Business Analytics With R And Python
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Business Analytics With R And Python
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Author : David L. Olson
language : en
Publisher: Springer Nature
Release Date : 2024-07-30
Business Analytics With R And Python written by David L. Olson and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-30 with Computers categories.
This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence.
Practical Business Analytics Using R And Python
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Author : Umesh R. Hodeghatta
language : en
Publisher: Apress
Release Date : 2023-01-27
Practical Business Analytics Using R And Python written by Umesh R. Hodeghatta and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-27 with Computers categories.
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.
Data Mining For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2017-09-12
Data Mining For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-12 with Mathematics categories.
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions www.dataminingbook.com Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
Practical Business Analytics Using Python And R
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Author : Kumar P
language : en
Publisher: Independently Published
Release Date : 2024-11-22
Practical Business Analytics Using Python And R written by Kumar P and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-22 with Computers categories.
In today's fast-paced and data-driven world, businesses are increasingly relying on data to guide their strategies, optimize operations, and stay ahead of the competition. From customer behavior to market trends, every facet of a business generates vast amounts of data-data that holds the key to better decision-making, enhanced performance, and greater profitability. However, unlocking the potential of this data requires more than just access to it; it requires the tools, techniques, and frameworks that allow us to analyze, interpret, and act on the insights it provides. This book, Practical Business Analytics using Python and R: A Hands-On Approach to Business Intelligence, is designed to provide you with the knowledge and practical skills necessary to leverage the power of data in business contexts. Whether you are a business analyst, data scientist, or business manager, this book aims to equip you with a solid foundation in business analytics using Python and R, one of the most powerful and widely used open-source programming languages for statistical analysis and data science. Python and R have become an indispensable tool in the world of analytics. Its flexibility, vast library of packages, and user-friendly environment make it ideal for analyzing and visualizing data, building predictive models, and uncovering trends that drive business success. In this book, we will guide you through the fundamentals of Python and R, as well as advanced techniques in business analytics, with a focus on solving real-world business problems. Who Should Read This Book? This book is aimed at anyone interested in applying business analytics to solve real-world problems using Python and R. It is ideal for: Business Analysts: Who want to enhance their analytical skills and learn how to use Python and R for solving business problems. Data Scientists: Who are looking to expand their knowledge in business contexts and understand how to apply advanced analytics in a business environment. Managers and Decision Makers: Who want to understand how data-driven insights can inform strategic business decisions. Students and Beginners: Who are learning business analytics, data science, or related fields and want a practical guide to applying analytics using Python and R. This book is structured to cater to both beginners and more advanced users. Each chapter begins with a conceptual introduction to the topic, followed by practical, hands-on examples using Python and R. You will find step-by-step instructions, along with clear explanations of the code and the business implications of the results. Whether you are working through the chapters in order or focusing on specific topics, you will gain the skills necessary to apply business analytics in your organization. So, let's dive in and begin unlocking the true potential of your business data with Python and R!
Mastering Business Analytics With Python R
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Author : Manas Pandey
language : en
Publisher: Independently Published
Release Date : 2024-02-05
Mastering Business Analytics With Python R written by Manas Pandey and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-05 with Business & Economics categories.
"Mastering Business Analytics with Python & R: Theory and Practice" is a comprehensive guide unlocking the principles and applications of Business Analytics (BA). 5 Key Learning Outcomes or Takeaways from "Mastering Business Analytics with Python & R: Theory and Practice." 1. Demystifying Business Analytics (BA) for Informed Decision-Making: Takeaway: Gain a deep understanding of how BA empowers data-driven decisions, replacing intuition with concrete evidence. 2. Practical Applications Across Marketing, Finance, Operations, and HR: Takeaway: Discover real-world use cases of BA in diverse business functions, solving problems and optimizing performance in marketing, finance, operations, and HR. 3. Mastering Data Management and Wrangling: Takeaway: Develop essential data handling skills, including cleaning, transforming, imputing and transforming messy data into a usable format for analysis. 4. Drawing Key Insights and Recommendations: Takeaway: Learn powerful techniques for analyzing data with descriptive and inferential statistics, uncovering trends, patterns and translating them into actionable business recommendations. 5. Mastering Visualization and Predictive Modeling: Takeaway: Create impactful data visualizations using Python and R libraries, and leverage predictive modeling techniques to forecast future trends and outcomes. What you'll discover: Data Mastery: Conquer data wrangling with powerful Python libraries like pandas, NumPy and R packages like dplyr and tidyr, transforming messy data into a clean, usable format for analysis. Visual Insights: Craft compelling data visualizations using Python libraries like Matplotlib and Seaborn and R packages like ggplot2(grammar of graphics), effectively communicating insights to stakeholders. Statistical Prowess: Employ powerful statistical models in Python libraries like Scikit-learn and Statsmodels and R packages like moments, uncovering patterns, trends, and relationships within your data. Machine Learning(ML) Expertise: Understand in simple terms the mathematical aspect of ML algorithms (Decision tree, Neural Net, SVM, Clustering, etc). Build and deploy machine learning models in Python libraries like tensorflow, sklearn, Pytorch and R packages like caret, rpart, e1071, xgboost, etc. anticipating future trends and predicting business outcomes. Beyond the Basics: Application of BA in Marketing, Operations, Finance, and HR with practical examples and hypothetical datasets. Predict customer behavior, optimize operations, predict customer credit worthiness, forecast trends, predict employee churn and more!" "Mastering Business Analytics with Python & R: Theory and Practice" revolutionizes data-driven decision-making by seamlessly blending theoretical concepts with hands-on applications, empowering readers to unlock the full potential of Business Analytics across diverse business functions.
Modern Business Analytics
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Author : Deanne Larson
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-12-17
Modern Business Analytics written by Deanne Larson and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-17 with Business & Economics categories.
Deriving business value from analytics is a challenging process. Turning data into information requires a business analyst who is adept at multiple technologies including databases, programming tools, and commercial analytics tools. This practical guide shows programmers who understand analysis concepts how to build the skills necessary to achieve business value. Author Deanne Larson, data science practitioner and academic, helps you bridge the technical and business worlds to meet these requirements. You'll focus on developing these skills with R and Python using real-world examples. You'll also learn how to leverage methodologies for successful delivery. Learning methodology combined with open source tools is key to delivering successful business analytics and value. This book shows you how to: Apply business analytics methodologies to achieve successful results Cleanse and transform data using R and Python Use R and Python to complete exploratory data analysis Create predictive models to solve business problems in R and Python Use Python, R, and business analytics tools to handle large volumes of data Commit code to GitHub to collaborate with data engineers and data scientists Measure success in business analytics
Advanced Analytics In Power Bi With R And Python
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Author : Ryan Wade
language : en
Publisher: Apress
Release Date : 2020-09-05
Advanced Analytics In Power Bi With R And Python written by Ryan Wade and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-05 with Computers categories.
This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard to do, if not impossible, using native Power BI tools without Power BI Premium capacity. For example, you will learn to score Power BI data using custom data science models, including powerful models from Microsoft Cognitive Services. The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration, but become easier through the application of data wrangling features that languages such as R and Python support. If you are a BI developer, business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you to do that. What You Will Learn Create advanced data visualizations through R using the ggplot2 package Ingest data using R and Python to overcome the limitations of Power Query Apply machine learning models to your data using R and Python Incorporate advanced AI in Power BI via Microsoft Cognitive Services, IBM Watson, and pre-trained models in SQL Server Machine Learning Services Perform string manipulations not otherwise possible in Power BI using R and Python Who This Book Is For Power users, data analysts, and data scientists who want to go beyond Power BI’s built-in functionality to create advanced visualizations, transform data in ways not otherwise supported, and automate data ingestion from sources such as SQL Server and Excel in a more succinct way
Behavioral Data Analysis With R And Python
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Author : Florent Buisson
language : en
Publisher: O'Reilly Media
Release Date : 2021-08-17
Behavioral Data Analysis With R And Python written by Florent Buisson and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-17 with Business & Economics categories.
Most of the data that companies collect is related to customer behaviors, such as clicks on a website or purchases in a supermarket. But data science algorithms and predictive analytics tools aren't that specific, so customer data is treated the same way as, for example, astronomical or genomic data. This practical guide introduces powerful methods for behavioral data analysis that you're probably not aware of. Advanced experimental design will help you get the most out of your A/B tests, while causal diagrams will allow you to tease out causality from correlation even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, this practical book provides complete examples and exercises in R and Python to help you gain more insight from your 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
Business Analytics Using R A Practical Approach
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Author : Umesh R Hodeghatta
language : en
Publisher: Apress
Release Date : 2016-12-27
Business Analytics Using R A Practical Approach written by Umesh R Hodeghatta and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-27 with Computers categories.
Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictiveanalytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. What You Will Learn • Write R programs to handle data • Build analytical models and draw useful inferences from them • Discover the basic concepts of data mining and machine learning • Carry out predictive modeling • Define a business issue as an analytical problem Who This Book Is For Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals.
Machine Learning For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2023-03-22
Machine Learning For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-22 with Computers categories.
MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.