Course Code (Undergraduate): 100639
Course Code (Postgraduate): 2010552
48 Contact Hours
7.5 ECTS points Credit Points
By adequately capturing, analysing and interpreting data can have an extensive impact on the productivity of the business. Business analytics are now strategic necessities for business growths in this competing business environment. This course aims to explore advanced quantitative analytical techniques, tools and technologies commonly used when undertaking managerial planning and decision-making. It encompasses topics like analysis of social media data, automated machine learning, visual analytics, open source tools, agile methods, and ethical issues like algorithmic bias.
The course does not require students to be equipped with programming skills prior to this course. The analytic techniques and analytical skills developed from this course will prepare the students more competitive in the future business workplace.
The software tools mentioned during class include Microsoft Excel, R, SAS Visual Analytics, DataRobot, SPSS, KNIME and Tableau.
Schedule and Topics
2. Data Analytics for Business
3. Data Exploration in Business Analytics
4. Data Mining Methods in Business Analytics
5. Clustering and Segmentation
6. Classification and Regression Trees (CART)
7. Visualization and Communication
8. Automated Machine Learning
9. Technology Infrastructure for Business Analytics
10. Working with Unstructured Data
11. Business Analytics Methodology
12. Design and Agile Thinking
13. Ethical Aspects
Group project (40%);
The course is open for undergraduate/postgraduate business students.
Tanushri Banerjee & Arindam Banerjee (2019), Business Analytics, SAGE Publishing.
Academic journal articles and handouts on specific topics will be used to supplement the textbook and lecture material.
Course materials (including lecture notes, supplementary readings and solutions to assignment questions) are handed out during the class.