Data Science Techniques and Applications
To provide students with more advanced study of data analytics (building on the Python module), focussing on a range of applied data analysis techniques to convert information into knowledge.
On successful completion of this module a student will be expected to be able to:
- understand techniques for quantitative data analysis, and be confident in their ability to tackle data analysis problems;
- use Python to apply the techniques learned on the module;
- validate and evaluate data analysis results, and
- demonstrate satisfactory knowledge of network models.
The syllabus will build on
- Python programming expertise acquired in the first year of the programme, and
- basic knowledge of statistics, acquired in the previous term.
The new syllabus also accommodates the need to cover relational databases and SQL.
The new syllabus is as follows:
- introduction to the module; definitions of Data Science;
- statistics and probability refresher;
- the relational data model and how to query SQL databases;
- experiences with Data Science discovery in Python;
- Web data extraction;
- from data to graphs, and their relevant properties;
- centrality measures;
- correlation (if time allows).
Various topics will be demonstrated by practical lab sessions. Guest lecturers from industry may present parts of certain topics.
The ability to program in Python, SQL and a basic knowledge of statistics (such as obtained by having taken the module Programming with Data or as approved by the module leader). This module is designed for a minimal overlapping with the Machine Learning module.
Coursework assignments (20%) and a 2 hour exam (80%).
The reference textbooks will be:
- J. Grus, “Data Science from Scratch – First principles with Python.” O’Reilly, 2015, and
- G. Caldarelli and A. Chessa, “Data Science and Complexity Networks.” Oxford University Press, 2016.
- The following book is suggested reading: S. Marsland, “Machine Learning – An Algorithmic Perspective (2ed).” CRC Press, 2015.