Assessment

The intended learning outcomes (ILOs) of FDS are that, on successful completion of this course, you should be able to: 

1. Describe and apply good practices for storing, manipulating, summarising, and visualising data
2. Use standard packages and tools for data analysis and describing this analysis, such as Python and LaTeX.
3. Apply basic techniques from descriptive and inferential statistics and machine learning; interpret and describe the output from such analyses.
4. 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.
5. Complete a data science project and write a report describing the question, methods, and results.

There are 2 formative (not for credit) courseworks, 1 summative (for credit) coursework, and an exam. You can see the release dates and submission deadlines in the Coursework Planner in Learn 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:

• Formative exercise 1, Visualisation: You will practise identifying how the principles of visualisation covered in Week 3 are broken in bad visualisation, and then produce a new, fixed version of the visualisation using python and a Jupyter notebook.
• Formative exercise 2, Visualisation and interpretation: You will practise using python and Jupyter notebooks to summarise and visualise a dataset using the principles of visualisation covered early in Semester 1. You will also interpret your data summaries and visualisations.
• Coursework 1, Project and presentation: 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 project report, your code and a presentation.
• Exam: A 2-hour exam in the May exam diet 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 exam will also assess claims made in a case study and 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, report and presentation that covers all the ILOs, and which will be completed over four days and require around 15 hours of work.