2024/25 Undergraduate Module Catalogue
FOOD1061 Understanding Data
10 creditsClass Size: 120
Module manager: Valentine Nlebedim
Email: V.U.Nlebedim@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
This module is not approved as a discovery module
Module summary
The module will introduce you to basic data analysis methods and statistical analysis packages for the Food and Nutritional Sciences. Depending on your discipline, you will explore some specialist software, e.g., Nutritics for Nutrition. The module will also engage you in a practical dietary assessment exercise, where you will be expected to apply your analysis skills.Objectives
The module aims:To develop graduates who understand basic statistical analysis package for food science and nutrition data.
To provide practical problem-solving experience in using specialist nutrition software to collect and analyse dietary data, e.g. myfood24 for data collection of dietary information.
Learning outcomes
On completion of this module students are expected to:
1. Identify and explain basic statistical concepts, measures, models and their interpretation when analysing data
2. Become familiar with the procedures for collecting and defining appropriate data appropriately.
3. Critically reflect and select appropriate statistical methods for data analysis.
4. Present data in appropriate graphical and tabular formats.
Skills Learning Outcomes:
5. The skill to employ technology in a suitable and ethical manner to improve productivity, recognizing the suitable technologies for various tasks.
6. Know how to access, learn and adapt to new technologies. Can range from basic IT skills (spreadsheets, presentations) to programming and coding skill .
7. The competence to take a logical approach to solving problems; resolving issues by tackling from different angles, using both analytical and creative skills. The expertise to understand, interpret, analyse and manipulate numerical data.
8. Skills in numeracy and literacy, including written communications. Being capable to understand, interpret, analyse and manipulate numerical data.
Syllabus
Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| Lectures | 11 | 1.00 | 11.00 |
| Practicals | 5 | 2.00 | 10.00 |
| Private study hours | 79.00 | ||
| Total Contact hours | 21.00 | ||
| Total hours (100hr per 10 credits) | 100.00 | ||
Opportunities for Formative Feedback
Formative feedback in an MCQ and in-person discussions during practical sessions.Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Assignment | Coursework | 100.00 |
| Total percentage (Assessment Coursework) | 100.00 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Reading list
The reading list is available from the Library websiteLast updated: 12/07/2024 11:19:03
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