The rapid advancement of digital technologies has transformed the global business environment, making data one of the most valuable strategic assets for organizations. Every business function-from marketing and finance to human resources and supply chain management-now relies on data-driven insights to make informed decisions, optimize operations, and create sustainable competitive advantages. In this era of digital transformation, Business Analytics has emerged as a critical discipline that combines business knowledge, statistical methods, and technological tools to convert raw data into meaningful information for strategic decision-making. Consequently, analytical thinking and data literacy have become essential competencies for management graduates seeking to excel in today’s dynamic and technology enabled business landscape.
Recognizing the growing significance of analytics in modern business practices, Dr. Manmohan Singh Bengaluru City University, under the State Education Policy (SEP) framework, has introduced Business Analytics as a core course for the Bachelor of Business Administration (BBA), Fifth Semester. The inclusion of this course reflects the University’s commitment to equipping students with future-ready skills that meet the evolving expectations of industry and align with the objectives of experiential, multidisciplinary, and outcome-based education envisioned under the State Education Policy.
This textbook has been carefully developed in strict accordance with the prescribed syllabus of the University. It seeks to provide students with a comprehensive understanding of both the theoretical foundations and practical applications of Business Analytics. The content has been presented in a structured, learner-friendly, and application-oriented manner, ensuring that students not only grasp analytical concepts but also develop the ability to apply them in solving real-world business problems. The book aims to bridge the gap between academic learning and industry practice by integrating conceptual clarity with practical analytical tools and business-oriented examples.
The book is systematically organized into five comprehensive units, each designed to build analytical competence progressively.
Unit 1 – Introduction to Business Data Analytics
Unit 2 – Data Collection, Cleaning, and Visualization
Unit 3 – Quantitative Analysis Techniques
Unit 4 – Qualitative Analysis Techniques
Unit 5.– Business Applications, Ethics and Data Privacy
One of the distinguishing features of this textbook is its emphasis on experiential and practice-oriented learning. In line with the objectives of the State Education Policy, theoretical discussions are complemented by laboratory sessions, practical illustrations, business examples, and application-based learning activities that encourage critical thinking and analytical problemsolving. The content has been designed to support classroom teaching, self-learning, project work, internships, and professional skill development, making the book equally valuable for students and faculty members.
Contents –
UNIT – 1 INTRODUCTION TO BUSINESS DATA ANALYTICS
Definition and Scope of Business Data Analytics, Importance of Data Analytics in Business Decision-Making, Types of Business Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive, Quantitative vs. Qualitative Analysis: Concepts and Differences, Tools for Business Data Analytics (Introduction to Excel, Power BI, and Python). Lab Session: Basic operations in Excel and introduction to Power BI dashboards.
UNIT – 2 DATA COLLECTION, CLEANING, AND VISUALIZATION
Sources of Business Data (Primary and Secondary), Data Collection Methods (Surveys, Web Scraping, Transactional Data), Data Cleaning Techniques: Handling Missing Values, Outliers, and Duplicates, Data Visualization Techniques: Charts, Graphs, and Dashboards, Tools for Data Visualization: Excel, Power BI, and Tableau. Lab Session: Creating dashboards and charts using Excel and Power BI.
UNIT – 3 QUANTITATIVE ANALYSIS TECHNIQUES
Statistical Analysis: Measures of Central Tendency, Dispersion, and Correlation, Hypothesis Testing: t-tests, Chi-square tests, and ANOVA, Regression Analysis: Simple and Multiple Linear Regression, Time Series Analysis: Moving Averages, Trend Analysis, and Forecasting, Machine Learning Basics: Classification and Clustering Techniques. Lab Session: Hands-on exercises in Excel for executing Time series analysis and Regression.
UNIT – 4 QUALITATIVE ANALYSIS TECHNIQUES
Understanding Qualitative Data: Text, Images, and Interviews, Sentiment Analysis and Text Mining, Thematic Analysis for Business Insights, Tools for Qualitative Analysis: NVivo, Orange, and Python (NLTK Library).
UNIT – 5 BUSINESS APPLICATIONS, ETHICS AND DATA PRIVACY
Marketing Analytics: Customer Segmentation, Market Basket Analysis, Financial Analytics: Risk Analysis and Fraud Detection, HR Analytics: Employee Performance and Retention Analysis, Supply Chain Analytics: Demand Forecasting and Inventory Optimization. Ethical Issues in Business Data Analytics, Data Privacy Regulations (GDPR, HIPAA, Indian IT Act)
