Programming for social data science

This course supports the mission of CSPS to equip students with the technical and practical
competences needed to access, manage, and analyze real-world data, enabling them to apply
computational methods and data-driven insights using Python and R.

By the end of this course, students will be able to independently acquire, manage, and
analyze data using both Python and R. They will develop practical proficiency in working
within interactive environments such as Jupyter and RStudio, enabling them to apply
fundamental programming concepts across different platforms. Learners will be equipped to
handle a variety of data formats, perform essential cleaning and transformation tasks, and
implement strategies for integrating and organizing heterogeneous datasets. These skills will
prepare them to engage confidently with data-driven problems in real-world contexts.
In addition, students will gain experience in accessing and utilizing publicly available data
repositories, applying appropriate preprocessing techniques to prepare datasets for analysis.
They will learn to address common data challenges, such as missing values and
inconsistencies, and to create effective visual representations to better understand their data.
Finally, students will be introduced to basic machine learning methods, including both
supervised and unsupervised approaches, and will acquire the ability to critically evaluate
model performance using standard metrics. Collectively, these objectives ensure that students
not only build technical competence but also develop the analytical mindset needed to
translate raw data into meaningful insights.