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

Academic year 2020–2021

"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. We will base it on Amazon's AWS. After finishing the course, the students will have a large part of the knowledge required to pursue (independently) for an Amazon AWS Certification.



Homework 5 is out; it is due on January 14.

Homework 4 is out; it is due on December 23.

Homework 3 is out; it is due on November 29.

The due date for Homework 2 is postponed to November 15.

Homework 2 is out; it is due on November 8.

We have created a slack account, which you can (and should) use for questions, discussions, and so on. If you have not registered, email Cristina.

Be careful for Monday's time-schedule change. New time: 15.00-17.00.

We start classes on October 5.

Homework 1 is out; it is due on October 25.

You need to register to the class mailing list to be able to do homeworks, be part of groups, receive announcements, etc.; if you don't have the link, email Aris.



Aris Anagnostopoulos, Sapienza University of Rome.

Ioannis Chatzigiannakis, Sapienza University of Rome.

Teaching Assistants (TA)

The best way to ask any questions is through slack. If you are registered in the course mailing list and have not received an invitation from Cristina, email Cristina.

Cristina Menghini, Sapienza University of Rome.

Luca Maiano, Sapienza University of Rome.

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Ali Reza Seifi Mojaddar, Sapienza University of Rome.

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When and where:

Monday 15.00–17.00, Via di Castro Laurenziano 7a, Room 9 (3rd floor)

Wednesday 16.00–18.00, Via di Castro Laurenziano 7a, Room 9 (3rd floor)

Friday 14.00–18.00, Via Tiburtina 205, Room 15

As per university's policy, students will need to register to follow each lecture physically:


Following online

We will use zoom for the lectures. To obtain the credentials you need to register your email information.


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 3.

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. For Windows users the Anaconda distribution has packaged all of them together and you can download it for free. For MAC/Linux users, all packages can be installed using the pip3 tool.

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
October 5 Introduction to data science Introduction to data science at Sapienza
October 7 Introduction to data mining Introduction to data mining
October 9 Introduction to Laboratory Tools
Introduction to AWS Educate
Introduction to AWS S3
Open Datasets
CSV and JSON in Python Pandas
Laboratory Notes
How to open an AWS Educate account
How to Use Jupyter Notebook in 2020: A Beginner’s Tutorial
Create and run your first Python project using pyCharm
Free Educational Licenses
Introduction to GitHub
European Open Data Portal
Kaggle eCommerce behavior dataset
October 12 Basic data types
October 14 Basic algorithms concepts Book chapter 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 16 Introduction to Visualization Laboratory 2: Data Visualization
Pandas Documentation on Visualization
Pandas Documentation on Grouping
Python Plotting With Matplotlib (Guide)
Grouping Data with Pandas (Guide)
Cookbook Chapter 4
Cookbook Lesson 1
seaborn: statistical data visualization
October 19 Basic algorithmc concepts (cont.)
October 21 Recursion Analysis of recursive algorithms using backward substitution. (The third video in the series shows the analysis of binary search.)
Code for binary search in Python.
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 23 AWS Elastic Cloud Compute

Laboratory 3: AWS EC2 & AWS CLI
Tutorial: Getting started with Amazon EC2 Linux instances
How to connect to your S3 buckets from your EC2 instances
AWS Command Line Interface (CLI)

October 26 NP-Completeness Those who are more interested in learning about the concepts of P, NP, NP-completenss, can look at the chapter on NP completeness from the book "Algorithms" by Dasgupta, Papadimitriou, and Vazirani. This material is optional.
October 28 NP-Completeness (cont.), Distance measures Chapter 3–3.4
October 30 Linux Shell Programing Environment

Laboratory 4: Linux Command Line
Introduction to Bash Shell Scripting
The Shell Scripting Tutorial
Learn Regular Expressions Online
Test Regular Expressions Online
GNU sed, a stream editor
Introduction to GNU sed

November 2 Distance measures (cont.) A Chapter
November 4 Distance measures (cont.)
November 6 AWS DynamoDB: Key-Value and Document Databases

Laboratory 5: AWS DynamoDB
What is NoSQL?
Getting Started with DynamoDB
Getting Started Developing with Python and DynamoDB
Boto is the Amazon Web Services (AWS) SDK for Python

November 9 Dynamic programming, and basic computer-architecture concepts Book chapter on edit distance from the book "Algorithms" by Dasgupta, Papadimitriou, and Vazirani.
November 11 Discussion about multidimensional arrays, text preprocessing Check the web page link here MRS Chapters 2.0–2.2
November 13 Introduction to HTML
Introduction to Web Scraping
Introduction to NLP
Laboratory 6: Web Scrapping
Introduction to HTML5
Structuring the web with HTML
Introduction to BeautifulSoup
Selenium with Python
Natural Language Processing with spaCy
Use Sentiment Analysis with Python
November 16 Inverted indexes for Boolean queries MRS Chapters 1.0–1.4
November 18 Introduction to data structures, ADTs, linked lists, dictionaries, hashing Wikipedia pages on data structures and linked lists, book chapter on heaps and heapsort from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein. Book chapter on hashing from the book "Algorithms" by Dasgupta, Papadimitriou, and Vazirani.
November 20 TFIDF, scoring and term weighting, implementation with inverted indexes MRS Chapters 6.2, 6.3.1–6.3.3, 7.1.0
November 23 Sorting Book chapters on insertion sort, mergesort, and quicksort from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
November 25 Introduction to MapReduce, construction of large indexes Most of the things we talked about can be found on this quick introduction
November 27 Introduction to Elastic Map Reduce Laboratory 7: Elastic Map Reduce
Big Data Analytics Options on AWS
Bigtable: A Distributed Storage System for Structured Data
Getting Started: Analyzing Big Data with Amazon EMR
Using EMR Notebooks
PySpark: the Python API for Spark
November 30 (Binary) heap, the ML/DM cycle Book chapter on heaps and heapsort from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein.
December 2 (Binary) heap (cont.)
December 4 Introduction to Clustering in Python Laboratory 8: Clustering
Clustering in Python
K-Means in Python
December 7 Introduction to clustering, hierarchicla clustering, and k-means MMD Chapter 7.2, Slides
December 9 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
December 14 Shortest paths and minimum spanning treees Book chapter on shortest paths and on minimum spanning trees from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein
December 16 Centrality measures, PageRank
December 18 Introduction to Graphs in Python
Twitter API
Laboratory 9: Graphs
NetworkX: Network Analysis in Python
Tweepy: An easy-to-use Python library for accessing the Twitter API
Twitter API Documentation
Amazon Simple Queue Service
Python Code Samples for Amazon SQS
December 21 PageRank (cont.) MMD Chapter 5.1
December 23 Community detection, minimum cut, end of course Book chapter on minimum cut from the book "Probability and Computation" by Mitzenmacher and Upfal




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.