University of Sunderland
School of Computer Science
CETM46 – Data Science Product Development
2019/20 Hong Kong
Assignment 1 of 2 – 50% of the summative value of the module
Extract from module descriptor – The module will be assessed by two coursework assignments which will cover related learning outcomes of the module respectively. In this first assignment (1 of 2), students will undertake a state of art research paper literature review, considering the key aspects of data science product design and development.
Assignment 1 of 2: A Literature Review Report
The following learning outcomes will be assessed:
Knowledge
• Critical understanding of the use of data repositories for storage of data sets for decision making in organisations
• Critical understanding of modern data science systems and their ecosphere
Skills
• Critical analysis, selection and evaluation of data science methodologies and software tools onto a broad range of datasets and data analysis applications.
The detail in the learning outcomes should be reflected in the deliverable for the assignment.
Important Information
You are required to submit your work within the bounds of the University Infringement of Assessment Regulations (see your Programme 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 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.
Submission Date and Time
Deliverable by Friday 17-01-2020 by 23.59PM
Submission Location
Digital copy via Canvas
Assignment Specification
A literature review report of the state of art research papers, considering the key aspects of data science product design and development (i.e., real-world data problem requirements analysis and specification, organisational decision making policy, related available data repositories and datasets, software engineering methodology, machine learning models, software development tools/platforms) in one of the following application domains that you are interested in:
• Organisations in Public Sectors
• Education Institutions
• Entertainment Industry
• SMEs
• Or a domain related your own work experience
Deliverable:
A literature review report with max word limit 3000 words excluding references. For any student exceeding the limit the Guidance for Students on the Penalty for Exceeding the Limit for Assessed Work shall apply.
Exceeds limit by up to 10%
No penalty
Exceeds limit by up to 20%
-5 percentage points
Exceeds limit by up to 30%
-10 percentage points
Exceeds limit by up to 40%
-15 percentage points
Exceeds limit by up to 50%
-20 percentage points
Exceeds limit by more than 50%
Mark of zero
Marking Scheme
Content:- the quality of content that describes key aspects of Data Science product design and development in the application domain you are interested in. 20%
Critical Evaluation: – the quality of your critical evaluation of state of art with the literature evidence presented 25%
Reasoned Argument: -the quality of your reasoned argument and conclusions 25%
Citations: – how well the materials from the literature are cited in the body paper 10%
Literature Search: – the range and quality of the reference list (15-20 papers) 10%
Presentation:- the quality of formatting and structuring of the literature review 10%
0-40Marks (Fail)
41-69Marks (Pass-Good)
70-100Marks (Excellent)
Content
(20%)
Low or no discussion of the key aspects of domain specific Data Science product design and development
(See Assignment Specification Section)
Good discussion of the key aspects of domain specific Data Science product design and development
(See Assignment Specification Section)
Detailed and specific discussion of the key aspects of domain specific Data Science product design and development
(See Assignment Specification Section)
Critical Evaluation
(25%)
Low or no discussion of the key methodology limitations of domain specific Data Science product design and development
Good discussion of the key methodology limitations of domain specific Data Science product design and development
Detailed and specific discussion of the key methodology limitations of domain specific Data Science product design and development
Reasoned Argument
(25%)
Low or no discussion of your own argument of the review result
Good discussion of your own argument of the review result
Detailed and specific discussion of your own argument of the review result
Citations
(10%)
Low or no use of citation in correct format
Good use of citation in correct format
All use of citation in correct format
Literature Search
(10%)
Low or no use of references in correct format
Good use of references in correct format.
All use of references in correct format
Presentation
(10%)
Low or no formatting and structuring of the report
Good formatting and structuring of the report
Complete formatting and structuring of the report