata. Methodical presentations of conventional and emerging procedures in survey statistics techniques for establishment statistics, encompassing probability sampling designs and sample coordination, non-probability sampling, missing data treatments, small area estimation and Bayesian methods. Providing a broad overview of most up-to-date science, this book challenges the status quo and prepares researchers for current and future challenges in establishment statistics and methods. Perfect for survey researchers, government statisticians, National Bank employees, economists, and undergraduate and graduate students in survey research and economics, Advances in Business Statistics, Methods and Data Collection will also earn a place in the toolkit of researchers working –with data– in industries across a variety of fields.

Build a successful career in business analysis When it comes to doing good business, change is a very good thing. And effective business analysts are at the heart of identifying opportunities for growth and implementing the solutions that can transform an organization’s foundation—and ultimately increase its profitability. Whether you’re an aspiring business analysis professional or a seasoned analyst looking for the latest techniques and approaches, Business Analysis For Dummies helps you discover the newest tips and tricks for turning knowledge into the changes that have a real and meaningful impact on business and drive your organization towards value delivery. Identify areas for growth and create solutions Learn how to bring people together to collaborate effectively Discover ways to better understand and serve your customers See how business analysis works in the real world Learn the technology to make the job easier Find business solutions to improve your organization’s performance Understand how to dig deeply into your organization’s data, processes, and business rules Dummies makes the path to business success clear. Start here to turn your love of business analysis into the catalyst that makes a difference.

A deep dive into the programming language of choice for statistics and data With R All-in-One For Dummies, you get five mini-books in one, offering a complete and thorough resource on the R programming language and a road map for making sense of the sea of data we're all swimming in. Maybe you're pursuing a career in data science, maybe you're looking to infuse a little statistics know-how into your existing career, or maybe you're just R-curious. This book has your back. Along with providing an overview of coding in R and how to work with the language, this book delves into the types of projects and applications R programmers tend to tackle the most. You'll find coverage of statistical analysis, machine learning, and data management with R. Grasp the basics of the R programming language and write your first lines of code Understand how R programmers use code to analyze data and perform statistical analysis Use R to create data visualizations and machine learning programs Work through sample projects to hone your R coding skill This is an excellent all-in-one resource for beginning coders who'd like to move into the data space by knowing more about R.

Microsoft Power Platform Enterprise Architecture is your essential guide to designing powerful, enterprise-grade solutions using Microsoft technology. This thoroughly structured book equips you with architectural insights, methodologies, and best practices necessary to optimize solutions using the Microsoft Power Platform and integrate them seamlessly with M365 and Azure. What this Book will help me do Design robust enterprise solutions leveraging Microsoft Power Platform and Dynamics 365. Integrate Power Platform tools with Microsoft 365 and Azure effectively for comprehensive solutions. Implement advanced security, extensibility, and lifecycle management methodologies. Migrate and manage data efficiently within the Power Platform ecosystem. Overcome architectural challenges in multi-system integration using proven techniques. Author(s) Robert Rybaric, the author of this book, is an experienced enterprise architect specializing in Microsoft technologies. With years of expertise in designing enterprise systems, Robert brings practical insights into crafting effective solutions using Microsoft Power Platform. His approach emphasizes clarity and practicality, ensuring concepts are both illustrative and applicable for readers. Who is it for? This book is ideal for enterprise architects and technical decision-makers aiming to design and deploy complex solutions using the Microsoft Power Platform. It is tailored for professionals with familiarity with Microsoft Power Platform and Azure services who wish to refine their skills in enterprise architecture to meet growing business demands efficiently.

This book is your comprehensive guide to achieving the Tableau Desktop Specialist certification. By working through its structured content, you'll gain the skills necessary to understand and utilize Tableau for data analysis and visualization, and you'll be confidently prepared to pass the certification exam. What this Book will help me do Master how to load and prepare data in Tableau efficiently for analysis. Design and create visually impactful charts and dashboards tailored to your audience. Learn to utilize calculations, parameters, and advanced functions in Tableau. Develop an understanding of managing dimensions, measures, and their application. Gain confidence in sharing and presenting insights effectively with Tableau. Author(s) Adam Mico, a renowned Tableau ambassador and visionary, has extensive experience in the data visualization field. Having successfully obtained the Tableau Desktop Specialist certification himself, Adam combines practical expertise with an intuitive teaching approach, ensuring readers gain both the knowledge and confidence required to excel. Who is it for? If you're starting your journey in data visualization or looking to formalize your Tableau skills, this book is intended for you. Beginners without prior Tableau experience will find structured guidance, while those with some knowledge will appreciate the detailed preparation tips. Ideal for professionals aiming to pass the Tableau Desktop Specialist certification or improve their data analysis capabilities.