The Other Course

This is a course that improves your skills in data science and gives you the space to write a paper within certain guardrails.

Preamble

Overview

The course will be an enormous amount of work and cause you a large amount of stress because it is likely your first opportunity to do unstructured original research. This is unfortunate, but there’s little way around it. All I can tell you is that having done this course, it’ll be easier in the future. And pressure makes diamonds.

The purpose of this course is to write an original research paper, and in the process of that, to learn some really useful skills. The paper will incorporate relevant literature, detailed data collection processes, be reproducible, use technically and statistically sound methodology, and present the outcomes in an informative manner. The work is divided into two parts, a report describing the relevant background and technical processes (an R Markdown produced pdf) and an interactive layer (either Shiny or an R Package).

The purpose of this course is to explore the technical aspects required to design and complete an end-to-end data science project, similar to those that students are likely to encounter in a professional environment. This course will require students to:

Essentially this course provides students with the freedom to conduct original research on a topic of interest to them within certain guidelines.

FAQ

Course learning objectives

Pre-requisites

You need to have taken ‘The Course’ or equivalent, such that you’ve taken courses such that you’ve covered all of Telling Stories with Data.

Acknowledgements

Thanks to the following who helped develop this course: Thomas William Rosenthal.

Content

Week 1

Tasks:

Readings:

Week 2

Tasks:

Readings:

Week 3

Tasks:

Readings:

Week 4

Tasks:

Week 5

Tasks:

Readings:

Week 6

Tasks:

Readings:

Week 7

Tasks:

Readings:

Week 8

Tasks:

Readings:

Week 9

Tasks:

Readings:

Week 10

Tasks:

Readings:

Week 11

Tasks:

Readings:

Week 12

Finalise all apsects.

Tasks:

Assessment

Learning diary

Task: Each week you will read relevant papers and books, engaging with them by writing notes and completing exercises. You will use GitHub to manage these notes and exercises and email a link to me at the end of each week. Additionally, reflect on what went well, what has room for improvement, and consider ‘lessons learned’ during the week.

Date: At the end of the week please send me a link to the GitHub repo that contains this diary.

Weight: 15 per cent.

Presentation I

Task: 10-15-minute presentation on what you’ve learned about the literature and plans.

Date: Roughly end of Week 4 (exact date determined by lab presentation cycle— the last Friday of the month).

Weight: 15 per cent.

Presentation II

Task: 10-15-minute presentation on what you’ve learned about the data.

Date: Roughly end of Week 8 (exact date determined by lab presentation cycle— the last Friday of the month).

Weight: 15 per cent.

Presentation III

Task: 10-15-minute presentation on what you’ve learned about the model.

Date: Roughly end of Week 12 (exact date determined by lab presentation cycle— the last Friday of the month).

Weight: 15 per cent.

Final Paper

Task: A fully reproducible paper and associated Shiny app or R Package. Toward the mid-term break we will have a meeting to discuss the topic of your final paper. It will be due on the last day of the exam period. This will be marked by me and reviewed by another professor.

Date: Second last day of exam period.

Weight: 40 per cent.