ADVANCED PROGRAMMING WITH DATA

Academic year
2026/2027 Syllabus of previous years
Official course title
ADVANCED PROGRAMMING WITH DATA
Course code
CM0659 (AF:734602 AR:436690)
Teaching language
English
Modality
On campus classes
ECTS credits
6
Degree level
Master's Degree Programme (DM270)
Academic Discipline
IINF-05/A
Period
2nd Semester
Course year
1
Where
VENEZIA
The course is about designing robust data-analysis and machine-learning projects. The focus is on learning to reason about a data project as a whole — from acquiring and shaping messy real-world data, through validating and structuring an analysis so its results can be defended, to building and evaluating predictive models. Throughout, the material is grounded in real environmental data (air quality, hydrology, weather, monitoring). The course suits students of any background, whether or not they have previously studied machine learning.
Knowledge and understanding. Principles of reliable data-processing design; the Python data stack (NumPy, pandas); data validation and testing; machine-learning working mechanisms ( fitting, gradient descent, evaluation).

Applying knowledge and understanding. Ingesting, cleaning, and transforming real data; querying and data visualization; training and evaluating standard machine-learning models.

Making judgements. Evaluating model trustworthiness and accuracy; choosing appropriate mechanisms and metrics.
- A first programming course (in any language) and familiarity with basic programming logic.
- Basic statistics and linear algebra.
- Python is not assumed to be solid: a self-study refresher is provided as recommended preparatory material and briefly recapped at the start of the course.
- A prior machine-learning course is helpful but not required.
1. Introduction: tooling, and Python refresher
2. Programming foundations: functions, object literacy, NumPy and vectorization
3. Tabular data: tidy data, group-by, time series, file formats
4. Data acquisition and querying: files, web APIs, SQL with DuckDB
5. Code engineering: data validation, testing, packaging
6. Visualization: choosing and reading charts
7. Machine learning: core mechanisms, training and using models, evaluation.

- W. McKinney, *Python for Data Analysis*.
- J. VanderPlas, *Python Data Science Handbook* (available online).
- Official documentation for pandas, scikit-learn, DuckDB, pandera, and the visualization libraries used in the course.
- Group project: a data-to-model pipeline; assessed on correctness, validation, testing, and documentation.
- Individual oral exam: each member examined alone on the project and its concepts; confirms individual contribution.

- **Group project (50%)** — a data-to-model pipeline; assessed on correctness, validation, testing, and documentation.
- **Individual oral (50%)** — each member examined alone on the project and its concepts; confirms individual contribution.

oral

The instructor is responsible for ensuring the authenticity and originality of all examinations and coursework. In cases of suspected academic misconduct, an additional on-site assessment may be required during the exams, which may differ from the standard format.

- Group project (50%)
- Individual oral (50%)
Lectures introduce each topic and supervised laboratory sessions apply it. Lab work contributes to a term project developed incrementally across the course.

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development

Goal 3: good health and well-being
Goal 4: quality education
Definitive programme.
Last update of the programme: 02/07/2026