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

Academic year 2018–2019

"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.



Homework 5 is out; it is due on December 23.

Homework 4 is out; it is due on December 10.

Homework 3 is out; it is due on November 25.

Homework 2 is out; it is due on November 6.

Homework 1 is out; it is due on October 14.

We start classes on September 24.



Aris Anagnostopoulos, Sapienza University of Rome.

Ioannis Chatzigiannakis, Sapienza University of Rome.

Teaching Assistant (TA)

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When and where:

Monday 16.00–18.00, Via Ariosto 25, Room B2

Tuesday 14.00–16.00, 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 3.7.

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.

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 24 Introduction to data science and data mining Introduction to data mining
September 27 Introduction to Pandas, Open Data Sets, CSV Pandas Tutorial
Pandas Cookbook
Pandas Cookbook Chapter 1: Reading CSV files
Open Data Portal City of Rome
October 1 Introduction to basic data types
October 2 Basic algorithms concepts 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.
October 4 Introduction to Visualization with Matplotlib and Pandas Pandas Documentation
Python Plotting With Matplotlib (Guide)
Cookbook Chapter 4
Cookbook Lesson 1
October 8 Basic algorithms concepts (cont.) 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). Look also at the Wikipedia page on NP-completeness.
October 9 Distance measures Chapters 3.5.1, 3.5.2, 3.5.4
October 11 Introduction to JSON, Key-Value Databases and MongoDB Introduction to MongoDB and pyMongo
Quick start guide to mLab
Lab Notes on MongoDB
Lab Code on MongoDB
October 15 Distance measures (cont.) Chapter 3.5.5
October 16 Dynamic programming
October 18 Introduction to Web Scraping Introduction to HTML5
Introduction to BeutifulSoup
Errors & Exceptions in Python
Lab Notes on Web Scraping
Lab Code on Web Scraping
October 22 Preprocessing for text mining; inverted indexes for Boolean queries MRS Chapters 1.0–1.4, 2.0–2.2
October 23 Sorting Description and visualization of bubblesort (and other sorting algorithms).
November 5 Quicksort, Vector-space model for text mining, TFIDF Book chapter on quicksort from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein., MRS Chapter 6.2, 6.3.1
November 6 Scoring and term weighting MRS Chapter 6.3.2, 6.3.3
November 8 Introduction to API The Strategic Value of APIs
Introduction to REST API
Lab Notes on MongoDB REST API
November 12 Introduction to MapReduce, construction of lare indexes Most of the things we talked about can be found on this quick introduction
November 13 Introduction to data structures, ADTs, (binary) heap Wikipedia page on data structures, book chapter on heaps and heapsort from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
November 15 Introduction to MapReduce with Apache Spark and pySpark Lab Notes on Apache Spark
An Introduction to pySpark
Apache Spark in Python: Beginner's guide
November 19 Stacks, queues, maps, and hash functions Book chapters on stacks and queues, hash functions, hash maps, and binary search trees from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
November 20 Introduction to clustering and k-means Slides
November 22 MapReduce with Apache Spark and pySpark Project Gutenberg, Book 1
Project Gutenberg, Book 2
Project Gutenberg, Book 3
Project Gutenberg, Book 4
Project Gutenberg, Book 5
November 26 k-means++, hierarchical clustering Notes above, MMD Chapter 7.2
November 27 Introduction to graphs, graph traversal Notes on graphs, book chapter on graph-traversal from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
November 29 Introduction to Threads in Python Lab Notes on Python Threads
An Introduction to Python Threads
Web Scraping Food Recipes
December 3 Graph traversal (cont.), shortest paths, minimum spanning tree
December 4 Centrality measures
December 6 Introduction to Clustering in Python Lab Notes on Clustering in Python
December 10 Centrality measures (cont.), Intro to PageRank
December 11 Markov Chains and PageRank
December 17 Introduction to deep learning
December 18 Epidemics and influence models in social networks




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