- 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 prediction.
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: firstname.lastname@example.org
See Department webpage.
More information will be available on the course webpage at formazioneonline.unisalento.it
Introduction to Data Science and Machine Learning.
Linear Model. Non-linear Regression.
Shrinkage methods. Lasso.
K-Nearest neighbour algorithms.
Classification. Logistic regression.
Unsupervised learning. K-means algorithms.
Lectures notes will be provided. The teaching material will be made available through the Lecture webpage at formazioneonline.unisalento.it.
- Boehmke, B. and Greenwell, B.: Hands-on Machine Learning with R. Free available at https://bradleyboehmke.github.io/HOML/
- Hull, J.C.: Machine Learning in Business – An introduction to the world of data science, 2019. Slides avalilable free online.
- James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R. Springer, 2013. Free available at https://www.statlearning.com/
First Semester (dal 15/09/2021 al 31/12/2021)
Type of assessment
Joint Written and Oral - Final grade