Python for Data Science (Ph.D. Course)

Module Description and Motivation

Python is the most useful and powerful programming language oriented to data science and machine learning. Although simple, some aspects need to be carefully addressed. This Ph.D. course aims to introduce methods, techniques, and tools for the analysis of experimental data using Python language. Particular emphasis is placed on data manipulation and transformation.

Scientific Sector (SSD)

ING-IND/31 (Electrical Engineering)

Overview and Credits

Ph.D. Course in Information and Communication Technology (ICT). 18 hours, 3 CFUs.

Prerequisites

Students are expected to have some knowledg of basic computer programming.

Grading

Small practical project assignment on one of the course topics. The topic can also be agreed based on the student’s Ph.D. research program.

Syllabus

1. Introduction to Python.

2. Basics of Python programming and Python data structures

3. Python programming fundamentals

4. Main scientific libraries

5. Data manipulation 

6. Data preparation and transformation

7. Specific topics

Detailed contents.

Suggested Books

• Naomi Ceder, The Quick Python Book, 3rd Edition,‎ Manning, 2018.

• Naomi Ceder, Python - Guida alla sintassi, alle funzionalità avanzate e all'analisi dei dati, Apogeo, 2019. (in Italian)

• Jake VanderPlas, A Whirlwind Tour of Python, O'Reilly Media, 2016.

• Jake VanderPlas, Python Data Science Handbook: Essential Tools for Working with Data, O'Reilly Media, 2016.

• Matt Harrison, Machine Learning Pocket Reference: Working with Structured Data in Python, O'Reilly Media, 2019.

• Joel Grus, Data Science from Scratch, O'Reilly Media, 2nd Edition, 2019.

• Lecture notes.

Time and Location

The module will start on January 15 2025, with the following schedule:

• Wednesday, 14:00 - 17:00, Room DIET09

• Thursday, 14:00 - 17:00, Room DIET09

Office Hours

Every day by appointment.

Classroom

News, updates, and communications about the course will be available on Google Classroom, with the code: 3un72tv.

Course Materials

Lecture Slides:

1. Course overview [Slide - Notebook - Lecture note]

2. Introduction to Python [Slide - Notebook - Lecture note]

3. Basics of Python programming and Python data structures [Slide - Notebook - Lecture note]

4. Python programming fundamentals [Slide - Notebook - Lecture note]

5. Main scientific libraries [Slide - Notebook - Lecture note]

6. Data manipulation [Slide - Notebook - Lecture note]

7. Data preparation and transformation [Slide - Notebook - Lecture note]

8. Specific topics [Slide - Notebook - Lecture note]

Python Source Codes:

• Source code

Additional Resources:

• Markdown syntax [Web guide - Cheat Sheet 1 - Cheat Sheet 2]

GitHub

The course is also available on GitHub as Jupoyter Notebook at the following link.

Exams Session

Contact directly the instructor.