LAB OF INFORMATION SYSTEMS AND ANALYTICS

Academic year 2020/2021 Syllabus of previous years
Official course title LAB OF INFORMATION SYSTEMS AND ANALYTICS
Course code ET7008 (AF:304951 AR:170894)
Modality On campus classes
ECTS credits 6
Degree level Bachelor's Degree Programme
Educational sector code INF/01
Period 4th Term
Course year 2
Where RONCADE
Contribution of the course to the overall degree programme goals
The goal of this course is to teach students methods and technologies for effective data analysis.
Expected learning outcomes
The course discusses fundamental technique for predictive and descriptive analysis of data.

Students will achieve the following learning outcomes:

Knowledge and understanding: i) understanding principles of non-supervised learning; ii) understanding principles of supervised learning.

Applying knowledge and understanding: i) being able to apply supervised and unsupervised analysis techniques; ii) being able to use data analysis software tools (e.g., scikit-learn).

Communication: i) reporting comprehensive comparative analysis among different data analysis methods
Pre-requirements
Students should have achieved the learning outcomes of courses "Introduction to Coding and Data Management" and "Probability and Statistics".
Contents
1. Intro to Data Science
2. Similarity Search in Text
- Text representation; Tokenization, Stemming, Lemmatization; Vector space; Similarity measures;
3. Collaborative Filtering
- content-based, item-based collaborative filtering
4. Clustering:
- Centroid-based clustering; Hierarchical clustering; Agglomerative clustering; Density-based clustering; Quality evaluation;
5. Supervised Learning
- Model training, validation and tuning; Classification; Regression; Feature Engineering; Decision Trees;
6. Ensemble methods
- Bagging and Boosting; Bias vs. Variance trade-off; Over-fitting and Under-fitting; Random Forest
Referral texts
- Python Data Science Handbook. O’Reilly. 2016.
- Lecture notes. Selected readings provided during the course.
Assessment methods
Learning outcomes are verified by a set of exercises and a project.

The exercises require to apply data analysis methods to a given dataset of limited complexity.

The project requires to conduct a comparative analysis of different tools applied to a specific dataset or problem.
The student must chose and motivate the most appropriate solution and deliver a report discussing a comparative analysis of the chosen methods.
Teaching methods
Lectures and hands-on sessions. The following software tools will be used during the course: Jupyter, scikit-learn.
Teaching language
English
Type of exam
oral
Definitive programme.
Last update of the programme
27/04/2020