Systems and Tools for Data Science

Systems and Tools for Data Science
Systems and Tools for Data Science
RESIT Coursework – ePortfolio
Coursework Outline
The Systems and Tools for Data Science module explores popular software tools and
platforms for supporting various data science projects. The module will be delivered
via a series of seminars to showcase the current data science practice and provide
opportunities of practicing various data science tasks using relevant systems and tools.
Each seminar typically covers one system (hardware and/or software), explores its
applications in data science and provide hands-on practical examples on how the
system can be utilised to solve real-life problems.
The module is fully assessed by a submission of a single ePortfolio that summarises
the seminars supported by your own reflection on the systems and tools by analysing
their pros and cons and comparing to similar tools in data science.
Coursework Details
You need to submit a single document (preferably a PDF file) via Moodle covering
the following workshops/seminars under this module:
 Tableau (session 1)
 TigerGraph (session 4)
 ML (session 5)
 Zizo software (session 6)
You can access the workshops’ recordings via MS Team (EDU – Systems and Tools
for Data Science (SPFSTDS21T3)).
The coverage of each workshop should include the following sections:
1. A general overview of the workshop (around half a page)
2. A description of the system(s)/tool(s) demonstrated in the workshop (around
one page).
Systems and Tools for Data Science
3. A documentation of the practical tasks/functionalities covered in the workshop
(if there is a practical) including some screenshots to support your explanation
(max of 4 pages including screenshots). If you were unable to run the practical
tasks yourself, screenshots from the session recording will be acceptable.
Please note that providing screenshots without proper explanation is not usually
accepted.
4. A discussion section in which you need to analyse the pros and cons of the tools
covered in the workshop and briefly compare them to other similar tools in data
science. You may also highlight their relevance to your work, if applicable, or to
your project (around one page).
Submission Deadlines:
18th April 2022 at 10am via Moodle Resit submission point.
No extension on this coursework can be given except for approved mitigating
circumstances.
This coursework will account for 100% of your final mark.
Please note that ALL submissions will automatically go through a Turnitin check
against online sources and all similarities will be flagged. When using any sources,
make sure that you apply a proper paraphrasing and referencing.
If you have any question, please contact the module leader, Dr Maysson Ibrahim via
MS Teams or email [email protected]
Marking Matrix for this coursework

Work Aspects
Distinction ≥ 70%	Merit ≥ 60%	Pass ≥ 50%	Fail
Accuracy	Precise and correct terms used	Sufficiently precise. Most terms used correctly	Reasonably accurate in context but not in words	Severe lack of precision and misunderstanding

AssignmentTutorOnline

Systems and Tools for Data Science

Validity	Argument consistent and logical. Show strong critical reasoning	Good logical argument. Show limited critical thinking & reasoning	Sufficiently valid argument, but may not with proper reasoning	Little valid argument. Opinionated decisions
Completeness	All required elements covered	Majority elements covered	Sufficient elements covered	Severely incomplete work
Objectivity	Factual not opinionated	Mainly factual	Limited or not well argued	No objectivity
Clarity and Professionalism	Statements clearly made, diagrams, figures and references professionally presented	Statements easy to follow, but may not be carefully built. reasonable use of figure reference, diagrams	Sufficiently clear to follow. Use of figures, diagrams and references is present	Severely lack of clarity. Extremely limited in content. No sign of professional “look and feel”.