Advanced Topics in Data Science

Learning Outcomes

By the end of the course, students will be able to:

a) Apply advanced data science techniques to solve real-world problems, particularly in digital humanities, natural language processing, and related domains;

b) Understand advanced data science techniques and their theoretical foundations;

c) Apply advanced statistical and machine learning methods, including deep learning, to complex data science problems, and select appropriate techniques based on data characteristics and problem requirements;

d) Implement effective data manipulation and preprocessing workflows;

e) Demonstrate proficiency in handling large and complex datasets through advanced techniques, including data cleaning, feature engineering, and dimensionality reduction, with a focus on text and multimodal data in line with the programme’s scope;

f) Create and interpret advanced data visualisations using appropriate tools and libraries, and communicate insights effectively to different stakeholders;

g) Design and implement practical big data pipelines using technologies such as Apache Spark, Hadoop, and distributed computing frameworks;

h) Plan and evaluate data science methodologies in real-world scenarios.