Algorithmic Methods of Data Mining (Sc.M. in Data Science)

Academic year 2015–2016

"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 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.



The third and final project is out. See the project (below) for the due date.

Project 2 is out. It is due on Monday, December 21, at 23.59. See the project section below.

Project 1 is out. It is due on Sunday, November 15, at 23.59. See the project section below.

Remember to insert your information in the link we said in class.



Aris Anagnostopoulos, Sapienza University of Rome.

Carlos Castillo (ChaTo), Sapienza University of Rome.

Ioannis Chatzigiannakis, Sapienza University of Rome.


When and where:

Monday 10.15–13.30, Via Ariosto 25, Room A3

Wednesday 12.00–15.30, Via Tiburtina 205, Room 16


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 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:

In addition, we will cover material from various other 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. There are currently two main versions of Python, version 2.7 and version 3.4, which are slightly incompatible, but it is easy to translate programs from one version to the other. Even though version 3.4 is newer and better designed, version 2.7 is still more widely used and most available libraries are written for it. Therefore, in the class we will use Python 2.7.

To learn the language you can find a lot of material online. You can start from Python's documentation site:

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

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 the instructor for more information.



Chapters for which no book is mentioned refer to the "Mining of Massive Datasets" (see below).

Date Topic Reading
September 28 Introduction to data science and data mining Introduction to data mining
September 30 Introduction to the Linux shell for data science Notes
October 5 Strings, substring matching, distances,
text preprocessing
LRU Chapters 3.5.5, 3.5.6,
Naïve algorithm for stream matching
Automata and string matching
Introduction to unicode
MRS Chapters 2.0–2.2
October 7 Processing Text Files with Python

Notes and Homework

October 12 Text similarity and clustering
October 14 Working with K-means clustering and data visualization Notes and Homework
October 19 Text summarization and indexing
October 28 Searching with inverted indices, sorting, external sorting MRS Chapters 1.2, 1.3, 4.1, 4.2
Chapter on quicksort from the CLRS book, "Introduction to Algorithms"
November 2 Basic graph concepts for data science Notes on graphs
Sections on BFS and the Dijkstra's algorithm from the CLRS book, "Introduction to Algorithms"
November 4 Python library networkx and graph visualization Notes available on dropbox
November 9 Graph mining methods
November 11 OpenAirlines Dataset and Graphs Notes available on dropbox
November 16 Introduction to social network, epidemics, and influence
November 18 Twitter API and Graph Visualization

Notes available on dropbox

November 23 Graph clustering
November 25 Foursquare API and Graph Visualization Notes available on dropbox
November 30 Mining of frequent itemsets LRU, Chapters 6.0–6.2, 6.4.1–6.4.2, 6.4.5
December 7 Recommender systems
December 14 Data Science for Social Good




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.