Data Skills

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ASSIGNMENT INSTRUCTIONS

Assessment Portfolio
Assessment code: 011
Academic Year: 2022/2023
Trimester: 2
Module Title: Data Skills Part 2
Module Code: MOD008862
Level: 3
Module Leader: Paul Davidson
Weighting: 50%
Word Limit: 1,500
This excludes bibliography and other items listed in rule
6.83 of the Academic Regulations.
Assessed Learning
Outcomes
1 – 4
Submission
Deadline:
Please refer to the deadline on the VLE

WRITING YOUR ASSIGNMENT:
This assignment must be completed individually.
You must use the ARU Harvard referencing system.
Your work must indicate the number of words you have used. Written assignments must
not exceed the specified maximum number of words. When a written assignment is
marked, the excessive use of words beyond the word limit is reflected in the academic
judgement of the piece of work which results in a lower mark being awarded for the piece
of work (regulation 6.74).
Assignment submissions are to be made anonymously. Do not write your name anywhere on
your work.
Write your student ID number at the top of every page.
Where the assignment comprises more than one task, all tasks must be submitted in a
single document.
You must number all pages.
SUBMITTING YOUR ASSIGNMENT:
In order to achieve full marks, you must submit your work before the deadline. Work that is
submitted late – if your work is submitted on the same day as the deadline by midnight,
your mark will receive a 10% penalty. If you submit your work up to five working days after
the published submission deadline – it will be accepted and marked. However, the

element of the module’s assessment to which the work contributes will be capped with a
maximum mark of 40%.
Work cannot be submitted if the period of 5 working days after the deadline has passed
(unless there is an approved extension). Failure to submit within the relevant period will
mean that you have failed the assessment.
Requests for short-term extensions will only be considered in the case of illness or other
cause considered valid by the Director of Studies Team. Please contact
[email protected]. A request must normally be received and agreed by the Director of
Studies Team in writing at least 24 hours prior to the deadline. See rules 6.64-6.73:
http://web.anglia.ac.uk/anet/academic/public/academic_regs.pdf
Exceptional Circumstances: The deadline for submission of exceptional circumstances in
relation to this assignment is no later than five working days after the submission date of this
work. Please contact the Director of Studies Team –
[email protected]. See rules 6.112 –
6.141:
http://web.anglia.ac.uk/anet/academic/public/academic_regs.pdf
ASSIGNMENT QUESTION
Your task is to write a commentary on FOUR pieces of data chosen from a bank of data
samples. Each commentary is a separate, stand-alone work.
For each piece of data, you must:
1. Summarise what the data shows, clarifying any key terms.
2. Describe at least two important features of the data.
3. Explain the key features using academic/reliable sources as evidence.
4. Comment on the data and source in terms of its reliability.
5. Include appropriate in-text citations and a reference list.
Data Sources
All of the data samples are broadly related to the various fields of study of ARUL students and
may be presented in the form of tables, bar charts, line graphs, or pie charts. They have been
taken from a variety of sources, including academic journals, textbooks and websites of
international organisations, as well as the media.
The data is divided into four separate folders (A, B, C, D) and you must choose
one piece of
data from each folder
. Go to ‘Data Samples’ in the Assessment/Assessment Guidance tile on
the VLE. Copy and paste your chosen piece of data from each folder into a Word document.
You will need to write between 350-400 words for each commentary, with the overall word
count not exceeding 1,500 words.
ASSESSMENT CRITERIA
Your data commentary portfolio will be scored based on the following criteria, each of which
is weighted at 25%:

Accuracy: identifying at least two important features of the data and providing an
accurate description of each feature.
Explanation: explaining the key features using academic/reliable sources as evidence
and commenting on the reliability of the source, for example, considering issues of bias.
Language: writing in a fluent and coherent way, using accurate vocabulary for
describing data and an appropriate academic style.
Referencing: accurate use of in-text citations and an accurately formatted reference
list.