Programming for Data Science

The teaching activity takes place in class, with exposure to concepts, techniques, and methods, with a strong focus on solving practical
problems. Software will be used to support problem-solving.

The Programming for Data Science course unit is intended for students without previous programming experience. It is a structuring unit of
the master’s course in data analysis and decision support systems, as students will learn the fundamentals of Python programming
necessary to carry out programming activities in other curricular units and to develop Data Science projects. In this curricular unit, after a
brief introduction to the concepts of algorithms and programming, students will become familiar with the base libraries for Data Science
projects, such as NumPy, Pandas, Matplotlib, SciPy, and Scikit-learn. This curricular unit is intended to equip the student with a set of skills
to autonomously carry out programming and development activities for Data Science projects.

1. Basic Python Notions
1.1 Syntax and execution.
1.2 Basic types and operators.
2. Data Structures
2.1 Lists and tuples.
2.2 Dictionaries and sets.
3. Programming Fundamentals
3.1 Conditions.
3.2 Loops.
3.3 Functions.
4. Working with Data
4.1 Files.
4.2 NumPy.
4.3 Basic Pandas.
5. Data Visualization
5.1 Simple plots.
5.2 Histograms and boxplots.
6. Introduction to the scikit-learn Library
6.1 Basic concepts.
6.2 Regression, classification, and clustering.
6.3 Model evaluation.
7. Other Applications
7.1 Text manipulation (strings and basic NLP notions).
7.2 Simple automation (scripts for repetitive tasks).
7.3 APIs and access to external data.

NAO