BUSINESS ANALYTICS

Using data about customers, markets and internal performance in business decision-making has dramatically enhanced the ability of organizations such as businesses, non-profits, and governments to gain insights and make better decisions
The module offers a broad overview on data analytics as a means to improve effective business intelligence and implement effective marketing strategies. The module provides students with both the key understanding of business problems and a hands-on toolkit based on RStudio to apply business analytics, in particular in the field of marketing and consumer behaviour.

Students will learn...
• how to combine firm theory and data analytics to take informed marketing and managerial decisions
• how to distinguish between causal and predictive problems and to use the relevant data analytics technique
• how to apply unsupervised and supervised algorithms to solve business problems
• how to evaluate and choose models

Basic statistical knowledge and coding skills are required.

Topics covered in the module will include:

- Data science for business
- Causality and prediction
- Classification models
- The random utility model and consumption theory: logit and multinomial logit
- Model evaluation for business intelligence
- Market segmentation and unsupervised modeling
- Attrition and consumers
- Intro to Network analysis

Provost, F., & Fawcett, T., 2013. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media
Marr, B., 2016. Key business analytics: the 60+ business analysis tools every manager needs to know. Pearson UK.

For attending students, final evaluation will include a group project work (50%) with class presentation and a final written exam (50%).

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Lessons will include slides presentations and hands-on lab coding in R.