- Teaching in italian
- BUSINESS ANALYTICS
- BUSINESS ANALYTICS
- Subject area
- Reference degree course
- DIGITAL MANAGEMENT
- Course type
- Bachelor's Degree
- Teaching hours
- Frontal Hours: 36.0
- Academic year
- Year taught
- Course year
- Reference professor for teaching
- DURANTE FABRIZIO
Basic elements of calculus and statistics for data analysis
The course presents a vast set of machine learning tools for understanding and making prediction from the data. All the presented tools are illustrated in several real case studies with the software R.
Knowledge and understanding:
· Knowledge and understanding of machine learning models;
· Knowledge and understanding of quantitative tools for business, including segmentation and forecasting.
Applying knowledge and understanding:
· Ability to extract relevant information from big dataset for management and business innovation.
· Ability to identify the machine learning models that are suitable to analyse correctly a specific business problem.
· Ability to use a specific programming language to implement machine learning procedures.
Making judgements on pros and cons of different machine learning tools.
to present in a concise way the results of a quantitative analysis.
Ability to formalize in an algorithmic form a problem of interest in business.
Frontal lectures, exercises, computer labs.
The written exam consists of several exercises and one or more review questions. The project work consists of the preparation of a quantitative analysis related to the contents of the course with the help of the software R.
To pass the exam students must obtain a positive evaluation on both the written exam and the project. Both parts weigh 50% of the total points.
Sample of the written exam will be available at the course webpage.
There is no difference in the assessment procedures between attending and non-attending students.
University of Salento “promuove e garantisce l’inclusione e la partecipazione effettive degli studenti con disabilità” (art. 10 of the Statute). Students that have a disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) could contact the Disability and Accessibility Offices in Student Services: email@example.com
see the webpage economia.unisalento.it
Starting with January 2021, more information will be available on the course webpage.
Introduction to Machine Learning. Cross-Validation.
K-Nearest neighbour algorithms.
Linear Model. Regularization. Lasso.
Support Vector Machines.
Unsupervised learning. K-means algorithms. Clustering.
John C. Hull: Machine Learning in Business – An introduction to the world of data science, 2019.
James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R. Springer, 2013. Free available at http://www-bcf.usc.edu/~gareth/ISL/
Lectures notes will be provided.
Second Semester (dal 24/02/2021 al 31/05/2021)
Type of assessment
Oral - Final grade