Program Overview

Data Analysis and Theory in Analytical Chemistry (CHEM0041)

Key Information

• Faculty: Faculty of Mathematical and Physical Sciences
• Teaching department: Chemistry
• Credit value: 15
• Restrictions: Available to chemistry, natural science, BASc and suitably qualified affiliate students only.

Alternative Credit Options

There are no alternative credit options available for this module.

Description

This module aims to develop skills for data processing using descriptive and inferential statistics transformations, identifying and interpreting uncertainty associated with analytical method development. The aim is to develop reputable experts in applied analytical chemistry professions and research who can determine the validity and reliability of data.

Module Aims

1. Understand statistics of data, fundamental descriptive statistics, associated continuous probability density functions and their importance in data processing.
2. Understand data visualisation and models; linear and polynomial responses with homoscedastic uncertainty and heteroscedastic uncertainty.
3. Explain how inferential statistics such as the Student's t test and Analysis of Variance can help to distinguish similarities and differences between data sets.
4. Describe data variance – covariance, Monte Carlo simulation methods for data processing and Bayesian statistics.
5. Understand the data collection and processing requirements for method development and validation in analytical chemistry.
6. Describe the appropriate data processing methods needed to develop and validate a wide range of sample preparation and measurement techniques in analytical chemistry.
7. Understand the importance of data quality in analytical chemistry and the activities undertaken in laboratories to ensure the reliability of measurement results.
8. Discuss the strengths and limitations of measured data and the data processing methods for a wide range of measurement scenarios. Interpret data in relation to experimental design and application.

Teaching and Learning Methods

• Lectures / workshops: The topics are introduced through combined lectures, electronically and the lecture workshops will be held in computer cluster rooms. Working through lecture material is considered an indicator of student engagement and is therefore compulsory.
• Self-study: In addition to timetabled hours it is expected that you engage in self-study in order to master the material. This can take the form of practicing example questions (additional questions, given during the workshops, available on Moodle and past exam questions) and further reading in textbooks and online.

Module Deliveries for 2026/27 Academic Year

• Intended teaching term: Term 1
• Postgraduate (FHEQ Level 7)

Teaching and Assessment

• Mode of study: In person
• Methods of assessment: 100% Coursework (2 assessments)
• Mark scheme: Numeric Marks

Other Information

• Number of students on module in previous year: 15
• Module leader: Dr Victoria Hilborne

Last Updated

This module description was last updated on 10th March 2026.