Module overview
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Efficiently manipulate and wrangle data using the Tidyverse and the dplyr package
- Write interactive apps for exploring data using the Shiny package
- Understand the importance of data visualisation and be able to produce sophisticated graphics using the ggplot package
- Understand the importance of ethics in data science and be able to produce reproducible work using knitr and RMarkdown.
Syllabus
-What is data science?
-Data manipulation and wrangling using the Tidyverse and dplyr R package.
-Data visualisation using the ggplot R package.
-Interactive data exploration using Rshiny.
-Data and ethics including repeatability, replicability and reproducibility, making use of knitr and Rmarkdown.
-Towards big data and an introduction to distributed computing.
Learning and Teaching
Teaching and learning methods
12 Lectures
24 Computer labs
| Type | Hours |
|---|---|
| Independent Study | 114 |
| Teaching | 36 |
| Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |