News | General Information | Syllabus | Class Schedules | Previous Years
- ANNOUNCEMENT: There will be no class on Wednesday, December 6, due to the IT Meeting event, which you are all welcome to join.
- Starting this year, the Big Data Computing course has been moved to the first semester and divided into two distinct modules, each one carrying 3 CFUs (credits).
Prof. Daniele De Sensi will lead the first module, while the second module (i.e., this module) will be under my instruction.
Importantly, these modules will not run concurrently; once the first module concludes, the second will begin. The estimated start date for this module is expected to be in the first week of November. We will provide more precise information as the conclusion of the first module approaches.
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 2023-2024 academic year.
- Tuesday from 2:00 p.m. to 4:00 p.m. in Aula Magna - "Building C" at Viale Regina Elena 295
- Wednesday from 10:00 a.m. to 1:00 p.m. in Aula Magna - "Building C" at Viale Regina Elena 295
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=17115
- Email: [email protected]
- Website: https://www.di.uniroma1.it/~tolomei
- Bacheca Sapienza: https://corsidilaurea.uniroma1.it/it/users/gabrieletolomeiuniroma1it
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 tools that are suitable for the large-scale analysis of data distributed across clusters of computers. More specifically, the course will give students the ability to proficiently develop big data/machine learning solutions on top of industry-standard frameworks, such as Hadoop and Spark, 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, and statistics. In addition, students must have non-trivial computer programming skills (preferably using Python programming language). Previous experience with Hadoop, Spark, or distributed computing is not required.
TBA
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]
| Lecture # | Date | Topic | Material |
|---|---|---|---|
| Lecture 1 | 11/21/2023 | Introduction to Big Data: Motivations and Challenges | [slides: PDF] |
| Lecture 2 | 11/22/2023 | The Curse of Dimensionality | [slides: PDF, notebook: ipynb] |
| Lecture 3 | 11/28/2023 | Clustering: A General Framework | [slides: PDF] |
| Lecture 4 | 11/29/2023 | Clustering: K-means | [slides: PDF] |
| Lecture 5 | 12/05/2023 | Dimensionality Reduction: Principal Component Analysis (Part I) | [slides: 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