Undergraduate Module Descriptor
SSI1007: Introduction to Data Analytics in R or Python
This module descriptor refers to the 2022/3 academic year.
The exact contents of the course depend on the programming language being taught, R and Python courses allow for learning a new software system and the syntax of a language. The ILOs and data sets used in teaching are generic, enabling students to gain skills in the theory and practical aspects of data analytics for application in a broad range of disciplines.
Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover at least some of the following topics:
- Introduction to statistics
- Basic mathematical notation
- Data cleaning
- Variable types
- Summarising data
- Data visualisation
- Relationships between variables
- Hypothesis testing
- Communicating data
- Group project
- Familiarising yourself with the programming environment
- The logic of coding
- Syntax essentials
- Understanding error messages
Assessment will be in the form of either a group project that demonstrates the skills learnt throughout the course by interrogating a research question and presenting the results, or open-book online ELE quizzes. The mode of assessment will be dependent upon the number of students on a course iteration: those with greater than 35 students will use quizzes.
Learning and Teaching
This table provides an overview of how your hours of study for this module are allocated:
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
...and this table provides a more detailed breakdown of the hours allocated to various study activities:
|Category||Hours of study time||Description|
|Scheduled Learning & Teaching activities||8 (R or Python)||Lectures: Overview of statistical theory and demonstration of practical applications|
|Scheduled Learning & Teaching activities||8 (R or Python)||Practicals: Practical activities completed in-class|
|Guided independent study||10 (R or Python)||Revision of the sessions, finishing any practicals, completing suggested reading, practicing code, sourcing data|
This module has online resources available via ELE (the Exeter Learning Environment).