The aim of this module is to develop proficient use of common data analysis applications and specialist statistical such as SPSS to support all other modules and the project work undertaken by students.
This is a foundation course in bio-statistics and relevant data analysis software applications that will introduce the student to the practical use of computers in Biosciences.
Formatting workbooks, use of formulas, functions and absolute cell referencing, creation and formatting of charts. Importing large data sets and preparing (cleaning) data for further analysis using functions and methods.
Manipulating and analysis of data in large bioscience studies data sets using functions, filtering and Microsoft Excel ‘data analysis toolpak’.
Data Visualisation
The use of a specialist data visualisation application such as Tableau and Microsoft Excel.
Understanding the power of good graphical representation and selection of appropriate chart types.
Importing large data sets and preparing (cleaning) data for data exploration and analysis through visualisation.
Databases
The use of databases and their capabilities through the use of Microsoft Excel, Tableau and a specialist statistical applications such as SPSS.
Importing and defining databases, sorting, filtering and querying data in large Bioscience studies data sets.
Bioinformatics
Introducing the primary areas of biological research analysis related to the field of Bioinformatics. Analysis of the biological research material using online resource databases such as the NCBI’s blast.
An introduction to the practical application of machine learning in Bioscience studies through the application of a simple linear regression classifier in a large data set.
Statistics
An introduction to statistical software and its use in biostatistics using a specialist statistical application such as SPSS and Microsoft Excel.
Categorical and Continuous Data, Descriptive statistics, Summary measures of spread and centre and appropriate graphics. Inferential statistics in terms of hypothesis testing and estimation. Measures of association and agreement.
Interpretation and scientific evaluation
Evaluating results from data analysis and discussing these in the context of their validity, their context in terms of the literature and their applicability.
Laboratory teaching in a computing environment (computer lab or online).
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