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CETM47: Machine Learning, Data Mining, and Data Analytics Tools
IMPORTANT INFORMATION You are required to submit your work within the bounds of the University Infringement of Assessment Regulations (see you’re Programmed Guide). Plagiarism, paraphrasing and downloading large amounts of information from external sources, will not be tolerated and will be dealt with severely. Although you should make full use of any source material, which would normally be an occasional sentence and/or paragraph (referenced) followed by your own critical analysis/evaluation. You will receive no marks for work that is not your own. Your work may be subject to checks for originality which can include the use of an electronic plagiarism detection service.
Where you are asked to submit an individual piece of work, the work must be entirely your own. The safety of your assessments is your responsibility. You must not permit another student access to your work. Where referencing is required, unless otherwise stated, the Harvard referencing system must be used (see your Programmed Guide).
The assignment is based on individual work by each student. (CETM47)
The module will be assessed by two coursework assignments. CETM47 Assignment Two covers learning outcomes 2, 3, 4, 5 and 6 of the module. There will be a report containing both research and practical component where students will be required to research and present their critical evaluation of a specific area of Data Analytics tools/techniques for a practical application. The submission requirements consist of a report of 3000 to 5000 words maximum plus appendices containing, e.g. references, screenshots, etc.
You need to choose a practical problem and/or research problem. You will write a report that addresses your chosen problem.
You are free to organize it in whatever way best explains your work.
CETM47 Subjects List
Learning Outcomes :(CETM47)
A critical understanding of trends, tools, and current developments in the areas of Machine Learning, Data Mining, and Data Analytics.
A critical understanding of Machine Learning, Data Mining, and Data Analytics tools.
Understanding of the professional, ethical, social and legal considerations involved in Data Mining and Data Analytics.
To critically assess, choose and apply the appropriate Machine Learning, Data Mining, and Data Analytics formalism and tools to practical problems.
To identify and assess data for the use of Data Mining and Data Analytics tools.
To define, explain and interpret the results obtained from the practical application of Machine Learning, Data Mining, and Data Analytics tools.
You are expected to hand in: (CETM47)
A Report should contain the following:
Description of the problem.
Critical evaluation and selection of the current problem relevant Machine Learning / Data Analytics tools.
Data collection. References to data sources. Problems with data.
Critical considerations of professional, ethical, social and legal issues
Presentation of a formal statement of the given problem/task.
Application of the proposed Machine Learning / Data Analytics tools to the chosen problem.
Assessment of the proposed Machine Learning / Data Analytics technique(s) performance. Use of performance measures.
Critical evaluation of application and results of the chosen technique (e.g. compare/measure of model performance, etc.)
Description and explanation of support which is needed for use of the proposed Machine Learning / Data Analytics tools.
Discussion of the level of success achieved and any enhancements which would improve the work.
You could use appendixes to introduce details of relevant work, screenshots, copies of papers used for critical evaluation and collected data. Excluding the appendixes, title page and list of references, assignment work should be 3000 words maximum.
There are a total of 100 possible marks in this assignment. Your work shall be graded for originality as well as for accuracy.
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