Taxmann's Business Analytics – Underscoring the pivotal role of data in the contemporary business landscape for data analysis and strategic implementation | MS Excel | Tableau | R [NEP]

Download or read book Taxmann's Business Analytics – Underscoring the pivotal role of data in the contemporary business landscape for data analysis and strategic implementation | MS Excel | Tableau | R [NEP] written by H.K. Dangi and published by Taxmann Publications Private Limited. This book was released on 2024-04-15 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book emphasises the critical role of data in today's evolving business landscape. It highlights the increasing complexity of the business environment and the growing demand for professionals adept at analysing data patterns and translating them into actionable strategies. This book is designed to progressively build the reader's knowledge in business analytics, from fundamental concepts to specialised techniques and ethical considerations, complete with practical applications and exercises for reinforcement. The Present Publication is the Latest Edition, focusing on the latest syllabus under UGCF 2022, aligning with the National Education Policy (NEP) adopted by the University of Delhi. This book is authored by Prof. H.K. Dangi and Gurveen Kaur, with the following noteworthy features: • [Balanced Approach Between Theory and Practice] The book maintains an equilibrium between theoretical knowledge and practical application. It lays a solid theoretical foundation in Business Analytics while also emphasising its practical aspects • [Real-World Application and Hands-On Learning] Incorporating real-life case studies, hands-on examples, and exercises, the book ensures that students can connect theoretical concepts with their implementation in the real world • [Educational Journey in Business Analytics] This book offers insights into data-driven decision-making and strategic thinking The structure of the book is as follows: • [Learning Outcomes] Every chapter begins with the list of learning outcomes which the readers will achieve after the completion of the chapter • [Headings/Sub-headings] Chapters are further divided into headings and sub-headings to increase the reader's comprehension • [Practice & Discussion Questions] Each chapter contains a series of practice/discussion questions to help the reader review the material • [Case Studies] are provided at the end of each chapter to help readers implement their learning into hypothetical real-life situations The content is methodically divided into eight chapters, covering a broad range of topics such as: • Introduction o Begins with a historical overview and the architectural framework of business analytics o Definitions, distinctions between analysis and analytics, and types (descriptive, predictive, prescriptive) are discussed o Applications across finance, marketing, human resources, and healthcare are explored alongside a case study and summary, followed by exercises and multiple-choice questions • Data Preparation o Focuses on the data preparation process, using MS-Excel for cleaning and validation, identifying outliers, and understanding covariance and correlation matrix o Practical application to business, summary, exercises, and multiple-choice questions are included • Data Summarisation and Visualisation o Covers types of data summarisation and visualisation, with an emphasis on using Tableau o The chapter concludes with exercises and multiple-choice questions • Getting Started with R o Introduces R and R Studio, highlighting the advantages of R, installation processes, data structures in R, and their application to business o Summarised with exercises and multiple-choice questions • Descriptive Statistics Using R o Measures of central tendency, dispersion, and relationship between variables are explored o Focuses on data visualisation using R through various plots and business applications, followed by a summary, exercises, and questions • Predictive Analytics o Discusses simple and multiple linear regression models, confidence and prediction intervals, regression analysis using R, and their applications in business o A summary, exercises, and multiple-choice questions are provided • Textual Analysis o Highlights the significance, applications, and challenges of textual data analysis o Introduces methods and techniques like word clouds, tree maps, and sentiment analysis using R, with a focus on business applications, summarised with exercises and questions • Ethics in Business Analytics o Addresses the meaning and importance of ethics in analytics, ethical issues, and considerations for ethical conduct o Concludes with practical applications to business, a summary, exercises, and multiple-choice questions