Why was this course developed?
The Certificate in Statistics and Data Analysis was developed following a Scientific Skills Survey which was conducted by the Wales Ireland Network for Scientific Skills (WINSS) team to address the scientific skills gaps within industries based in Dublin, Meath, Kildare, Waterford, Kilkenny, Wexford, Wicklow, Carlow, South Tipperary, Cork and Kerry. The survey was sent to more than 100 companies in the pharmaceutical/biotechnology, medical device and food sectors. Transferable skills such as project management, data handling and analysis, statistics and IT skills are in high demand based on the response from 70% of the companies surveyed. Of the transferable skills listed, the skills most commonly required by the companies (84%) required training in the area of data handling and analysis. Recognising this demand, the Certificate in Statistics and Data Analysis was devised to provide advanced level training and address this skills deficit.
This programme aims to develop the learner's competence in the application of modern techniques of data analysis in the context of analytical method development. The learner will be enabled to apply statistical analysis processes to data sets and interpret the results in order to quantify data quality and compare data sets. These data sets will deal with real world applications from the pharmaceutical, biopharmaceutical and food industries. The learner will also develop competence in a range of process control tools and minimisation strategies. Dedicated statistical software packages will be used for the delivery of this programme, features of which will enable the student to perform a comprehensive analysis and assessment of data quality. Continuous improvement and data-driven decisions will also be emphasised throughout the course of the module.
What are the learning outcomes?
On successful completion of this module, a student will be able to:
1.Critically analyse analytical data using appropriate descriptive statistics tests.
2.Critically assess data sets based on applied inter-comparison significance tests.
3.Critically analyse analytical data using statistical software packages.
4.Synthesise and integrate process capability indices as a statistical measure of process capability.
5.Justify the usefulness of Shewhart control charts, CUSUM charts and other control charts to control a process.
6.Critically evaluate sampling plans and statistical analysis of quality in product batches.