"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
• Find true love with data mining

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. We will base it on Amazon's AWS. After finishing the course, the students will have a large part of the knowledge required to pursue (independently) for an Amazon AWS Certification.

Announcements

Homework 2 is out. It is due on October 27.

Only for this week we will have class on Friday October 4th, at 2pm in Room 14 of the "Tumminelli" building (Building CU007)".

There will be no class on October 2. The class of October 2 will be moved either to October 3 or October 4. Details will follow.

Homework 1 is already out. It is due on October 13. For the part on AWS, you need to wait for the first lab, on October 1.

The first day of the ADM classes is on September 30, 2024. Instead, the week September, 23–27 you will have the Python pre-course.

You need to register to the class mailing list to be able to do homeworks, enter in groups, and so on. Email Aris to do it.

Instructors

Aris Anagnostopoulos, Sapienza University of Rome.

Federico Siciliano, Sapienza University of Rome.

Teaching Assistants (TA)

The best way to ask any questions is through slack. If you are registered in the course mailing list and have not received an invitation from Daniel, email Daniel.

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).

Daniel Jiménez (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).

Mehrdad Hassanzadeh (This email address is being protected from spambots. You need JavaScript enabled to view it. ), Data Science student, Sapienza University of Rome (LinkedIn).

Edo Fejzic (This email address is being protected from spambots. You need JavaScript enabled to view it. ), Data Science student, Sapienza University of Rome (LinkedIn).

When and where:

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

Thursday 12.00–14.00, Main campus, Physics building "Enrico Fermi" (Building CU033), Room 7.

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 created overhead for the in-class students, sorry. Make sure that you follow the reading material provided during the semester in this web page, and that you participate (even remotely) in the homework groups.

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. 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, 5th 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

The standard way to pass the course is to do a set of five homeworks (one individual, four in randomly created groups), which you do during the semester and a final oral exam. Organizational details will be given in class. You also have the option to do a written exam, which we do not recommend (we find that the topic of the class are hard to learn just by studying for an exam). In the previous years, almost everyone chose the first option.

Syllabus

Chapters for which no book is mentioned refer to the "Mining of Massive Datasets" (see below). For the other textbooks, we refer to with the author initials: A, ZAL, MRS.

Homeworks

• Homework 1 (due on 13/10, 23.59)
• Homework 2 (due on 27/10, 23.59)

Collaboration policy (read carefully!): You can discuss with other students of the course about the projects. However, you must understand well your solutions and the final writeup must be yours and written in isolation. In addition, even though you may discuss about how you could implement an algorithm, what type of libraries to use, and so on, the final code must be yours. You may also consult the internet for information, as long as it does not reveal the solution. If a question asks you to design and implement an algorithm for a problem, it's fine if you find information about how to resolve a problem with character encoding, for example, but it is not fine if you search for the code or the algorithm for the problem you are being asked. For the projects, you can talk with other students of the course about questions on the programming language, libraries, some API issue, and so on, but both the solutions and the programming must be yours. If we find out that you have violated the policy and you have copied in any way you will automatically fail. If you have any doubts about whether something is allowed or not, ask the instructor.

The same applies for generative AI tools, such as ChatGPT, Gemini, Bing, and so on. These can be useful tools in your work and there are some homework questions in which we ask you explicitly to use them. However, the use of such tools when it is not explicitly allowed will be treated as plagiarism and is strictly prohibited.