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BMOL30110

Academic Year 2025/2026

Data Analysis Skills (BMOL30110)

Subject:
Biomolecular & Biomed Science
College:
Science
School:
Biomolecular & Biomed Science
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Assoc Professor Gerard Cagney
Trimester:
Spring
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module introduces students to the broad concepts and skills needed to carry out effective data analysis in science and elsewhere. There is a strong emphasis on data visualization as an entry point to analyzing your data.

Three broad themes:
1. Data Analysis – The general concepts
2. Data Visualization – Principles + practice using the R package ggplot2
3. Topical Examples – Case studies from visiting lecturers who are at the forefront of using data to investigate current scientific questions

The module is introductory and no prior knowledge of programming or statistics is needed. On completion, students should:
- have a general understanding of how data scientists approach analysis problems
- have acquired some basic skills in using R and ggplot2 to carry out data analysis, construct useful visualizations, and an appreciation of what makes an effective figure
- have listened to some experts in the field and considered how basic data analysis concepts can be applied to a wide variety of scientific problems

About this Module

Learning Outcomes:

1. Understand the general properties of data, and the different approaches to analysing data for science
2. Understand the principles & practical use of data visualization
3. Develop basic skills in use of R programming language for simple data analysis & preparation of figures
4. Learn about advanced data analysis applications from leaders in science and industry

Indicative Module Content:

Class 1. Data Analysis I:
Roles, aims, tools of data analysis
The data life cycle
Data types: variables, parameters, constants
Preparing data – data ‘wrangling’; the Tidyverse

Class 2. Data Analysis II:
Exploratory data analysis - Brief introduction to key topics:
Sampling & distributions
Statistical significance & p-values
Quantiles & Q-Q plots
Relationships between variables : correlation & regression
Overview of hypothesis testing
Statistical modelling

Class 3. Data Analysis III:
What is artificial intelligence?
Differences between traditional statistical modelling and machine learning
Supervised learning: classification, regression
Unsupervised learning: clustering, dimension reduction

Class 4. Data Visualization I:
Elements of a plot
Choosing a chart type:
Representing amounts, proportions, frequency, variation, relationships
Aesthetics of plots
The process of data visualization

Class 5. Data Visualization II:
Aims & history of data visualization
Perception & Gestalt laws, encoding with colour
Telling a story
Good & bad figures
Design for reproducibility

Class 6. Data Visualization III
Class Exercises
Students in groups select and present examples of ‘good’ & ‘bad’ data visualization from the scientific literature or general publications, followed by class discussion
Prize for best example

Classes 7, 8, 9 involve presentations by visiting scientists active in cutting edge research

Practical 1. Introduction to the R programming language:
Overview of R & the R Studio IDE
Download & install R & R Studio
The R Studio layout
Command lines, scripts, projects
Objects, functions, packages
Write your first code
Import data, make a simple plot
Finding help
Hints for using R

Practical 2. Data visualization using ggplot2: Making basic plots:
The R Studio IDE
The basic plot() function
The grammar of graphics (data, aesthetics/coordinate systems, geoms)
The ggplot2 package
Make a simple plot
Customize your plot
Saving & exporting plots

Practical 3. Data visualization using ggplot2: Advanced plots
Displaying multiple variables
Using layers in your plots
Facets – divide & conquer
Interactive visualizations with plotly

Practical 4. Data visualization using ggplot2: Working with models
Basic analysis of a biological dataset using a general liner model (GLM)
a) OMICS dataset, or
b) Coronavirus time series

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

60
Lectures

16
Small Group

6
Practical

20
Total

102

Approaches to Teaching and Learning:
Content is delivered through a mixture of:

1. Lectures (1 hour) – 2 sessions per week: Mostly theory content, with some class exercises interweaved

2. Workshops/Practicals (3 hours) – 1 session per week: Supervised sessions where different aspects of basic R programming and ggplot2 skills are introduced and put into practice by students on their own computers in class

3. Additional self-guided exercises are provided to reinforce these skills at home

A comprehensive Lab Manual containing all this content (theory, practise, self-guided) will be provided

Requirements, Exclusions and Recommendations
Learning Recommendations:

No prior knowledge assumed


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Quizzes/Short Exercises: Data Analysis:
Short Comprehension Questions (Take Home). Short data analysis problems based on examples covered in the class. (20%)
 Week 10 Graded No
20
 No
Exam (Take-Home): Data Analysis:
Machine Learning Exercises. Take home exercises focused on unsupervised and supervised machine learning		 Week 10 Graded No
20
 No
Portfolio: Portfolio of class exercises & self-guided study. Each student produces a portfolio of their R visualization work carried out during the module; assessed for clarity & quality Week 10 Graded No
20
 No
Individual Project: Mini-project. Students are provided with a multivariate dataset and suggested broad research questions. They are asked to prepare a short (2 – 4 page) report describing the data structure... Week 10 Graded No
20
 No
Individual Project: Case Study:
Student presents a short (2 – 4- page) report on a science-based investigation or analysis that includes both data analysis & data visualization elements		 Week 10 Graded No
20
 No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Not yet recorded

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25 Mon 13:00 - 13:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25 Wed 13:00 - 13:50
Spring Practical Offering 1 Week(s) - 21, 22, 23, 24 Tues 10:00 - 12:50
Spring Practical Offering 2 Week(s) - 21, 22, 23, 24 Fri 10:00 - 12:50
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