COMPUTER PROGRAMMING AND DATA MANAGEMENT - 2 - PRACTICE

This course covers part of the "quantitative" aspects of the master program, and aims to provide the student with knowledge and skills on the computational aspects fundamental for the data science field.

More specifically, the goal of this course is to teach students how to use a programming language to write scripts and more complex software libraries to handle data, including storage, loading, preparation, processing and visualization.

The course introduces the basics of the design and implementation of data processing software applications and libraries. Data processing is intended as the many activities that can be performed on data through automatic computation, including preprocessing, handling, and analysis.

This course teaches students to define a problem and its solution in terms of data structures and information and to use a programming language to solve it effectively.

The students will achieve the following objectives:

Knowledge: i) learn the basics of data handling and the most common data structures; ii) understand how to interpret and write data handling programs in the Python programming language;

Application of knowledge: i) analyze problems and design formal algorithmic solutions using data structures; ii) translate solutions into computer programs to be applied to data.

Communication: i) generate basic data visualizations for preliminary analysis.

The student must have attended the first module of this course proficiently. Specifically, he/she must already know how to write programs in the Python programming language.

1) Introduction
2) Recap of Python built-in data structures and functions
3) Modules and Object-Oriented python
4) Using the NumPy numerical computing package
5) Using he Pandas library
6) Storing and loading data from different sources
7) Data cleaning and preparation
8) Data wrangling, aggregation and manipulation
9) Plotting and visualization
10) Handling time series

Python for Data Analysis, Wes McKinney, O'Reilly Media, Inc., ISBN: 9781491957660

The exam will be written and it will include three theoretical questions, about topics covered during the whole course, and a small exercise, asking to code from scratch a solution to a practical problem. An example of the exam is provided in the support material section of the Moodle website.

orale

Il/la docente ha il dovere di vigilare affinché siano rispettate le regole di autenticità e originalità delle prove d'esame. Di conseguenza, nei casi in cui vi sia il sospetto di un comportamento irregolare, l'esame può prevedere un ulteriore approfondimento, contestuale alla prova d'esame, che potrà essere realizzato anche in modalità differente rispetto alle modalità sopra riportate.

Each theoretical question will grant up to 4 points (total 12 points) and the exercise will grant up to 20 points.

The students must submit the answers to the theoretical questions within 30 minutes form the start of the exam and the whole solution must be submitted within 90 minutes overall (i.e. if the student submits the theoretical questions in advance, he/she will have more time available for the exercise).

Note that students can obtain up to 2 extra points by submitting the challenges assigned each week before the following the practice lecture. Specifically 1 extra point will be granted by submitting at least two (fully functional) assignments and 2 extra points will be granted by submitting all the five (fully functional) assignments.

The course combines theoretical and practical sessions to provide a solid understanding of the fundamental concepts and operational techniques of data analysis. Lectures introduce the core tools of the Pandas library, integrating theoretical explanations with practical examples in Python.

Hands-on exercises allow students to apply what they have learned to real-world datasets, exploring data manipulation, cleaning, transformation, and exploration techniques. Independent experimentation is encouraged to develop problem-solving skills and promote active learning.