BS709-7-AP-CO:
Data Analysis and Visualisation
2024/25
Life Sciences (School of)
Colchester Campus
Autumn & Spring
Postgraduate: Level 7
Current
Thursday 03 October 2024
Friday 21 March 2025
15
29 July 2024
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC C16112 Tropical Marine Biology,
MSC F71012 Marine Science and Sustainable Development,
MPHDC16148 Tropical Marine Biology,
MPHDC16184 Tropical Marine Biology,
PHD C16148 Tropical Marine Biology,
PHD C16184 Tropical Marine Biology,
MSCIB097 Tropical Marine Biology,
MSCIBA97 Tropical Marine Biology (Including Placement Year),
MSCIBB97 Tropical Marine Biology (Including Year Abroad)
This module provides you with the opportunity to improve your data analysis and visualisation skills.
The ability to critically analyse raw data, and to communicate findings from these analyses in appropriate formats in a multidisciplinary research environment, provides the foundation for your successful career in environmental sciences.
The aim of this module is:
- To equip you with an array of numeracy skills including the confident application of methods for handling, analysing, interpreting and visualising large datasets.
By the end of this module, students will be expected to be able to:
- Demonstrate a comprehensive understanding of the R language and environment for statistical computing and graphics.
- Demonstrate systematic understanding of the specialised techniques and approaches involved in collecting, analysing and communicating research findings to a variety of audiences.
- Formulate appropriate research questions, and critically evaluate and test scientific hypotheses.
- Design an appropriate experimental programme to test hypotheses including evaluating the appropriate statistical approaches to apply.
- Show competence in the use of spatial tools to analyse and visualise geo-referenced datasets.
- Effectively visualise and interpret the results of statistical analyses.
Students will learn to determine the appropriate analytical requirements for different data types, how to group raw and transformed data, and statistically test scientific hypotheses. You will become familiar with using R, the language and environment for statistical computing and graphics outputs, and gain skills in using R as a geographic information system for the analysis and mapping of geo-referenced data.
Sessions
- Introduction to programming
- The R environment
- Descriptive statistics
- Data visualisation
- Univariate statistics
- Basic multivariate statistics
- Advanced multivariate statistics
- Ecological bioinformatics
- Introduction to spatial analysis
- Cartography in R
- Species distribution modelling
This module will be delivered via:
- Two “boot camps”, one at the start of the autumn term and one at the start of the spring term. This will ensure that students have developed the skills necessary to analyse and visualise data for other modules they are taking in the respective terms. Each boot camp will consist of six sessions across three consecutive days. Each session will begin with a short lecture to introduce the topic and associated theory, followed by a worksheet.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Moodle Quiz 1 |
|
10% |
| Coursework |
Moodle Quiz 2 |
|
10% |
| Coursework |
Data Science worksheet |
25/10/2024 |
40% |
| Coursework |
Spacial Analysis worksheet |
07/03/2025 |
40% |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Martin Wilkes, email: m.wilkes@essex.ac.uk.
Dr Martin Wilkes
School Graduate Office, email: bsgradtaught (Non essex users should add @essex.ac.uk to create a full email address)
Yes
No
Yes
Dr Sebastian Hennige
Available via Moodle
Of 13 hours, 3 (23.1%) hours available to students:
5 hours not recorded due to service coverage or fault;
5 hours not recorded due to opt-out by lecturer(s), module, or event type.
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