Your studies will be governed by CUC Academic Regulations on Assessment, Progression and Awards.
For courses accredited by professional bodies such as the IET (Institution of Engineering and Technology) there are some derogations from the standard regulations, and these are detailed in your Programme Handbook.
Both cheating and plagiarism are totally unacceptable, and the University maintains a strict policy against them. It is YOUR responsibility to be aware of this policy and to act accordingly.
• Don’t pass off anyone else’s work as your own, including work from “essay banks”. This is plagiarism and is viewed extremely seriously by the University.
• Don’t submit a piece of work in whole or in part that has already been submitted for assessment elsewhere. This is called duplication and, like plagiarism, is viewed extremely seriously by the University.
• Always acknowledge all of the sources that you have used in your coursework assignment or project.
• If you are using the exact words of another person, always put them in quotation marks.
• Check that you know whether the coursework is to be produced individually or whether you can work with others.
• If you are doing group work, be sure about what you are supposed to do on your own.
• Never make up or falsify data to prove your point.
• Never allow others to copy your work.
• Never lend disks, memory sticks or copies of your coursework to any other student in the University; this may lead you being accused of collusion.
By submitting coursework, either physically or electronically, you are confirming that it is your own work (or, in the case of a group submission, that it is the result of joint work undertaken by members of the group that you represent) and that you have read and understand the University’s guidance on plagiarism and cheating.
You should be aware that coursework may be submitted to an electronic detection system in order to help ascertain if any plagiarised material is present. You may check your own work prior to submission using Turnitin at the https://openmoodle.coventry.ac.uk/. If you have queries about what constitutes plagiarism, please speak to your module tutor or the Centre for Academic Success.
It is your responsibility to ensure that work submitted in electronic format can be opened on a faculty computer and to check that any electronic submissions have been successfully uploaded. If it cannot be opened it will not be marked. Any required file formats will be specified in the assignment brief and failure to comply with these submission requirements will result in work not being marked. You must retain a copy of all electronic work you have submitted and re-submit if requested.
1) Compare the different aspects involved in the modern Data Science.
2) Critically evaluate and practice implementing a wide range of algorithms and modern tools used to solve various data science tasks.
3) Apply learned techniques to formulate and solve real-life data-based problems.
4) Communicate technical information in a range of formats appropriate to a specific audience.
Title: Building and evaluating data analysis and machine learning pipelines.
Style: Coursework consisting of a report, dataset and programming scripts.
This coursework is most suited for assessing the learning outcomes of the module providing the practical nature of the Data Science field. The area is growing fast and the interest in data analysis and machine learning solutions constantly increases. Learning to formulate and solving practical and research-oriented data-driven projects will ensure your continuing employability through development of analytical soft skills.
You are required to find a dataset, formulate a problem you want to address with the dataset (e.g. predict whether a mushroom is poisonous or not based on its characteristics), build and evaluate at least two machine learning models that would address the problem, and draw conclusions and recommendations based on your findings. The submission should include your report, dataset (plus any number of sets representing pre-processing stages if needed) and Python scripts with comments, all included in one zip-file. Your work should be original and produced by you. Copying whole tutorials, scripts or images from other sources is not allowed. Any material you borrow from other sources to build on should be clearly referenced (use comments to reference in Python scripts); otherwise, it will be treated as plagiarism, which may lead to investigation and subsequent action.
1. Cover page with title of your project; module code, title, coordinator name; your name and student number; date.
2. Abstract
3. Introduction, background, aim and objectives
4. Dataset(s) description (can be supported with figures and references to Python code)
5. Problem to be addressed (justified and supported with references to literature)
6. Machine learning model N (iterate for each model/algorithm)
1. Summary of the approach (justified and supported with references)
2. Data pre-processing, visualisation, feature selection (with references to Python code)
3. Model training, evaluation and testing (with references to Python code)
4. Results and discussion (supported with tables, figures and references to Python code)
7. Results comparison across the models built (supported with tables, figures and Python code)
8. Conclusion, recommendations and future work
9. References
For advice on writing style, referencing and academic skills, please make use of the Centre for Academic Success:
Recommended length of the report is 3,000 words excluding figures and tables. A typical student would be expected to spend a minimum of 40 hours working on the coursework to pass this assignment.
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