"The success of companies like Google, Facebook, Amazon, and Netflix, not to mention Wall Street firms and industries from manufacturing and retail to healthcare, is increasingly driven by better tools for extracting meaning from very large quantities of data. 'Data Scientist' is now the hottest job title in Silicon Valley." – Tim O'Reilly

• Data Scientist: The Sexiest Job of the 21st Century

The course will develop the basic algorithmic techniques for data analysis and mining, with emphasis on massive data sets such as large network data. It will cover the main theoretical and practical aspects behind data mining.
The goal of the course is twofold. First, it will present the main theory behind the analysis of data. Second, it will be hands-on and at the end students will become familiar with various state-of-the-art tools and techniques for analyzing data.

We will cover some very basic topics necessary for handling large data, such as hashing, sorting, graphs, data structures, and databases. We will then move to more advanced data mining topics: text mining, clustering, classification, mining of frequent itemsets, graph mining, visualization.

The theoretical part will be complemented by a laboratory where students will learn how to use tools for analyzing and mining large data.

Announcements

The first day of the ADM classes is on September 22, 2025.

You need to register to:

• The class mailing list.
• Google classroom.

Instructors

Aris Anagnostopoulos, Sapienza University of Rome.

Luca Becchetti, Sapienza University of Rome.

Teaching Assistants (TA)

The best way to ask any questions is through Google classroom.

Loris Cino (This email address is being protected from spambots. You need JavaScript enabled to view it. ), Ph.D. candidate in Data Science, Sapienza University of Rome (LinkedIn).

Josef Zerpa Ruiz(This email address is being protected from spambots. You need JavaScript enabled to view it. ), M.Sc. student in Computer Science, Sapienza University of Rome (LinkedIn).

When and where:

Monday 14.00–16.00, Via di Castro Laurenziano 7a (Building RM018), Room 2.

Thursday 14.00–16.00, Viale Regina Elena 295, Building D (RM112), Room 101.

Lab: Tuesday 15.00–19.00, Via Tiburtina 205, Room 17.

Online lectures and nonattending students

There will not be lectures online, only in class. Attending the classes and the lab is, of course, highly recommended. In addition, participation (through making and answering questions) can increase your final grade.

We know that there are students who cannot be in Rome (workers, visa problems, etc.). We mainly use the blackboard during class and online attendance or registration would create overhead for the in-class students, sorry. Make sure that you follow the reading material provided during the semester in this web page.

Office hours

You can use the office hours for any question regarding the class material, past or current homeworks, general questions on data mining, the meaning of life, pretty much anything. The best resource is Google classroom. If this is not enough, you can send an email to the TAs and, if needed, to the instructors for arrangement.

Textbook and references

We will use a variety of textbooks. Whenever we can, we will try to find books that are available online. As the course progresses, we will indicate what you should read. The main books that we will use are the:

• (A) C. Aggarwal, "Data Mining: The Textbook," Springer (must be downloaded from Sapienza)
• (ZAL) R. Zafarani, M. A. Abbasi, and H. Liu, "Social Media Mining: An Introduction," Cambridge University Press
• (LRU) J. Leskovec, A. Rajaraman, and J. Ullman, "Mining of Massive Datasets," Cambridge University Press
• (MRS) C. D. Manning, P. Raghavan and H. Schütze, "Introduction to Information Retrieval," Cambridge University Press
• (J) J. Janssens, "Data Science at the Command Line", O'Reilly

In addition, we will cover material from various other sources, which we will post online as the course proceeds.

If you are interested in the topic of algorithms, we recommend the following books:

• T. Cormen, C. Leiserson, R. Rivest, and S. Stein, "Introduction to Algorithms" (4th ed): This is a classic book, very detailed, sometimes too verbose
• S. Dasgupta, C. Papadimitriou, and U. Vazirani, "Algorithms": Very succinct but well written, probably the first book to check out. If you cannot follow it, try one of the other books first
• T. Roughgarden, "Algorithms Illuminated": Another introductory text. It is a more recent book. Tim writes very well.
• J. Kleinberg, and E. Tardos, "Algorithm Design": This is a more advanced book, probably not recommended if it is your first contact with algorithms, but will increase your knowledge a lot if you already know the basic concepts.

Python resources

The main programming language that we will use in the course is Python 3.

To learn the language you can find a lot of material online. You can start from Python's documentation site: https://www.python.org/doc/.

If you would like to buy some books, you can check the

• "Learning Python, 6th edition," by Mark Lutz. It is a bit verbose, but it presents well the features of the language.
• "Python Pocket Reference, 5th edition," by Mark Lutz. It is usefull as a quick reference if you know more or less the language and you are searching for some information.

We will use several libraries in the class. For Windows users the Anaconda distribution has packaged all of them together and you can download it for free. For MAC/Linux users, all packages can be installed using the pip3 tool.

We will also use Python (Jupyter) notebooks. You can find instructions for the installation at the Jupyter web site.

If you have problems with Python installation you can obtain an ubuntu virtual machine with Python preinstalled. Contact the instructor for more information.

Examination format

There are two ways to pass the class:

• Do a hackathon that we will set up at the end of the semester.
• Do a 1-hour written exam, where we will ask you basic concepts that we have covered during the semester.

• Do a 2-hour more extended written exam.

In addition, we will take into account participation during class.

Syllabus and lecture material

Make sure that you register to the class mailing list and to Google classroom to be able to get this information. See the Announcements above.