IRDS - Introduction to Research in Data Science

This course is now hosted on Learn. However the information for the miniproject and the literature review is still held here.

IRDS Learn Page

Lecturer: Amos Storkey
TA: Jennifer Williams

Introduction

Welcome! This course has three objectives:

To introduce you to the research skills you will need for the PhD
To provide you with experience about practical issues in applying data science methods
To provide you with an overview of current research problems in data science

This course is open only to first year students in the CDT in Data Science, for whom it is a required course. We will enforce this requirement strictly. If you are not a member of the CDT and want to see similar material, I would recommend Data Mining and Exploration.

Course descriptor

Format

This course has a number of components:

An introduction to the CDT
A set of introductory lectures about research skills, including reading and distilling papers, giving talks, writing skills, asking questions etc.
A set of practical lectures covering the business of handling data science projects,
A series of guest lectures about research examples in data science. These will take up a significant part of the course and be from many potential supervisors from the CDT,
A series of lab sessions to introduce you to relevant software,
A small practical project in which you will apply data science methods to a real data set.

Lab Sessions

We will post a series of tutorial exercises to introduce you to software tools in machine learning and databases that may be useful for the small practical project and in your future career. These exercises are designed for self-study. You may work on them at your own pace. There is nothing to hand in. A tutor will be available during the scheduled weekly lab session times to answer questions.

Literature review

There will be an opportunity to do a literature review in an area of potential research interest. This literature review will be part of the overall mark for the project. The literature review should be about 3 to 4 sides of comments, and involve reviewing 3 to 5 primary papers and a handful of secondary papers.

Small Practical Project

In this project, you will apply data science methods of your choice to a real world data set. A list of suggested data sets will be posted for you to choose from, or you may suggest your own. You will produce a report of 4-5 pages explaining what analytic tools you used, why you chose them, and what results they produced. More detailed instructions for this project will be released in mid October. For a tentative timeline, please see the page about the small practical project.

Assessment

This course will be assessed entirely on the basis of the literature review and the small practical project. Your mark will be on the basis of your literature review (30%), the report on the project (50%) and also on a presentation that you will give when the project is finished (20%).