This course is now hosted on Learn. However the information for the miniproject and the literature review is still held here.
Welcome! This course has three objectives:
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.
This course has a number of components:
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.
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.
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%).
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