程序代写代做代考 data science algorithm Excel data structure CMS052 Abstract Data Types & Dynamic Data Structures Assessment 1
CMS052 Abstract Data Types & Dynamic Data Structures Assessment 1
School of Computer Science
CMP3036M Data Science Page 1 of 2
CMP3036M Data Science Assessment 1 of 2 Criterion Grid 2016 – 2017
Learning
Outcome
Criterion Pass 2:2 2:1 1st
LO1
Critically apply
fundamental
concepts and
techniques in
data science
Section I: Data Programming Tools
(5%)
The tools section should discuss the
core data programming tools that
you have used to develop your data
solution. You should highlight the
capabilities of the tools that you
have used, and discuss their
strengths and weaknesses in
relation to other similar tools to
manipulate the assessment
dataset(s).
You have mentioned the
data programming tools
that you have used.
However, your
description is very brief.
More details about the
tools could have been
discussed.
You have described the
data programming tools
that you have used. You
have highlighted the
capabilities of the tools
but haven’t discussed or
gone into sufficient
details on their strengths
and weaknesses in
relation to other similar
tools.
You have briefly described the
data programming tools that
you have used. You have
highlighted the capabilities of
the tools, and have discussed
their strengths and weaknesses
in relation to other similar
tools. However, some of your
discussion could be more
closely related to the
manipulation of the assessment
dataset(s).
You have described the data
programming tools that you
have used in detail, including
highlighting the capabilities of
the tools, discussing their
strengths and weaknesses in
relation to other similar tools.
Overall your discussion is
closely related to the
manipulation of the
assessment dataset(s). You
have also used diagrams and
academic literature to support
your discussion where
appropriate, and reference all
tools used and discussed.
Section II: Training Data Pre-
processing and Analysis (10%)
This section presents a significant
and detailed discussion that covers
all main processes carried out to
arrive at the data solution.
Algorithm(s) or method(s) used
must be presented. Programming
challenges and advanced techniques
carried out should be detailed
verbosely.
You have described the
training data. However,
your data description is
presented briefly and
lacks coherent detail.
The data pre-processing
methods are not
explained.
You have described and
summarised the training
data clearly, and your
report briefly explains
your data pre-processing
methods. However,
some of the discussion
could be more detailed.
You have described and
summarised the training data
clearly, and your report
explains your data pre-
processing methods in
significant detail. Some
discussions could be further
supported by providing
evidence such as tables,
figures, statistics or academic
literature.
You have described and
summarised the training data
clearly, and providing solid
evidence including tables,
figures and statistics. Your
report explains your data pre-
processing methods in
excellent detail, and you have
used academic literature to
support your discussion.
CMP3036M Data Science Page 2 of 2
Section III: Predictive Models
(20%)
The predictive models section
should discuss the algorithms or
methods you have implemented on
the training data, explain why and
how they can provide predictions
for the test data.
You must submit your data
solution file following the
requirements in the briefing
document.
You have submitted
your data solution file
following the
requirements in the
briefing document. You
have discussed at least
one algorithm or
method, and your
implementation. The
presented discussion is
basic and lacks
sufficient detail to
explain your
approaches.
You have submitted
your data solution file
following the
requirements in the
briefing document. You
have discussed at least
two algorithms or
methods and your
subsequent
implementations. The
presented discussion is
brief and lacks detail.
You have submitted your data
solution file following the
requirements in the briefing
document. You have discussed
in detail at least two
algorithms or methods, and
your subsequent
implementations. The
discussion provides some
evidence of understanding
your implementation.
Academic literature is used
sparsely to support your
discussions.
You have submitted your data
solution file following the
requirements in the briefing
document. You have
discussed at least three
algorithms or methods in
depth. Your report shows very
good and robust understanding
of the discussed algorithms or
methods, and it clearly
explains your
implementations. Relevant
academic literature is used to
support your discussions.
Section IV: Model Evaluation
(10%)
The model evaluation section
should provide a critical and
reflective discussion on the AUC
metric and comparing the
algorithm(s) or methods that you
have implemented, and explain how
you selected the final algorithm or
method under the AUC for your
final data solution submission.
You have provided an
introduction to the AUC
metric for model
evaluation. However,
your description is too
brief and you haven’t
compared your
implemented algorithms
or models adequately. A
discussion on why you
selected the final
algorithm or method
under the AUC is
missing/too basic.
You have provided an
introduction to the AUC
metric for model
evaluation. You have
provided comparison on
your implemented
algorithms or methods
under the AUC.
However, your
comparison discussion
could offer more detail,
and your model
selection for final
submission is not clear.
You have provided an
introduction to the AUC metric
for model evaluation. You
have also provided comparison
on your implemented
algorithms under the AUC,
and have explained why and
how you select the final
algorithm or method for the
data solution. However, some
discussions could be in more
depth, and they can be further
supported by calculated results
or academic literature.
You have provided an
excellent and detailed
introduction to the AUC
metric for model evaluation.
You have clearly compared
your implemented algorithms
under the AUC, and have
explained why and how you
selected the final algorithm or
method for the data solution.
You have presented calculated
results and academic literature
to a high standard to support
your discussions.
Section V: References (5%)
The references section should
contain a properly formatted list
(Harvard style) of all academic
literature and other supporting
materials that have been cited
throughout the report.
Your reference list is
lacking externally
sourced material or your
reference list doesn’t
follow the university’s
referencing format.
Your reference list
evidences engagement
with externally sourced
material. Most of your
reference items follow
the university’s
referencing format.
Your reference list contains
academic and other relevant
sources. The list robustly
follows the university’s
referencing format.
Your references list contains
very good academic literature
that demonstrates a good
understanding of the area of
interest. The list follows the
university’s referencing
format to a high standard.
Weighting Criteria in this assessment are weighted as indicated by the percentages presented above.
