This module is not currently running in 2026 to 2027.
10. A synopsis of the curriculum
The module gives an advanced introduction into data analytics to provide the skills needed to become more independent in research. It will describe how to apply statistical analysis to scientific data, including hypothesis testing, distribution, and probability, and how to represent data effectively in graph format. It will cover a range of basic tools for data manipulation, including those needed to visualise, sort, count, and re-format data, and for finding clusters in data. It will also cover how to tackle problematic data, such as errors and consistency, and regression models to predict new data values. It will also discuss the ethics and risks associated with sharing data.
Blended distance learning:
Contact Hours: 100 hours
Private Study Time: 50 hours
Total Learning Time: 150 hours
Coursework assignments
Weighting:
Essay Assignment 50% - 1000 words
Portfolio 50% - composed of 5 individual assignments where topics are applied to the workplace
The pass mark for each individual assessment is 40%. All assessments must be passed in order to pass the module
Song, P. (2007) Correlated Data Analysis: Modelling, Analytics, and Applications. Springer.
Satyanarayana, C. (2019) Computational Intelligence and Big Data Analytics in Bioinformatics. Springer.
Wong, C. (2016) Big Data Analytics in Genomics. Springer.
See the library reading list for this module (Medway)
The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
Apply statistical analysis to scientific data.
Demonstrate a clear understanding of the basic tools for data manipulation.
Critically evaluate problems found in scientific data.
Predict new data values via regression models.
Critically discuss clustering in data.
Critically evaluate the ethics and risks of sharing data.
The intended generic learning outcomes.
On successfully completing the module students will be able to:
Develop and demonstrate an ability to work and communicate effectively with others.
Analyse, evaluate and correctly interpret data.
Present and communicate data effectively.
Obtain and use information from a variety of sources as part of self-directed learning.
Manage their time and use their organisation skills within the context of self-directed learning.
University of Kent makes every effort to ensure that module information is accurate for the relevant academic session and to provide educational services as described. However, courses, services and other matters may be subject to change. Please read our full disclaimer.