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This module builds on the quantitative methods modules of year 1 (Probability, Statistics and Modelling I) and year 2 (Probability, Statistics and Modelling II). It introduces data science techniques as a means for more sophisticated quantitative data analysis. This module aims to introduce students to computational methods for crime science. It consists of ten combined sessions (lectures + practical sessions). Practical sessions complement the lectures and enable the students to put the concepts into practice using the R programming language. In the practical sessions, the students will learn the skills necessary to conduct a full data science project. Lectures: The first part (web data collection) teaches the students how to collect data from online resources both through structured queries using APIs and through custom-made web-scraping. In the second part (text mining), the students learn how to handle messy text data and how to quantify and analyse this powerful type of data. The third part (machine learning) focuses on supervised and unsupervised machine learning where the students gain an understanding of the kinds of machine learning, the reporting and performance assessment of machine learning models, and commonly used algorithms. The module ends with two guest lectures by researchers working on advances on the intersection between data science and crime problems. The techniques covered in this module will be of relevance to students undertaking their final year independent research project. This module is suitable for those intending to start in data science positions.