Informatics 2 - Foundations of Data Science (INF2-FDS) is a 20 credit course at Level 8, normally taken in Year 2. It runs throughout the year. There is no exam for this course, the course mark is based 100% on coursework. The University descriptor is here.
Course Outline
The course will be delivered through a combination of lectures, workshops, and practical labs; students will be expected to complete both pencil-and-paper and programming-based exercises on their own time as well as during workshops and scheduled labs. Students will complete a data science project to assess their practical and writing skills, and will also take a class test in each semester. Technical topics in the course will be covered in three sections, with indicative topics listed below. Practical aspects of these will use a Python-based ecosystem.
1. Data wrangling and exploratory data analysis
Working with tabular data
Descriptive statistics and visualisation
Linear regression and correlation
Clustering
2. Supervised machine learning
Classification
More on linear regression; logistic regression
Generalization and regularization
3. Statistical inference
Randomness, simulation and sampling
Confidence intervals, law of large numbers
Randomized studies, hypothesis testing
Interleaved with these topics will be topics focusing on real-world implications (often using case studies), critical thinking, working and writing skills. These may be introduced in lecture but will often include a workshop discussion and/or peer review of written work. Indicative topics include:
A. Implications:
Where does data come from? (Sample bias, data licensing and privacy issues)
