BS709-7-SP-CO:
Data Analysis and Visualisation

The details
2023/24
Life Sciences (School of)
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 15 January 2024
Friday 22 March 2024
15
16 February 2024

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

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)

Module description

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.

Module aims

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.

Module learning outcomes

By the end of this module, students will be expected to be able to:



  1. Formulate appropriate research questions, and critically evaluate and test scientific and statistical hypotheses.

  2. Design an appropriate experimental programme to test hypotheses including evaluating the appropriate statistical approaches to apply.

  3. Demonstrate systematic understanding of the specialised techniques and approaches involved in collecting, analysing and communicating research findings to a variety of audiences.

  4. Demonstrate a comprehensive understanding of the R language and environment for statistical computing and graphics.

  5. Show competence in the use of ArcGIS to visualise geo-referenced datasets.

Module information

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 ArcGIS for the analysis and mapping of geo-referenced data.


Sessions



  • Introduction to R and statistical computing.

  • Descriptive statistics and data handling.

  • Univariate statistics and data visualisation.

  • Basic multivariate statistics.

  • Advanced multivariate statistics.

  • Introduction to ecological bioinformatics.

  • Analysis of Next Generation Sequencing data.

  • Integrating eco- and bio- informatics.

  • Introduction to ArcGIS.

  • Advanced ArcGIS.

Learning and teaching methods

This module will be delivered via:

  • One 3-hour PC Lab session per week.

Each session will comprise approximate 1-hour of delivery on data analysis theory, with 2-hours of implementation of analyses on the PCs.

Teaching will be integrated between these two components as opposed to running them concurrently. For example, the students will learn an analysis approach and the theory behind it, then implement it for themselves, before moving to the next approach.

Bibliography

The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Data Visualisation Component    15% 
Coursework   ArcGIS Engagement    10% 
Coursework   Statistics Worksheet  09/02/2024  40% 
Coursework   Moodle Informatics Quiz - Deadline Only - Do Not Submit on FASER  20/02/2024  15% 
Coursework   ArcGIS Worksheet  08/03/2024  20% 

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

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Prof Alex Dumbrell, email: adumb@essex.ac.uk.
Prof Alex Dumbrell, Dr Martin Wilkes
School Graduate Office, email: bsgradtaught (Non essex users should add @essex.ac.uk to create a full email address)

 

Availability
Yes
No
Yes

External examiner

Dr Sebastian Hennige
Resources
Available via Moodle
Of 30 hours, 21 (70%) hours available to students:
9 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.

 

Further information
Life Sciences (School of)

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