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

Academic year 2016–2017

"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 homework is out, see the homework section below. For the deadline look at the instructions inside the homework.

Homework 2 is out. It is due on Sunday, December 11, at 23.59. See the homework section below.

Homework 1 is out. It is due on Sunday, November 6, at 23.59. See the homework section below.

You may download the presentation about the master program's study plan.

There will be no class on September 26.



Aris Anagnostopoulos, Sapienza University of Rome.

Ioannis Chatzigiannakis, Sapienza University of Rome.


When and where:

Monday 15.45–19.00, Via Ariosto 25, Room B2

Thursday 10.15–13.30, 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 3.5, which are slightly incompatible, but it is easy to translate programs from one version to the other. In the class we will use Python 3.5.

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.

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.



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

Date Topic Reading
September 29 Introduction to the Linux shell for data science Notes
October 3 Introduction to data science and data mining Introduction to data mining
October 6 Introduction to Python Introduction to Python
October 10 Strings, substring matching, distances,
text preprocessing,
the vector-space model
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, 6.2–6.3.1
October 13 Introduction to JSON, MongoDB, mLab Introduction to JSON, MongoDB, mLab
October 17 Search with inverted indices MRS Chapters 1.0–1.3, 4.0–4.2, 7.1.0
Book chapters on, binary search trees, hash tables, and quicksort from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
October 20 Introduction to Web Scraping Introduction to Web Scraping
October 24 Clustering Slides on clustering and on k-means
October 27 Introduction to Visualization, Clustering (K-Means) Introduction to Visualization, Clustering (K-Means)
November 3 Datasets, Visualization, Clustering Datasets, Visualization, Clustering
November 7 k-means (cont), graph, intro to social networks Notes on graphs, book chapters on minimum spanning trees, and on Dijksta's algorithm from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein, intro slides on social networks.
November 10 Introduction to Graphs in Python Introduction to Graphs in Python
November 14 Properties of social networks, guest talk on temporal networks Slides on temporal networks of guest talk by Aristides Gionis, book chapter on breadth-first search from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
November 17 FourSquare FourSquare
November 21 Link analysis and PageRank LRU Chapter 5.1, slides.
November 28 Personalized PageRank, high-level classification, mining frequent itemsets LRU Chapters 5.3, 5.4.4, 6.0–6.2, A Chapter 10.1
December 1 Twitter Twitter
December 5 Epidemics Slides on epidemics
December 12 Recommender systems Slides on recommender systems
December 19 MapReduce, Apache Giraph Chapter 2, tutorial on Apache Giraph




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.w