COURSE CURRICULUM

This Data Science course is structured around five core modules that provide a balanced mix of programming, analytics, big data, machine learning, and professional practices, culminating in an individual research project. The first module, Programming for Data Science (30 credits), focuses on developing practical programming skills essential for data science tasks. Students learn to work with high-level languages such as Python and use popular libraries and tools like pandas for data wrangling, BeautifulSoup for web scraping, and NLTK for natural language processing. The second module, Data Analytics (30 credits), introduces foundational concepts in mathematics and statistics, training students to work with multivariate datasets and communicate insights effectively through visualisation and storytelling. It also introduces the data science lifecycle through real-world case studies. The third module, The Data Science Professional (30 credits), explores the broader context of working with big data. This includes topics such as data governance, ethics, GDPR compliance, and data protection. It also covers the technical aspects of data engineering, SQL and NoSQL databases, and cloud-based distributed computing systems, while building leadership and management skills in a data-driven environment. The fourth module, Machine Learning and Big Data (30 credits), equips students with knowledge of machine learning algorithms and techniques, including supervised, unsupervised, and reinforcement learning, as well as neural networks. Students learn to apply these methods across various sectors such as healthcare, agriculture, and bioinformatics. The course concludes with an Individual Research Project (60 credits), where students apply their cumulative learning to solve a complex engineering or industry-relevant problem, often involving real-world data and professional practices. This hands-on project helps students gain valuable experience in research and technical problem solving, preparing them for roles in data science, analytics, and related fields.