FIT5145 Foundations of
data science
Assessment task 3: Business and data case study
(Rubric)
Criteria High
distinction
(100% to 80%)
Distinction (70% to
79% of
available mark)
Credit (60% to
69% of available
mark)
Pass (50% to 59%
of available mark)
Fail (<50% of
available mark)
Project Description: provide a
description about the data
science project that you
study/propose, what the
objective of the project, and
what data science roles (e.g.:
data scientist, data engineer,
system architect) are involved
in this project and what are
their responsibilities.
Provides a
sophisticated
description of
the data
science project
and goal.
Provide a
distinct
classification of
data scientist
roles.
Provides some
description of
the data
science project
and goal.
Provide some
classification of
data scientist
roles.
Provides a
limited
description of
the data
science project
and goal.
Provide a
limited
classification of
data scientist
roles.
Provides a
minimal
description of
the data
science project
and goal.
Provide a
minimal
classification
of data
scientist roles.
No description of
the data science
project and goal.
No clear
classification of
data scientist
roles.
Business Model: provide
analysis about the
business/application areas the
project sits in, what kind of
benefits or values the project
can create for the specific
business area and who can
benefit from, and what are the
challenges of the project.
Provides
critical analysis
of
business/appli
cation areas
the project sits
in. Provides
clear and
succinct
description of
project
benefits(value)
, stakeholders
and
challenges.
Provides some
analysis of
business/appli
cation areas
the project sits
in. Provides
some
description of
project
benefits(value)
, stakeholders
and
challenges.
Provides
limited analysis
of
business/applic
ation areas the
project sits in.
Provides
limited
description of
project
benefits(value),
stakeholders
and
challenges.
Provides
minimal
analysis of
business/appli
cation areas
the project sits
in. Provides
minimal
description of
project
benefits(value)
, stakeholders
and
challenges.
No analysis of
business/applicati
on areas the
project sits in. No
clear description
of project
benefits(value),
stakeholders and
challenges.
Criteria High distinction
(100% to 80%) Distinction (70% to79% of available
mark)
Credit (60% to 69%
of available mark)
Pass (50% to 59%
of available mark)
Fail (<50% of
available mark)
Characterising and Analysing
data: discuss potential sources
to collect the data, provide
analysis about the
characteristics of the data (e.g.,
the 4 V's), provide analysis on
the required platforms, software,
and tools for data processing
and storage, according to the
specific data characteristics
Provides clear
demonstration of
the sources,
characteristics of
data, and data
processing and
data storage, and
classification of
the basic
technologies in
use.
Provides some
demonstration of
the sources,
characteristics of
data, and data
processing and
data storage, and
classification of
the basic
technologies in
use.
Provides limited
demonstration of
the sources,
characteristics of
data, and data
processing and
data storage, and
classification of
the basic
technologies in
use.
Provides minimal
demonstration of
the sources,
characteristics of
data, and data
processing and
data storage,
and classification
of the basic
technologies in
use.
No clear
demonstration
of the sources,
characteristics
of data, and
data
processing and
data storage,
and
classification of
the basic
technologies in
use.
Characterising and Analysing
data: specify/propose the data
analysis and the statistical
methods used in the project,
provide analysis on why you
choose those methods and
discuss the high-level output,
etc.
Provides a distinct
classification of the
kinds of data
analysis and
statistical methods
that are available.
Provides some
classification of the
kinds of data
analysis and
statistical methods
that are available.
Provides a limited
classification of the
kinds of data
analysis and
statistical methods
that are available.
Provides a
minimal
classification of
the kinds of data
analysis and
statistical
methods that are
available.
No clear
classification of
the kinds of data
analysis and
statistical
methods.
Criteria High
distinction
(100% to 80%)
Distinction (70% to
79% of
available mark)
Credit (60% to
69% of available
mark)
Pass (50% to 59%
of available mark)
Fail (<50% of
available mark)
Demonstration: identify a usable
dataset for the proposed project
and perform some basic
analysis on the identified dataset
to demonstrate the feasibility of
the project, using R (e.g.,
detailing the
information/features contained
in the dataset, analyse the basic
characteristics of the dataset,
etc), and report the analysis
process and result in the
demonstration section of a final
report ;
Provides clear
demonstration on
the project by
using real or
mockup data and
analysing it.
Provides some
demonstration on
the project by
using real or
mockup data and
analysing it.
Provides limited
demonstration on
the project by
using real or
mockup data and
analysing it.
Provides
minimal
demonstration
on the project by
using real or
mockup data
and analysing it.
No clear
demonstration
on the project by
using real or
mockup data
and analysing it.
Standard for Data Science
Process, Data Governance and
Management: describe any
standard used in your data
science process, and describe
appropriate practices for data
governance and management
in the project, e.g., issues
related to the accessibility,
security, and confidentiality of
the data as well as potential
ethical concerns with the use
of the data.
Provides clear
description of
standard, data
governance and
management
Provides some
description of
standard, data
governance and
management
Provides limited
description of
standard, data
governance and
management
Provides minimal
description of
standard, data
governance and
management
No clear
description of
standard, data
governance
and
management
Think critically and creatively,
providing justification and
analysis
Thinks out of the
box, creates or
extends to a novel
or unique idea.
Provides a
sophisticated
critical analysis.
Collect ideas,
solutions and other
information in good
ways.
Provides detailed
justification and
analysis
Reformulates a
collection of
available
information.
Provide some
justification and
analysis.
Mostly repeats
existing
information.
Provide limited
justification and
analysis.
Just repeats
existing
information.
Do not provide any
justification or
analysis.
Provide a good quality of report
in terms of structure,
expression, grammar and
spelling.
Well structured, with
impressive fluency
and flow.
Appropriate use of
sub-headings and
relevant content
sections. Adheres to
specifications (word
limit, duration, file
format).
Well-structured and
generally good links
and flow. Adheres to
specifications (word
limit, duration, file
format).
Satisfactory
structure, mostly
satisfactory links
and flow. Adheres to
specifications (word
limit, duration, file
format).
Overall basic
structure is
adequate but lacks
links and flow.
Adheres to
specifications
(word limit,
duration, file
format).
Poorly structured,
lacking linkages and
flow. Does not
adhere to
specifications (word
limit, duration, file
format).
Presentation, slide:
structure,
expression,
grammar and
spelling
Well structured,
with impressive
fluency and flow.
Appropriate use
of sub-headings,
relevant content
sections, and good
quality of slides.
Adheres
to specifications
(word limit,
duration, file format)
Well-structured and
generally good
links and flow.
Adheres
to
specifications
(word limit,
duration, file
format).
Satisfactory
structure, mostly
satisfactory links
and flow.
Adheres to
specifications
(word limit,
duration, file
format).
Overall basic
structure is
adequate, but
lacks links and
flow.
Adheres to
specifications
(word
limit, duration, file
format).
Poorly structured,
lacking
linkages and flow.
Does not adhere to
specifications
(word limit, duration,
file format).
Peer-review for presentation Marks will be given based on peer-review