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

## Academic year 2019–2020

"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

- Data Scientist: The Sexiest Job of the 21st Century (pdf)
- Find true love with data mining

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.

### Announcements

Homework 5 is out; it is due on December 22.

Homework 4 is out; it is due on December 1.

Homework 3 is out; it is due on November 15 17.

Homework 2 is out; it is due on October 30.

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.

Homework 1 is out; it is due on October 13.

You **need to register to the class list** to be able to do homeworks, be part of groups, receive announcements, etc.; if you haven't, mail Aris.

Because of constructions, the first 4 weeks the class location are different from the ones written below. Please check at http://datascience.i3s.uniroma1.it/it/node/5855.

We start classes on September 23.

### 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 list and have not received an invitation from Cristina, email Cristina.

This email address is being protected from spambots. You need JavaScript enabled to view it. , Sapienza University of Rome.

This email address is being protected from spambots. You need JavaScript enabled to view it. , Sapienza University of Rome.

This email address is being protected from spambots. You need JavaScript enabled to view it. , Sapienza University of Rome.

### When and where:

Monday 16.00–18.00, Viale dell'Università 36 (palazzina ex-Tumminelli) (Building CU007), Room 13

Wednesday 16.00–18.00, Viale dell'Università 36 (palazzina ex-Tumminelli) (Building CU007), Room 14B

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:

- C. Aggarwal, "Data Mining: The Textbook," Springer (must be downloaded from Sapienza)
- M. J. Zaki and W. Meira, Jr., "Data Mining and Analysis: Fundamental Concepts and Algorithms," Cambridge University Press
- R. Zafarani, M. A. Abbasi, and H. Liu, "Social Media Mining: An Introduction," Cambridge University Press
- J. Leskovec, A. Rajaraman, and J. Ullman, "Mining of Massive Datasets," Cambridge University Press
- C. D. Manning, P. Raghavan and H. Schütze, "Introduction to Information Retrieval," Cambridge University Press

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: http://docs.python.org/3.

If you would like to buy some books, you can check the

- "Learning Python, 5th edition," by Mark Lutz. It is a bit verbose, but it presents well the features of the language.
- "Python Pocket Reference, 5th edition," by Mark Lutz. It is usefull as a quick reference if you know more or less the language and you are searching for some information.

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.

### 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 23 | Introduction to data science | Introduction to data science at Sapienza |

September 26 | Introduction to Pandas, Open Data Sets, CSV | Laboratory 1: Computational Thinking, Basic Tools and First Practice Jupyter Notebook of Code used in Lab 1 Python Basic and Composite Data types 10 minutes into Pandas Pandas Tutorial Open Data Portal City of Rome |

September 30 | Introduction to data mining and basic data types | Introduction to data mining |

October 2 | 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 3 | Introduction to Visualization with Matplotlib and Pandas | Laboratory 2: Data Visualization Jupyter Notebook of Code used in Lab 2 Pandas Documentation Python Plotting With Matplotlib (Guide) Cookbook Chapter 4 Cookbook Lesson 1 |

October 7 | Basic algorithms concepts (cont.) | 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 9 | Basic algorithms concepts (cont.) | 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 10 | Introduction to JSON, Key-Value Databases and MongoDB | Jupyter Notebook of Code used in Lab 3 Introduction to MongoDB and pyMongo Quick start guide to mLab |

October 14 | Similarity and distance measures | Chapter 3–3.4 |

October 16 | Distance measures (cont.) | A Chapter 3.4.2.1 |

October 17 | Introduction to Web Scraping | Jupyter Notebook of Code used in Lab 4 Introduction to HTML5 Introduction to BeutifulSoup |

October 21 | Dynamic programming | Book chapter on edit distance from the book "Algorithms" by Dasgupta, Papadimitriou, and Vazirani. |

October 24 | Web Scraping | Jupyter Notebook of Code used in Lab 4 |

October 28 | Preprocessing for text mining, inverted indexes for Boolean queries | MRS Chapters 1.0–1.4, 2.0–2.2 |

October 30 | TFIDF, scoring and term weighting | MRS Chapters 6.2, 6.3.1–6.3.3 |

October 31 | Introduction to API | The Strategic Value of APIs |

November 4 | 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 6 | Sorting | Book chapters on insertion sort, mergesort, and quicksort from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein. |

November 7 | Introduction to Threads in Python | An Introduction to Python Threads Web Scraping Food Recipes |

November 11 | Introduction to MapReduce, construction of large indexes | Most of the things we talked about can be found on this quick introduction |

November 13 | Dictionaries, binary search trees, and hashing | Book chapter on binary search trees 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 18 | Introduction to clustering and k-means | Slides |

November 20 | Introduction to clustering and k-means (cont.), hierarchical clustering | MMD Chapter 7.2 |

November 25 | 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 27 | Queues, stacks, BFS, DFS | Book chapter on queues and stacks from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein. |

November 28 | Introduction to Threads in Python | An Introduction to Python Threads Web Scraping Food Recipes |

December 2 | Shortest path | Book chapter on shortest paths from the book "Introduction to Algorithms" by Cormern, Leiserson, Rivest, and Stein. |

December 4 | Centrality measures | |

December 5 | Introduction to Clustering in Python | K-Means in Python |

December 9 | PageRank | MMD Chapter 5.1 |

December 11 | Introduction to social networks | |

December 16 | Epidemics and influence | |

December 18 | Wikipedia | |

December 19 | Introduction to the Linux shell for data science |

### Homeworks

- Homework 1 (due on 13/10, 23.59)
- Homework 2 (due on 30/10, 23.59)
- Homework 3 (due on 15/11 17/11, 23.59)
- Homework 4 (due on 1/12, 23.59)
- Homework 5 (due on 22/12, 23.59)

** **

**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