Practical Business Analytics Using Python And R
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Business Analytics Using R - A Practical Approach
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.
Practical Business Analytics Using Python and R
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!
Practical Business Analytics Using R and Python
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. You will: 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.