Academic year 2020/2021 Syllabus of previous years
Official course title BUSINESS ANALYTICS
Course code ET7023 (AF:284192 AR:160980)
Modality On campus classes
ECTS credits 6
Degree level Bachelor's Degree Programme
Educational sector code SECS-P/08
Period 1st Term
Course year 3
Moodle Go to Moodle page
Contribution of the course to the overall degree programme goals
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.
Expected learning outcomes
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
Referral texts
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.
Assessment methods
For attending students, final evaluation will include a group project work (50%) with class presentation and a final written exam (50%).
Teaching methods
Lessons will include slides presentations and hands-on lab coding in R.
Teaching language
Type of exam
Definitive programme.
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