COMP13212 Data Science syllabus 2019-2020
This course unit has two objectives. The first is to introduce the student to a range of fundamental, non-trivial algotithms, and to the techniques required to analyse their correctness and running-time.
The second is to present a conceptual framework for analysing the intrinsic complexity of computational problems, which abstracts away from details of particular algorithms.
To give students awareness of the elements of the “Data Science
Process” (many, but not all, of which will be studied in detail in this course).
To give students practice in using python tools for data processing and analysis, including numpy, scipy.stats, pandas, and Jupyter notebooks.
To give students understanding and practice in exploration and visualization of data.
To give students understanding of uncertainty in data, including how to measure it, visualise it, and model it.
To give students an introduction to statistical thinking and Bayesian reasoning.
To give students an introduction to ethical considerations in analysing data and drawing responsible conclusions.
To give a brief introduction to machine learning by use of naive Bayes classification and linear and logistic regression.
Lectures and coursework reported via Jupyter notebooks in Python.
- Lectures (22 hours)
- Practical classes & workshops (12 hours)
Learning outcomes are unknown for COMP13212.
COMP13212 does not have a specified reading list.