Corso di Laurea Magistrale in
Data Science, Scientific Computing & Artificial Intelligence
Organization of teaching activities
The description of the teaching activities scheduled for the 2023/24 academic year is given below. Details on individual courses are available on the page Teachings-Syllabus; other information and educational materials for students can be found on University e-learning platform.
The first two following tables contain the characterizing courses of the I and II year. Tables A, B, C, D, E contain activities of typology affine.
Some characterizing courses for computer science, mathematics and statistics training, scheduled for the I year, must be chosen by the student on the basis of their knowledge and startig skills. The theaching chosen by student of the I and II year, which must be complementary to the knowledge already acquired, will be distributed as follows:
- 18 CFU must be chosen from those in lists A, B, C, D, E of activities of typology affine;
- 18 CFU can be chosen freely from characterizing and similar courses not previously chosen or from courses offered by the University of Florence, provided they are consistent with the study plan.
A total of 24 CFU are attributed to the activities relating to the final exam, of which 18 for experimental, research and review work and 3 CFU for the drafting and discussion of the thesis. Another 3 CFU are reserved for further training activities.
Students will be assisted in their choice of study plan by the Study Program delegates. See also the following general guidelines (in italian).
ACTIVITIES OF TYPOLOGY caratterizzante
| I YEAR | ||
|
TEACHING |
CFU |
SSD |
|
One course chosen from:
|
9 |
MAT/06 MAT/08
|
|
Fundamentals of statistics for data science |
6 |
SECS-S/01 |
|
One course chosen from:
|
9 |
INF/01 |
|
One course chosen from:
|
6
|
MAT/08 |
|
Statistical learning per data science |
6 |
SECS-S/01 |
|
Data organization and data mining |
12 |
INF/01 |
|
Teachings chosen by student |
12 |
| II YEAR | ||
|
TEACHING |
CFU |
SSD |
|
Computational learning |
6 |
INF/01 |
|
One course chosen from:
|
6 |
IUS/20 L-LIN/01 |
|
High level training |
3 |
|
|
Final work: development of thesis |
18 |
|
|
Final examination |
3 |
|
|
Teachings chosen by student |
24 |
|
ACTIVITIES OF TYPOLOGY affine
|
TABLE A: BIOLOGY |
|||
|
TEACHING |
CFU |
SSD |
YEAR |
|
Language, cognition and computation |
6 |
BIO/19 |
I |
|
Big data in biology |
6 |
BIO/19 |
II |
|
Models in computational biology |
6 |
BIO/18 |
II |
|
TABLE B: CHEMISTRY |
|||
|
TEACHING |
CFU |
SSD |
YEAR |
|
High performance computing applied to chemistry |
6 |
CHIM/02-03 |
I |
|
Data science for biochemical sciences |
6 |
CHIM/03 |
I |
|
Fundamentals of chemistry for the data/computational scientist |
6 |
CHIM/03 |
I |
|
Chemical-physical modelling |
6 |
CHIM/02 |
II |
|
TABLE C: PHYSICS |
|||
|
TEACHING |
CFU |
SSD |
YEAR |
|
Data science for neuroscience |
6 |
FIS/03 |
I |
|
Data science for image analysis in astronomy and medical physics |
6 |
FIS/05 |
II |
|
Data science in particle physics |
6 |
FIS/01 |
II |
|
TABLE D: GEOLOGY |
|||
|
TEACHING |
CFU |
SSD |
YEAR |
|
Computational geochemistry and geostatistics |
6 |
GEO/08 |
I |
|
Analysis of numerical series for geophysics |
6 |
GEO/10 |
II |
|
Numerical paleobiology |
6 |
GEO/01 |
II |
|
TABLE E: COMPUTER SCIENCE, MATHEMATICS, STATISTICS |
|||
|
TEACHING |
CFU |
SSD |
YEAR |
|
Logic for artificial intelligence |
6 |
MAT/01 |
I |
|
Geometric deep learning |
6 |
MAT/03 |
II |
|
Mathematical models for distributed ledgers: theory and use cases |
6 |
MAT/05 |
II |
|
Information retrieval e tecnologie per il semantic web |
6 |
INF/01 |
II |
|
Statistical analysis of network data |
6 |
SECS-S/01 |
II |
Last
update
18.12.2023