Context

The overall assessment package is intended to test students’ comprehension of, and ability to integrate, technical skills with a broader understanding of, and reflection upon, computational approaches to urban research and spatial data science.

Assessment Elements

The assessments are grounded in a mixture of critical reflection and group work that map on to real-world data science challenges, including:

  1. Timed Open Book Examination (30%) to ensure that you are acquiring the fundamentals of data analysis in Python, and simulate the real-world challenge of having to write code to a deadline (to be completed between 13:30-19:30 on Thursdsay, 23 November 2023);
  2. Collaboratively evaluating and analysing a data set (60%) as a group you will be determining the suitability of a data set for tackling an analytical ‘problem’ using a mix of coding, analysis, and presentation skills in a reproducible format Tuesday, 19 December @ 18:00);
  3. Reflecting on the process (10%) to better-understand why a project succeeded/failed so as to improve future outcomes and recognise the contributions of individual members of the group to the success of the project (due Wednesday, 20 December 2023 @ 18:00).

Rationale

Collectively, these assessments seek to provide multiple opportunities to ‘shine’ both individually and as part of a group. You do not need to be the best programmer in the class in order to do well on these assessments. Indeed, focussing only on the programming is likely to result in a low mark because it missed the context in which data science and data analysis ‘work’. As a budding data scientist/analyst your job is just as much to understand your audience and their needs: you will work with clients who can’t really articulate what they want or why, so good project management often involves putting yourself in your client’s shoes and working out how to translate what they say they want into what they actually need.

You will therefore do poorly on the assessments if you do not do the readings, watch the pre-recorded lectures, or participate in discussions (both online and in-person during practicals and classes). These provide you with context for the work that is being done when you start typing and running code in a Jupyter Notebook. Code is the how. Context is the why.