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

Academic year 2021–2022

"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.

 

Announcements

We start classes on September 20.

Homework 1 is out; it is due on October 10.

You need to register to the class mailing list to be able to do homeworks, be part of groups, receive announcements, etc.; if you have not done so, email Aris.

 

Instructors

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 Luca, email Luca.

Luca Maiano, (This email address is being protected from spambots. You need JavaScript enabled to view it. ) Ph.D. candidate in Data Science, Sapienza University of Rome.

This email address is being protected from spambots. You need JavaScript enabled to view it. , Data Science student, Sapienza University of Rome. (LinkedIn)

This email address is being protected from spambots. You need JavaScript enabled to view it. , Data Science student, Sapienza University of Rome. (LinkedIn)

This email address is being protected from spambots. You need JavaScript enabled to view it. , Data Science student, Sapienza University of Rome. (LinkedIn)

This email address is being protected from spambots. You need JavaScript enabled to view it. , Data Science student, Sapienza University of Rome. (LinkedIn)

 

When and where:

Monday 14.00–16.00, Cità Universitaria, Building CU0002, Room IX.

Wednesday 16.00–18.00, Cità Universitaria, Building CU035, Room 15.

Tuesday 15.00–19.00, Via Tiburtina 205, Room 17.

As per university's policy, students will need to register to follow each lecture physically: https://prodigit.uniroma1.it/

 

Following online

  • We will use zoom for the lectures. To obtain the credentials you need to register your email information.
  • For the class lectures we will not post registrations. I will give you link to last year's lectures but there is no guarantee that the content will be exaclty the same.

 

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 TAs and, if needed, 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: https://www.python.org/doc/.

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.

 

Syllabus

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 20 Introduction to data science Introduction to data mining
September 21 Introduction to cloud computing
Introduction to AWS, AWS Academy, EC2, EBS, S3

Laboratory 1: Introductory course to AWS Cloud and Core Services (Part 1)
Laboratory 1: Introductory course to AWS Cloud and Core Services (Part 2)
Hosting a static website using Amazon S3
Tutorial: Getting started with Amazon EC2 Linux instances
How to connect to your S3 buckets from your EC2 instances
AWS Academy Cloud Foundations Course:

  • Module 1, 2, 3, 4, 6

September 22 Types of data, analysis of algorithms Notes: 5.1
September 27 Information on Github, Anaconda, etc.
September 28 Introduction to Code Development Tools
Introduction to AWS Labs, Cloud9, SageMaker, pyCharm, Jupyter
Open Datasets
CSV and JSON in Python Pandas
Laboratory 2: Basic Tools & First Practice
How to Use Jupyter Notebook in 2020: A Beginner’s Tutorial
Tutorial on AWS Cloud9
Create and run your first Python project using pyCharm
Free Educational Licenses
Introduction to GitHub
European Open Data Portal
Kaggle eCommerce behavior dataset
AWS Academy Cloud Foundations Course:
  • Module 13
September 29 Analysis of Algorihtms (cont.)
October 4 Analysis of Algorihtms (cont.)
October 5 Introduction to Visualization Laboratory 3: 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
Kaggle Steam Reviews Dataset 2021 dataset
October 6 Complexity classes and NP completeness
October 11 NP completeness (cont.), introduction to distance measures Chapter 3.5
October 12 Data Science at the Command Line

Laboratory 4: Data Science from the Command Line
Tutorial: Getting started with Amazon EC2 Linux instances
How to connect to your S3 buckets from your EC2 instances
AWS Command Line Interface (CLI)
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

October 19 Introduction to HTML
Introduction to Web Scraping
Laboratory 5: 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

 

Homeworks

 

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.