Data Science Project Assignment Brief: A Practical Approach to Solving Real-World Problems Using Machine Learning and Data Analysis Techniques
Assignment Brief
Data Science is continuously thriving as a great career option for this generation. It is among the most promising & happening choices altogether. So, suppose you are an aspiring data scientist and want to practice skills to become an efficient professional in this field. In that case, you are advised that after grabbing some excellent theoretical knowledge of Data Science, now is the time to do some practical projects. You must do some technical & real-time data science project so that it helps you boost your knowledge, technical skills, and overall confidence.
Task
As a data scientist, you have been tasked to model trends and manage data. You can choose any problem domain of your choice.
Once you have selected your topic, YOU MUST CONFIRM THIS WITH YOUR TUTOR. You will not be allowed to proceed with your case of choice without tutor agreement. You may not change your topic without further consultation with your tutor.
You need to carry on the data science procedure and write a report at the end. The following components highlight the most general architecture of a Data Science project, and hence your report should include:
Problem Statement
This is the fundamental component on which the project is based. It should define the problem that your model will solve and discuss the approach that your project will follow.
Dataset
This is a crucial component of your project and should be chosen carefully. Only large enough datasets from trusted sources should be used for the project.
Algorithm
This includes the algorithm you use to analyze your data and predict the results.
Training Models
This involves training your model against various inputs and predicting the output. This component decides the accuracy of your project. Note that using proper training techniques can produce better outcomes.
Conclusion
Write your observations and the trends found in the data. Also, predict the future trends, which can help in better decision making in your problem domain.
Your report should contain all the original figures and necessary references to authoritative sources to back up your claims and assertions. It should be written as a formal, technical report and contain a brief review of relevant literature.
Guidelines
Your submission should be 3000 words in length (+/- 10%). The following points should be kept in mind while working on your project and compiling the report for submission:
- Choose the programming language that you are comfortable with. However, the language chosen should be one of the in-demand languages such as Python, R, and Scala.
- Use datasets from trusted sources. You can use Kaggle datasets. Moreover, ensure that the dataset you are using does not contain errors.
- Find errors or outliers in your dataset and rectify them before training your model.
- You can use visualisation tools to find the errors in your dataset.
- Your algorithm to model your data and reason to select that particular algorithm for modelling purposes.
- Please make sure that you correctly cite and reference all secondary sources and include a reference list. The reference list will not be included in your final word count.
- Note that you can only submit either a PDF or a Microsoft Word document.
Grading
This activity will be graded, and you will receive feedback on it.
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Marking Rubric
|
DISTINCTION |
COMMENDATION |
PASS |
FAIL |
| Outstanding: 95
Excellent 85, Very good: 75, 77 |
Good: 62, 65, 68 | Clear Pass: 52, 55, 58 | Marginal Fail: 48, 45, 42
Clear Fail: 38, 35, 32, 25 Little or Nothing of Merit: 10, 0 |
| The assessed work will demonstrate: | The assessed work will demonstrate: | The assessed work will demonstrate: | Work of insufficient quality to achieve a Pass standard. ‘Fail’ grade work may suffer from some or all the following issues: |
| · An in-depth and systematic conceptual understanding of the topic
· All the elements of the question set have been addressed · Independent thought, rather than relying simply on the ideas of others, perhaps as contained in the prescribed reading of the module · Reading beyond that specified by the module author, reflecting a broad literature review, where appropriate · Reading with a critical understanding · Ability to evaluate theoretical concepts and draw appropriate conclusions · Where appropriate, reflective and critical application/integration of theoretical concepts to empirical and practical issues · An ability to analyse and synthesise information presenting material using structured, logical discussion and critical argument · Clarity and conciseness, well-constructed and articulate writing that will be presented to a high standard using academic/professional language · Harvard Referencing |
· A good conceptual understanding of the topic
· That all the key elements of the question set will have been addressed · Some evidence of independent thought rather than relying simply on the ideas of others, perhaps as contained in the prescribed reading of the module · Evidence of reading beyond that specified by the module guide. · Evidence of a good grasp of prescribed literature · An ability to evaluate theoretical concepts and draw appropriate conclusions · An ability to apply theoretical concepts to empirical and practical issues · Clarity and conciseness, well-constructed and articulate writing that will be presented to a high standard using academic/ professional language · Accurate and consistent use of the appropriate referencing system · Clear and concise written expression · Reference complete and consistent with Harvard guidelines |
· Satisfactory conceptual understanding of the topic
· That some of the key elements of the question set will have been addressed. · Some evidence of independent thought rather than relying simply on the ideas of others, perhaps as contained in the prescribed reading of the module · Some familiarity with, and satisfactory understanding of, prescribed literature · Some evidence of application of theoretical concepts to empirical and practical issues, and integrating theory and practice · PASS grade work may suffer from some or all the following issues: · Inclusion of some materials not directly relevant to the set question · Providing an incomplete answer to the set question · Weaknesses in structure or in clarity · Some errors in referencing in terms of the use of the appropriate referencing system and/or consistency |
· Achievement of very few/none of the module learning outcomes
· Completely fails to address the question set (e.g., student has written at length about the wrong topic) · Token or no attempt to integrate theory and practice · Little or no understanding of theoretical and empirical considerations · Little or no reference to appropriate literature · Little or no evidence that the student has grasped key ideas · Unduly descriptive and/or lacks analysis · Badly presented · References largely incomplete and not consistent with Harvard guidelines · An assessment offence has been committed · Is submitted in contravention of University submission regulations
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