Data Mining

Academic year 2022–2023

"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


The course will develop algorithms and statistical 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 use Python for downloading data and implementing various algorithms using its rich libraries and frameworks such as Spark, Storm, Giraph, and TensorFlow for mining of large-scale data.



Students who wish to take this course should be familiar with Python programming.



Homework 4 is out. It is due on January 8.

Homework 3 is out. It is due on December 4.

The deadline for homework 3 is extended to November 20 30.

Homework 2 is out. It is due on November 13.

The deadline for homework 2 is extended to October 30.

Homework 1 is out. It is due on October 23.

There is no class on September 26. We start classes on September 29.

Make sure to register in the class mailing list. For details email Aris.




Aris Anagnostopoulos, Sapienza University of Rome.

Teaching Assistant (TA)

This email address is being protected from spambots. You need JavaScript enabled to view it. , Sapienza University of Rome.


When and where:

Monday 11.00–13.00, Room A5-A6

Thursday 15.00–19.00, Room A5-A6


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 Gianluca or, if needed, to Aris for arrangement.


Textbook and references

The main textbook is the "Mining of Massive Datasets," by J. Leskovec, A. Rajaraman, and J. D. Ullman. The printed version has been updated and you can download the latest version (currently 3) from the book's web site.

In addition, we will also use some chapters fro some other textbooks, all available online:

The following book is not obligatory for the class, but is a vary useful book for the topic of feature engineering

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


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:

We will use several libraries in the class. The Anaconda distribution has packaged all of them together and you can download it for free.

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


Examination format

The evaluation will consist of two parts:

  • 4 sets of homeworks
  • A final project. Details will be given during the course

Late policy: Every homework must be returned by the due date. Homeworks that are late will lose 10% of the grade if they are up to 1 day (24h) late, 20% if they are 2 days late, 30% if they are 3 days late, and they will receive no credit if they are late for more than 3 days. However, you have a bonus of 10 late days, which you can distribute as you wish among all the homeworks. The homeworks will be discussed and graded at the end, during the final exam.

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


Collaboration policy (read carefully!): You can discuss with other students of the course about the homeworks. 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.


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, ZM, ZAL, MRS.

Date Topic Reading
September 26 Introduction to data mining and data types LRU Chapters 1.1, 1.3
Introduction to data mining
September 29 Crash course in probability A crash course on discrete probability
Check the background probability chapters below
October 6 Crash course in probability (cont.), Similarity and distance measures Book chapter on quicksort from the book of Mitzenmacher and Upfal on Probability and Computing
Chapter 3.5
October 10 Similarity and distance measures (cont.)
October 13 Similarity and distance measures (cont.), preprocessing for text mining, Tf-Idf scoring MRS Chapters 2.0–2.2, 6.2
October 17 Inverted indexes MRS Chapters 1.0–1.4, 7.1.0
October 20 Brief recap of Hadoop, MapReduce, and Spark; construction of large indexes. LRU Chapters 3.4, 2.0–2.4, Quick introduction to MapReduce
October 24 Shingles, minwise hashing LRU Chapters 3.0–3.3
October 27 Lab on Apache SPARK
October 31 Locality-sensitive hashing LRU Chapter 3.4
November 3 Introduction to clustering, hierarchical clustering, k-means Chapters 7.0–7.1.2, 7.2, 7.3.0–7.3.2. Chapter on k-means of the book of Christopher M. Bishop
November 7 k-means++ Paper by D. Arthur and S. Vassilvitskii<
November 10 k-means++ (cont.), introductino to generative models. Notes
November 17 Soft clustering and expectation–maximization, projections in vector spaces. Notes
November 21 Feature engineering
November 24 Principal component analysis
November 28 Recommender systems, PageRank LRU Chapters 9, 5.1
December 1 PageRank (cont.), introduction to neural networks, graph neural networks
December 5 Lab on NNs and GNNS
December 15 Explainability
December 19 Lab on explainability and applications in bioinformatics




Check the "Examination format" section below for information about collaborating, being late, and so on.

Handing in: You must hand in the homeworks by the due date and time by an email to the TA that will contain as attachment (not links!) a .zip or .tar.gz file with all your answers and subject

[Data Mining class] Homework #

where # is the homework number. After you submit, you will receive an acknowledgement email that your homework has been received and at what date and time. If you have not received an acknowledgement email within 2 days after the deadline then contact Gianluca.

The solutions for the theoretical exercises must contain your answers either typed up or hand written clearly and scanned.

The solutions for the programming assignments must contain the source code, instructions to run it, and the output generated (to the screen or to files).

We will not post the solutions online, but we will present them in class.


Notes, slides, and other material

Book chapters and notes:

Background reading on combinatorics, basic probability, random variables, and basic probability distributions.