Module Code: MSIN0025 2021-2022
Module Title: Data Analytics II
Assignment: Individual Assignment

Section Section
Marks
FAIL PASS MERIT DISTINCTION DISTINCTION+
Problem
Definition
10% Missing or
incomplete problem
definition; unclear
or unstructured
problem definition
Context, business problem
and data loosely identified
Context, business
problem and data clearly
identified
Context, business problem and
data explored with clarity and
understanding
Highly professional and clear
introduction, exploring context,
business problem and data
Data Description/
Data Visualization
30% Missing or
incomplete data
description and data
visualization
Data description and
visualization are included,
but lack specificity to the
objective or business
Data description and
visualization are included,
and align well to the
business objectives
Data description and
visualization are clearly
demonstrated, and align well to
the business objectives with
clear understanding
Highly professional data description
and data visualization, exceptionally
well align to the business objectives
with clear understanding
Data Preparation 10% Missing or
incomplete data
preparation
Data preparation/cleaning
is included, but lack
justification of the steps
and reason; Lack
consistency with the
business objectives and the
following models
Data
preparation/cleaning is
included, associated with
justification of the steps
and reason
Data preparation/cleaning is
included, associated with
clearly justification of the steps
and reason, and well align to
the business objectives and the
following models
Highly professional data
preparation/cleaning is included,
associated with exceptionally well
justification of the steps and reason,
and exceptionally well align to the
business objectives and the following
models
Prediction Models 40% Missing or
incomplete models
with configuration,
not clearly defined
scoring rule to
measure accuracy of
the models
Complete individual models
and the scoring rule to
measure accuracy of the
models and select the best
model
Complete individual
models and ensemble
model(s); justify the
scoring rule to measure
model accuracy and
select the best model
Complete individual models
and ensemble model(s) with
their configuration steps;
clearly justify the scoring rule
to measure model accuracy and
select the best model
Complete individual models and
ensemble model(s), exceptionally
well justification their configuration
steps; clearly justify the scoring rule
to measure model accuracy and
select the best model
Conclusion and
Recommendation
10% Missing conclusion
and
recommendations
Combine model results and
provide conclusions of the
business problems
Combine model results
and provide conclusions
of the business problems;
draw meaningful
recommendations to the
decision-makers
Combine model results and
provide conclusions of the
business problems; clearly
demonstrate meaningful
recommendations to the
decision-makers with reasons
Combine model results and provide
conclusions of the business
problems; clearly and detailed
demonstrate meaningful
recommendations to the decision-
makers with reasons