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 2023/2024.
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.
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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. the proof of your acceptance; and iii. the proof of your identity. Requests from non-institutional addresses without those cannot be accepted.
Classes starts on September 25th, 2023 (in agreement with the Data Science class schedule -- link).
The course takes place @:
Room 3 (via del Castro Laurenziano 7) on Mondays 16:00-19:00
Room 6 (via del Castro Laurenziano 7) on Fridays 11:00-13:00
The Lab (only for DS students) takes place @:
Lab 15 (via Tiburtina 205) on Thursdays 16:00-19:00
Please also refer to the Data Science first-semester lecture times for the class schedule.
Classes are in presence, however we make efforts to support remote participation (details are distributed via the mailing lists).
Please consider Sapienza's recommendations on the attendance of lectures.
Prof. Fabio Galasso, web-page, email: galasso remove_this @di DOT uniroma1 DOT it
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, including backpropagation and convolutional 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.