| Title: | Processing and Visualization |
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| Long Title: | Data Processing and Visualization |
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| Field of Study: |
Computer Science
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| Valid From: |
Semester 1 - 2021/22 ( September 2021 ) |
| Module Coordinator: |
Brigid Lucey |
| Module Author: |
Haithem Afli |
| Module Description: |
The complexity of biological problems requires the understanding of networks and interactions of chemical components, as well as the analysis of relations such as gene regulation, metabolic pathways, variance, co-variance etc. As a consequence, this knowledge frequently relies on data visualisation. In this module, the learner will investigate a variety of data processing techniques and visualisation concepts. More advanced visualisation methods and tools for analysing multi dimensional data, large data sets and geospatial data will also be examined and appraised. The learner will also research and critique some of the major current challenges within biological data processing and visualisation. |
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| LO1 |
Investigate programming techniques to clean, transform and query data. |
| LO2 |
Integrate standard programming libraries and their associated functionality to perform analysis of datasets and solve data-driven problems. |
| LO3 |
Develop appropriate data visualisation techniques to solve biological data analysis problems. |
| LO4 |
Assess patterns and knowledge discovered as a result of developing data visualisation techniques to a variety of biological data analysis problems. |
| Pre-requisite learning |
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list. |
| No incompatible modules listed |
Co-requisite Modules
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| No Co-requisite modules listed |
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
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| No requirements listed |
Module Content & Assessment
| Indicative Content |
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Data Array Manipulation
Overview of standard programming libraries for numerical computation. Creating multi-dimensional arrays. Performing operations such as indexing, slicing, boolean indexing, fancy indexing, building queries, transposing and applying conditional logic to arrays.
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Data visualisation pre-processing techniques
Learn data cleaning techniques relevant to data visualisation - data aggregation, data sampling, impute missing data, find inconsistencies. Learn transformation techniques - data normalisation, construct new variables, Investigate how to use regular expressions and data manipulation techniques to pre-process data sets.
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Advanced visualisation techniques
Investigate python libraries for visualisation and their features - interactivity, geospatial methods, hierarchical and networks solutions.
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Data processing and visualization libraries
Use Python libraries for data processing and visualization e.g. NumPy, SciPy, Matplotlib, Seaborn, Pandas and Plotly.
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| Assessment Breakdown | % |
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| Course Work | 100.00% |
| Course Work |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Project |
Design and implement programs that apply a range of programming concepts and libraries to solve data-driven problems. |
1,2 |
50.0 |
Week 6 |
| Project |
Evaluate and implement a visualisation technique to solve a problem; research, critique and communicate the biological data analysis topic. |
3,4 |
50.0 |
Week 13 |
| No End of Module Formal Examination |
| Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
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The institute reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Delivers the concepts and theories underpinning the learning outcomes. |
2.0 |
Every Week |
2.00 |
| Lab |
Application of learning to case studies and project work. |
2.0 |
Every Week |
2.00 |
| Independent Learning |
Student reads recommended papers and practices implementation. |
3.0 |
Every Week |
3.00 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
| Workload: Part Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Delivers the concepts and theories underpinning the learning outcomes. |
2.0 |
Every Week |
2.00 |
| Lab |
Application of learning to case studies and project work. |
2.0 |
Every Week |
2.00 |
| Independent Learning |
Student reads recommended papers and practices implementation. |
3.0 |
Every Week |
3.00 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
Module Resources
| Recommended Book Resources |
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- Martin Jones 2020, Biological data exploration with Python, pandas and seaborn: Clean, filter, reshape and visualize complex biological datasets using the scientific Python [ISBN: 9798612757238]
- Yasha Hasija and Rajkumar Chakraborty, Hands on Data Science for Biologists Using Python, 2021 Ed., Taylor & Francis Ltd [ISBN: 0367546795]
| | Recommended Article/Paper Resources |
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- Tallat et. al 2019, Visualization and Analytics of Biological Data by Using Different Tools and Techniques, 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST)
- Raiha Tallat, Rana M. Amir Latif, Ghazanfar Ali, Ahmad Nawaz Zaheer, Muhammad Farhan and Syed Umair Aslam Shah 2020, Jarvis: A Multimodal Visualization Tool for Bioinformatic Data
| | Other Resources |
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- Website: Visualizing Biological Data in Python/v3
- Website: DATA VISUALIZATION IDEAS AND LIBRARIES
FOR BIOINFORMATICS
- A source-code repository: Plotly BioVisualization with Python
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Module Delivered in
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