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

Academic year 2017–2018

"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 fourth homework is out; it is due December 22.

The third homework is out; it is due December 3.

The second homework is out; it is due November 12.

The first homework is out; it is due October 22.

We start classes on September 25.



Aris Anagnostopoulos, Sapienza University of Rome.

Ioannis Chatzigiannakis, Sapienza University of Rome.

Teaching Assistants (TAs)

Adriano Fazzone, Sapienza University of Rome.

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When and where:

Monday 16.00–20.00, Via Ariosto 25, Room B2

Thursday 10.00–14.00, Via Tiburtina 205, Room 15


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 3.6, which are slightly incompatible, but it is easy to translate programs from one version to the other. In the class we will use Python 3.6.

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). For the other textbooks, we refer to with the author initials: A, ZM, ZAL, MRS.

Date Topic Reading
September 25 Introduction to data science and data mining Introduction to data mining
September 28 Introduction to the Linux shell for data science Notes
October 2 Basic algorithms concepts and distances Book chapters on function growth from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
Lecture notes on Big O notation from an old MIT course.
Lecture notes on P, NP, and NP-completenss from an old UCI course. (we did not talk about reductions in class, you can ignore this part if you want.) Chapter 3.5, MRS Chapter 6.3
October 9 Jaccard similarity and edit distance A Chapter (note that the definition for edit distance there is different: it allows substitutions instead of just insertions and deletions; however the main idea and the approach to compute it are the same)
October 12 Accessing datasets in CSV format and data manipulation Notes, Python Code
October 16 Dynamic programming and edit distance, Preprocessing for text mining MRS Chapters 2.0–2.2
October 17 Inverted indexes for boolean queries MRS Chapters 1.0–1.4
October 19 Data visualization Python Code
October 23 Scoring and term weighting MRS Chapters 4.0–4.2
October 24 Introduction to data structures, map ADT, Hash functions, hash map, balanced trees Wikipedia page on data structures, book chapters on hash functions, hash maps and binary search trees from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
October 26 Document Databases Notes, Python Code
October 30 (Binary) heaps Book chapter on heaps and heapsort from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
October 31 Sorting Book chapter on quicksort from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
November 2 Web Scraping Notes, Python Code
November 6 Introducion to MapReduce and Hadoop
November 7 Introducion to clustering, k-means, k-means++ Slides
November 14 Hierarchical clustering Slides
November 20 Introducion to graphs, MST Notes on graphs, book chapters on MST from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
November 21 Stacks, queues, graph traversal algorithms, shortest path Book chapters on stacks and queues, graph-traversal, and Dijkstra's algorithms from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
November 27 Centrality measures, PageRank Check the graph notes above. Chapter 5.1
November 28 PageRank (cont.) Check the graph notes above.
December 11 Introduction to social networks Slides
December 12 Epidemics in social networks Slides
December 19 Introduction to recommender systems Slides




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