COMP13212 Data Science syllabus 2020-2021
This course unit is about extracting knowledge and information from data. Topics include: measuring uncertainty in data, and interpreting data using visualisation, statistical methods, probabilistic Bayesian methods, and basic machine learning techniques. Students will also gain an introduction to the Jupyter notebook, and techniques in Python to implement data science methods.
This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact. Please see Blackboard / course unit related emails for any further updates.
Lectures and coursework reported via Jupyter notebooks in Python.
- Lectures (22 hours)
- Practical classes & workshops (12 hours)
On successful completion of this unit, a student will be able to:
- Demonstrate awareness of the “Data Science Process” by describing qualitatively how it would apply in a given situation.
- Demonstrate awareness of need for data cleaning descriptively and by doing elementary data cleaning and preparation in the laboratory.
- Demonstrate ability to measure and express uncertainty from a set of data and quantities derived from that data.
- Demonstrate ability to choose and build appropriate models of different datasets.
- Demonstrate ability to evaluate the quality of a model of a dataset.
- Demonstrate the ability compare different models of a dataset and models of different dataset in order to draw statistically sound conclusions about hypotheses or claims from the data.
- Demonstrate ability to use python tools to: read and write data sets to and from files, produce descriptive statistics and draw conclusions from these, produce graphical visualisation and draw conclusions, perform basic statistical tests including the difference between means, and perform a simple machine learning experiment by building an email spam filter using a naive Bayes classifier.
No reading list found for COMP13212.