- Module code
- Taught during
- Session One
- Module leader
- Dr Philip Lewis
- Yes. Please refer to module pre-requisites below.
- Assessment method
- Practical Assessment (50%), Final test (50%)
This module will provide an introduction to the most fundamental data analytic tools and techniques, and will teach students how to use specialised software to analyse real-world data and answer policy-relevant questions.
Data Science is an exciting new area that combines scientific inquiry, statistical knowledge, substantive expertise, and computer programming. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. Students taking this module will be introduced to the most fundamental data analytic tools and techniques, and learn how to use specialised software to analyse real-world data and answer policy-relevant questions.
Upon successful completion of this module, students will:
- Have a sound understanding of the field of data science and develop the ability to analyse real-world data using some of its main methods;
- Become comfortable applying regression models for continuous and limited outcome variables;
- Explore more complex models, such as the widely-used panel data models;
- Develop familiarity with descriptive and predictive analytics, and their application to big data problems;
- Explore methods of text analytics and automated data acquisition;
This is a level two module (equivalent to second year undergraduate). In addition to the standard UCL Summer School entry criteria, applicants will be expected to have successfully completed at least one undergraduate level module in statistics and experience of using statistical computer software.
Classes (usually three or four hours per day) take place on the Bloomsbury campus from Monday to Friday any time between 9am and 6pm.
- Practical assessment (50%)
- Final test (50%)
Dr Philip Lewis works in the Department of Cell and Developmental Biology at UCL but originally studied for his PhD in the field of High Energy Physics. He worked on analysis of the massive datasets generated by the Tevatron collider, and on the computing infrastructure needed to store, retrieve and analyse the data. For the last five years he has worked in the field of Computational Biology, and currently helps to deliver the SysMIC course which trains doctoral students across the UK in the computational skills increasingly necessary for cutting edge biology research.