The intended learning outcomes (ILOs) of FDS are that, on successful completion of this course, you should be able to:
- Describe and apply good practices for storing, manipulating, summarising, and visualising data
- Use standard packages and tools for data analysis and describing this analysis, such as Python and LaTeX.
- Apply basic techniques from descriptive and inferential statistics and machine learning; interpret and describe the output from such analyses.
- Critically evaluate data-driven methods and claims from case studies, in order to identify and discuss a) potential ethical issues and b) the extent to which stated conclusions are warranted given evidence provided.
- Complete a data science project and write a report describing the question, methods, and results.
There are 3 courseworks and one class test; you can see the schedule in Learn Ultra or import the data into your calendar and use the coursework calendar file https://course.inf.ed.ac.uk/calendar/inf2-fds.ics to get all the coursework events into your Outlook or Google calendar.
The assessment activities are designed to assess these learning outcomes as follows:
Component | Time | Percentage | ILOs assessed |
Coursework 1: Data wrangling and visualisation. | mid-Semester 1 | 20% | 1, 2 |
Coursework 2: Critical evaluation of data science study and media article. | Start of Semester 2 | 20% | 4 |
Coursework 3: Project | Second half of Semester 2 | 40% | 1, 2, 3, 5 |
Class test | Semester 2, Revision period | 20% | 1, 3 |
- Coursework 1, Data wrangling and visualisation: We will ask you to use python and Jupyter notebooks to manipulate, summarise and visualise one or more datasets using principles of visualisation that cover early in Semester 1. You will also need to interpret your data summaries and visualisations.
- Coursework 2, Critical evaluation of data science study and media article: You will read a data science study, which may be a scientific paper, and a media article that describes the study. We will give you a choice of studies and accompanying media articles. We will give you a set of questions about the article and the media article. The answers to these questions will form a critical evaluation of the methods and claims in the studies. The questions will assess if you can identify ethical issues raised, and whether the conclusions are justified by the evidence provided.
- Coursework 3, Project: We will give you a choice of datasets, and ask you to complete a data science analysis of your chosen dataset, including proposing a question, describing the methods and results, and evaluating your contribution. We will assess your report of your project. The project may be done in groups or individually.
- Class test: A 2-hour class test around in the Semester 2 Revision period will assess your knowledge of good practices for storing, manipulating, summarising and visualising data, and how well you can apply basic techniques from descriptive and inferential statistics and machine learning and interpret and describe the output from such analyses. The class test may also assess your understanding of ethical issues.
Resit assessment
If you do not pass the course, there will be a resit assessment in August. Assessment for the resit will be a short data science analysis and report that covers all the ILOs, and which will be completed over four days and require around 15 hours of work.