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Curricular information is subject to change
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.
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 Type||Hours|
|Specified Learning Activities||
|Autonomous Student Learning||
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
|Resit In||Terminal Exam|
• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
|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|