COMPUTER PROGRAMMING AND DATA MANAGEMENT - 2

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, 2nd Edition, Wes McKinney, O'Reilly Media, Inc., ISBN: 9781491957660

The exam consists of developing and presenting a data analysis project, along with an individual oral exam covering the entire course content. It is designed to assess programming skills and problem-solving abilities through the techniques learned during the course.

oral

Grades will be assigned based on the level of knowledge of the course content, the ability to apply the learned techniques to real-world datasets, and the clarity of presentation.
Higher scores (27–30 with honors) reflect comprehensive knowledge and excellent analytical and communication skills. Intermediate scores (23–26) indicate a good understanding, while lower scores (18–22) correspond to a sufficient but limited preparation.

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.