module

Undergraduate Module Descriptor

SSI1007: Introduction to Data Analytics in R or Python

This module descriptor refers to the 2022/3 academic year.

Module Content

Syllabus Plan

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
16	10

...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

Online Resources

This module has online resources available via ELE (the Exeter Learning Environment).