Business Analytics

A core component of the DSEB curriculum, this course bridges economic theory, data science techniques, and real-world business strategy. Students move through the full analytics lifecycle: from framing complex business challenges to delivering data-backed, actionable recommendations. The course opens with descriptive and diagnostic techniques in Excel to understand past performance, then transitions to Stata for building and interpreting predictive models to forecast future outcomes. The final module covers data storytelling using Advanced Excel and Tableau to communicate analytical findings to executive audiences. This is an applied course in solving business problems with data, not a theoretical one. Class sessions combine lectures with live tool demonstrations; assignments provide hands-on practice.

This course is organised into five integrated modules that take students through the complete business analytics lifecycle. Beginning with problem framing and survey design, students develop skills in descriptive and diagnostic analytics using Microsoft Excel, predictive modelling using Stata, and data visualisation using Tableau. The course culminates in a capstone project where students apply analytical, modelling, and communication skills to solve a real-world business problem and present actionable recommendations.

By the end of this course, students will be able to:

• Develop a structured approach to translating business problems into analytical frameworks.
• Master descriptive and diagnostic analysis using Microsoft Excel.
• Build and interpret predictive regression models for forecasting using Stata.
• Create interactive dashboards and communicate analytical insights using business intelligence platforms.
• Synthesise analytical, modelling, and communication skills to deliver comprehensive business recommendations.

Upon successful completion of this course, students will be able to:

• Explain the differences between descriptive, diagnostic, and predictive analytics and identify their appropriate business applications.
• Implement advanced Excel features, including Power Query and PivotTables, to clean, process, and summarise complex datasets.
• Apply statistical techniques to build predictive regression models in Stata.
• Construct interactive dashboards using Tableau following data visualisation best practices.
• Analyse business datasets to identify trends, patterns, and anomalies.
• Interpret regression model outputs to derive meaningful business insights.
• Evaluate the quality and potential biases of datasets and survey instruments.
• Design surveys to collect primary data for business decision-making.
• Formulate and communicate data-driven strategic recommendations to non-technical audiences.

Business Analytics: The Science of Data-driven Decision Making – U. Dinesh Kumar.

There is no single required textbook. Academic papers, reports, book chapters, and lecture slides are provided through the Learning Management System (LMS).