MEIN40290 Data Mining for life sciences

Academic Year 2021/2022

Descriptive analytics, predictive analytics, and prescriptive analytics are crucial application areas for life sciences and healthcare. The objective of this module is for students to develop an in-depth understanding of data mining principles and techniques used for gleaning new insights given biological data from various sources.

The module will cover key concepts in data mining including information extraction and information indexing with practical examples of popular techniques and algorithms covering feature engineering, clustering, classification and prediction of complex unstructured data synonymous with biological datasets. The module also includes introduction to statistical modelling and a step-by-step guide of data mining practices with real world examples. Emphasis on reusability and reproducibility will be placed throughout the module.

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Curricular information is subject to change

Learning Outcomes:

At the end of the module, students will be able to implement the complete data mining life cycle focusing on thematic applications in life science. Students will be able to perform exploratory data analysis on big data, perform feature engineering, identify and implement model (perform training and evaluation) and deployment of the model.

Indicative Module Content:

Covering fundamental concepts of data mining including databases / data sources, various state-of-the-art techniques and applications as well as relevant statistical methods. Practical issues such as handling noisy and incomplete data and integration of various data sources to uncover new knowledge will be presented using real-world datasets running on industry adopted platforms.

Demonstration of real applications will enable students to employ appropriate data mining process to uncover meaningful discoveries / understanding using a wide array of heterogeneous data sources.

Student Effort Hours: 
Student Effort Type Hours
Lectures

24

Specified Learning Activities

50

Autonomous Student Learning

50

Total

124

Approaches to Teaching and Learning:
Lectures
Active/task-based learning;
Peer and group work;
Reflective learning;
Enquiry & problem-based learning; 
Requirements, Exclusions and Recommendations
Learning Requirements:

Some prior experience with programming in Python and working with the object-oriented programming paradigm.

Learning Recommendations:


Completion of MEIN40310 - Python for the Life Sciences


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Project: A data mining project with a medium to large unstructured biological dataset. Varies over the Trimester n/a Standard conversion grade scale 40% Yes

100


Carry forward of passed components
Yes
 
Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
     
Lecture Offering 1 Week(s) - 19, 20, 21, 22, 23, 24, 25, 28, 29, 31 Mon 13:00 - 14:50
Tutorial Offering 1 Week(s) - 19, 20, 21, 22, 23, 24, 25, 28, 29, 31 Thurs 10:00 - 10:50
Laboratory Offering 1 Week(s) - 19, 20, 21, 22, 23, 24, 25, 28, 29, 31 Wed 11:00 - 12:50
Spring