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Final Project Brief

Module 2: Introduction to Data Science

Peckham DAZ Programme, 2024-25

Learning Outcomes
LO1 Demonstrate knowledge of basic Data Science principles and techniques (Knowledge)
LO2 Complete a project presenting your skills in data gathering, analysis, and/or visualisation (Realisation)
LO3 Demonstrate the ability to work with data using Python programming techniques (Knowledge)
LO4 Reflect on the learning process and the final outcome (Enquiry, Communication)
LO5 (Advanced) Demonstrate the ability to independently develop a Python programming project on a selected topic (Enquiry, Knowledge, Realisation)
Assessment
Submission date 2 weeks after the final session (same weekday, 11:59 pm)
Adjusted submission date 3 weeks after the final session (same weekday, 11:59 pm)
Submission method: A single document (.pdf, .md., odt or .docx) sent to your tutor via email, containing:
1. A link to your code published on GitHub.
2. A project report describing your data sources, development process, any issues you encountered and how you solved them.
3. Links to exercises from class and homework developed throughout the course.
4. Any other supplementary materials you may choose to share (optional).

How will we mark your work?

Your work will be marked according to UAL Level 7 (MSc) assessment criteria. If you have any questions, please ask your tutor for clarification.

You will be given a "pass (Standard Brief)" or "pass (Advanced Brief)" grade, with verbal feedback. Please note that if you follow into MSc Computing and the Creative Industry programme at CCI and would like to use this project to get credits, you'll need to to meet the Advanced criteria.

https://www.youtube.com/watch?v=n1IXAFN_79I
https://www.arts.ac.uk/__data/assets/pdf_file/0034/179737/Assessment-Criteria-Level-7-PDF-94KB.pdf

When to expect feedback by

You can expect feedback within approx. 1 month of your submission deadline.

Adjusted deadline

If you're eligible for the adjusted deadline, please email your personal tutor directly and CC the Programme Leader (Marysia).

You're eligible for the adjusted deadline if:

  • you are disabled, have a learning difficulty, or are neurodiverse,
  • unexpected circumstances out of your control impacted your work on the final project (for example: illness, family situation, broken computer).

Late submissions:

  • submissions up to 7 days late will get a lower mark.
  • later submissions will be capped to a "pass" and may receive delayed feedback. Projects submitted more than 2 weeks after the deadline will be marked at the tutor's discretion.

Project specification

1. The Python Data Science project:

  • Example project topics:
    • visualise, analyse and describe a selected dataset found online,
    • create your own dataset using web scraping techniques or a selected web API and describe it.
    • manually create a dataset and present insights into it using Python programming techniques.
    • it may (but does not have to) refer to one of themes of the Southwark Economic Strategy 2023-30 (https://www.southwark.gov.uk/business/southwark-economic-strategy),
    • another topic that allows you to demonstrate the skills listed in this Final Project Brief.
  • The code should be available online on GitHub and consist mostly of .ipynb (Jupyter Notebook) and .py (Python script) files.
  • You can edit the code from class for a "pass", or merge, different files and look for your own resources for an Advanced project ("A" or "B")

2. Project report:

  • it should include:
    • a description of your data, its source, and collection method if applicable,
    • a reflection on representation and biases in the dataset and potential consequences,
    • what you learnt during working on the project,
    • what challenges you encountered in the process and how you approached them,
    • what went well in your project,
    • what you would do differently if working on a similar challenge in the future.
    • an ethical statement including your take on the data, its sources, biases, and potential uses of your project,
  • it should be submitted in writing (.pdf, .docx, .odt, or .md file) nad available in the GitHub repository.

3. Links or files of exercises from class or homework developed throughout the course.

  • these can be links to online coding platforms or links to code files shared elsewhere online.

4. The optional supplementary materials can include:

  • a different or work-in-progress versions of the project,
  • documentation of your learning process and exercises,
  • any other supporting documentation.