2022/23 Undergraduate Module Catalogue
CHEM3212 Big Data, Big Science
10 creditsClass Size: 150
Module manager: Dr Stuart Warriner
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2022/23
This module is not approved as a discovery module
Module summaryThe explosion of information means that many jobs often require people to handle large datasets efficiently and quickly, yet graduates often don’t have these core skills. In science new insights often involve taking lots of data and bringing it together in a way that illuminates the problem. In this course you will develop the core skills to efficiently handle large datasets. Using examples from across Chemistry you will see how to efficiently extract data using simple programming in python and reach meaningful conclusions. Online tools will help you acquire key skills while weekly seminars will let you explore real examples, enabling you use these skills to answer scientific questions.
ObjectivesTo enable students to explore how to handle large datasets to extract key scientific information.
Understand how large datasets can be useful within and outside science.
The ability to use simple python programming to extract data from large datasets.
Presentation of data efficiently and concisely.
Aggregating multisheet data using indirect functions
Fundamental python programming concepts
Pattern matching and data mining using python
|Delivery type||Number||Length hours||Student hours|
|Independent online learning hours||30.00|
|Private study hours||70.00|
|Total Contact hours||30.00|
|Total hours (100hr per 10 credits)||130.00|
Private studyLectures are to introduce the course and the assessment only
Online courses and examples to enable development of the technical skills for data analysis – eg basics of python programming.
Self study with self-taught examples and tests. These tools will then support the exercises in the workshops
Opportunities for Formative FeedbackThe workshop sessions will involve guided solutions to the project with a member of staff enabling feedback on the approach being taken and any technical issues.
The online learning will have self help exercises to enable the students to monitor their own progress.
Methods of assessment
|Assessment type||Notes||% of formal assessment|
|Total percentage (Assessment Coursework)||100.00|
The project will include a real data analysis exercise framed around a scientific question. The students will have to understand the data provided, determine what data is relevant and extract it using the skills they have obtained and then present the data as a short report. Students requiring to resit the module would be given a further attempt to complete the project over the summer.
Reading listThere is no reading list for this module
Last updated: 07/07/2022 11:10:17
Browse Other Catalogues
- Undergraduate module catalogue
- Taught Postgraduate module catalogue
- Undergraduate programme catalogue
- Taught Postgraduate programme catalogue
Errors, omissions, failed links etc should be notified to the Catalogue Team.PROD