News | General Information | Syllabus | Class Schedules | Previous Years
- NOTICE: The very first class, scheduled for Wednesday, 25th September 2025, will exceptionally take place in Room V "Guido Castelnuovo". For additional information, please take a look at the first-week class schedule available here.
- All the students that are willing to attend this class must subscribe ASAP to the Moodle web page of the course, as indicated below.
- Classes will start on Wednesday, September 25 2024. Students are kindly asked to refer to the class schedule at the following link.
Welcome to the Big Data Computing class!
This is a first-semester course of the MSc in Computer Science at the Sapienza University of Rome.
This repository contains class material along with any useful information for the 2024-2025 academic year.
The Big Data Computing course is divided into two distinct modules, each one carrying 3 CFUs (credits).
Prof. Daniele De Sensi will lead the first module, while the second module will be taught by Prof. Gabriele Tolomei.
Importantly, these modules will not run concurrently; once the first module concludes, the second will begin.
- Wednesday from 10:00 a.m. to 12:00 p.m. (Aula Magna - "Building C" - Viale Regina Elena 295 [map])
- Thursday from 1:00 p.m. to 4:00 p.m. (Room 1L - Via Del Castro Laurenziano, 7a [map])
Students must subscribe to the Moodle web page using the same credentials (username/password) to access the Wi-Fi network and Infostud services at the following link: https://elearning.uniroma1.it/course/view.php?id=18525 All the information will be provided through GitHub, whereas Moodle will be used as a repository for the course material.
Prof. Daniele De Sensi
- Email: [email protected]
- Website: https://danieledesensi.github.io
- Bacheca Sapienza: https://corsidilaurea.uniroma1.it/it/users/danieledesensiuniroma1it
Prof. Gabriele Tolomei
- Email: [email protected]
- Website: https://www.di.uniroma1.it/~tolomei
- Bacheca Sapienza: https://corsidilaurea.uniroma1.it/it/users/gabrieletolomeiuniroma1it
Prof. Daniele De Sensi
Please drop me a message at [email protected] in case you would like to schedule a meeting, either online (i.e., via Google Meet or Zoom) or in-person (i.e., in Room 306 located at the 3rd floor of Building E in Viale Regina Elena 295).
Prof. Gabriele Tolomei
Please drop me a message at [email protected] in case you would like to schedule a meeting, either online (i.e., via Google Meet or Zoom) or in-person (i.e., in Room 106 located at the 1st floor of Building E in Viale Regina Elena 295).
The amount, variety, and rate at which data is being generated nowadays, both by humans and machines, are unprecedented. This opens up a number of challenges on dealing with those data, as traditional computing paradigms are not conceived to operate at such a scale.
"Big Data" is the umbrella term that has rapidly become popular to describe methodologies and tools specifically designed for collecting, storing, and processing very large or complex data sets. In addition to addressing foundational computer science problems, such as searching and sorting, big data computing mainly focuses on extracting knowledge - thereby value - from large-scale data sets using advanced data analysis techniques, such as machine learning.
This course is intended to provide graduate-level students with a deep understanding of programming models and computer architectures that are suitable for the large-scale analysis of data. More specifically, the course will give students the ability to understand challenges and solutions in developing big data/machine learning workloads, and to tackle real-world problems faced by the so-called "Big Five" tech companies (i.e., Apple, Amazon, Google, Microsoft, and Facebook): text/graph analysis, classification/regression, and recommendation, just to name a few.
The course assumes that students are familiar with the basics of data analysis and machine learning, properly supported by a strong knowledge of foundational concepts of calculus, linear algebra, probability, statistics, and computer architectures.
The exam will consist in an oral exam.
No textbooks are mandatory to successfully follow this course. However, there is a huge set of references which may be worth mentioning, especially to those who wants to dig deeper into some specific topics. Among those, some readings I would like to suggest are as follows:
- Mining of Massive Datasets [Leskovec, Rajaraman, Ullman] available online.
- Big Data Analysis with Python [Marin, Shukla, VK]
- Large Scale Machine Learning with Python [Sjardin, Massaron, Boschetti]
- Spark: The Definitive Guide [Chambers, Zaharia]
- Learning Spark: Lightning-Fast Big Data Analysis [Karau, Konwinski, Wendell, Zaharia]
- Hadoop: The Definitive Guide [White]
- Python for Data Analysis [Mckinney]
Introduction
- The Big Data Phenomenon
- Motivation and Challenges
- Large-scale parallel systems for running big data workloads
| Lecture # | Date | Topic | Material |
|---|---|---|---|
| Lecture 1 | 25/09/2024 | Introduction to Big Data: Motivations and Challenges | [slides: PPT, PDF] |
In the following, you can quickly navigate through Big Data Computing class information and material from previous years.
NOTE: The folder containing the class material is unique, and it is subject to changes and/or updates; as such, there may be differences between the content displayed on this website and what has been shown in class in the past.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent