Fundamentals of Data Science and Laboratory
(also Foundations of Data Science)
Winter semester 2021/2022
Welcome! This is the home page of the "Fundamentals of Data Science and Laboratory" course (also "Foundations of Data Science"), taking place in the winter semester of 2021/2022.
The course (Fundamentals of Data Science and Laboratory) is part of the Master's Degree in Data Science -- Sapienza University of Rome -- organized jointly by the departments of Computer Science (DI), Information and Automation Engineering (DIAG) and Information, Electronics and Telecommunication Engineering (DIET), and Statistics (DSS).
The course (Foundations of Data Science) is also part of the Master's Degree in Computer Science, organized by the Computer Science (DI) Department.
You like the subject? why not consider a Ph.D. ? See adverts on my web-page or simply ask us!
Subscribe
Important: subscribe to the course mailing list to receive slides, course material, assignments and news.
Click on this link.
Note: join the mailing list with your institutional email.
If you are a first-year master's student and did not receive your institutional address yet due to specific situations, please request access to the group but also send an email detailing i. the circumstance; ii. a proof of your acceptance and iii. a proof of your identity. Requests from non instutional addresses without those cannot be accepted.
Time and rooms
Classes start on Monday Sept 20th, 2021 .
The course takes place @:
The Lab (only for DS students) takes place @:
Lab 15 (via Tiburtina 205) on Fridays 10:00-13:00
Please also refer the Data Science website for the class schedule.
Classes will be in presence and from remote.
Remote connection credentials will be distributed via the mailing lists.
Some useful links for students attending in presence:
Lecturer
Prof. Fabio Galasso, web-page, email: galasso remove_this @di DOT uniroma1 DOT it
Programme
The course is an introduction to the basics of Data Science as well as the relating topics from Data Mining, Machine Learning and Image Analysis, using the Python programming language.
I will cover the fundamental models, algorithms and approaches to deal with data from a Data Science and Machine Learning perspective, including the data preparation, the feature engineering, the model design and its optimization and evaluation. Topics from the course will include: basics of digital image processing, regression, classification with discriminative and generative models, optimization, bias/variance, regularization, clustering, dimensionality reduction, and a brief introduction to neural networks.
In the laboratory classes (for data science students only) I will introduce: the basics of Python, Numpy, data structures and Pandas, plotting, Scikit-learn, and sample programming of machine learning models from the course.