Lecturers: Dr Jonathan Cardoso-Silva

Data science has unlocked exciting possibilities for social scientists through its diverse toolkit, including big data analysis, visualisation, and machine learning models, enabling them to extract valuable insights from their data. 

Yet, the success of a data-driven project hinges on data quality. This is where data engineering plays a pivotal role. Professionals must ensure that their acquired data is sufficient and accurate and must be adaptable to handle 'messy data' effectively.

A substantial portion of time in data-driven projects (anecdotally 80%) is dedicated to cleaning and preprocessing data, with only 20% said to be devoted to building, evaluating, and deploying machine learning models. Despite the emergence of new AI technologies, which promise to automate many coding tasks, data manipulation is likely to remain an indispensable skill due to the inherent messiness of real-world data.

By the end of this course, you will be proficient in producing stunning web reports and visual dashboards to display your collected data and showcase your newly acquired data-wrangling abilities.