IAB303 Data Analytics Assessment Task

Assessment Task
IAB303 Data Analytics
for Business Insight
Semester I 2019
Assessment 2 – Data Analytics Notebook
Name Assessment 2 – Data Analytics Notebook
Due Sun 28 Apr 11:59pm
Weight 30% (indicative weighting)
Submit Jupyter Notebook via Blackboard
Rationale and Description
Foundational to addressing business concerns with data analytics is an understanding of
potential data sources, the kinds of techniques that may be used to process and analyse
those data, and an ability to present the final analytics in a way that is meaningful for the
stakeholders.

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This assessment will involve the creation a Jupyter notebook, demonstrating your
understanding of the technical process required to address a business concern using data
analytics.
You will use your knowledge from the workshops together with the techniques practiced in the
practical lab sessions, and apply both to a selected business scenario. You will not only
perform the necessary steps, but also provide an explanation of your decision process.
Learning Outcomes
A successful completion of this task will demonstrate:
1. An understanding of how a variety of analysis techniques can be used to take raw data
and turn it into information that is meaningful to a business concern.
2. How a particular business concern shapes the decision-making process in data
analytics.
3. An ability to select, prepare, and use appropriate data, analysis techniques, and
visualisations.
4. An understanding of a variety of data sources and the way that the data is structured.
Essential Elements
You must submit 1 Jupyter notebook which will:
1. Demonstrate an understanding of:
a. Selecting and processing data appropriate for required analysis
b. Selecting and performing analysis techniques appropriate to a business concern
c. Addressing a business concern through visualisation of analysis
2. Document your decision making with explanations of your choices
You will use the code cells of the notebook to demonstrate your grasp of analysis techniques,
and you will use the markdown cells to (a) craft a narrative linking the analysis to a business
concern, and (b) document your decision making.
Further detail on the steps required to produce the notebooks is outlined in the ‘detailed
instructions’ section below.
Marking Criteria
This assessment is criteria referenced, meaning that your grade for the assessment will be
given based on your ability to satisfy key criteria. Refer to the attached Criteria Sheet and
ensure that you understand the detailed criteria.
It is important to realise that the assessment does not only require that you know or
understand, but also that you demonstrate or provide evidence of your understanding. This
means that you are making your knowledge and understanding clear to the person marking
your assignment.
You will not receive marks or percentages for this assessment. You will receive an overall
grade (e.g. pass - 4, high distinction - 7) based on the extent to which you meet the criteria. In
general, the most important criteria (criteria 1-5) will be essential to the grade, and the least
important (criteria 6-7) will affect the grade when important criteria results conflict or are
ambiguous.
Detailed Instructions
The notebook should tell a story (narrative) based on a selected scenario, that starts with the
data selection, moves through the analysis, and concludes with connecting the visualisation to
the primary business concern of the scenario. The story should make sense to the
stakeholders.
For each step, you must document your decision making and explain why you did what you
did. This description of thinking should align with the overall narrative.
1. Scenario: This will briefly describe the business, the business concern and its significance
to the business, and the key stakeholders who have an interest in the concern. Scenarios
will be provided via blackboard for you to select from. You may choose your own scenario
only if it is approved (in advance) by a member of the teaching team – it must meet
minimum standards. A description of how you interpret your scenario should be provided
at the beginning of your notebook.
2. Data: You will choose a data source appropriate to your scenario, and write the necessary
code to obtain the data and make it available for analysis in your notebook.
3. Processing: The data may need to be processed prior to analysis. At a minimum it should
be cleaned, but it may need to be processed in other ways appropriate to your chosen
analysis technique.
4. Analysis: You will need to select an analysis that is appropriate to your scenario, and which
also includes:
a. At least two of: reading and cleaning a text file, parsing unstructured data,
analysing with social media data.
b. At least one of: use of open data API or web-scraping.
5. Visualisation: You will need to create a visualisation that is appropriate to your scenario and
the results of your analysis. You must include at least two different types of visualisation
(e.g. tabular, graph or chart, annotated text).
6. Connect with concern: You need to connect your visualisation back to the business
concern in a way that is meaningful to the stakeholders of the business. This may involve
providing additional descriptive text that explains how the visualisation might address the
concern.
Resources
The following resources may assist with the completion of this task:
Refer to the workshop and lab notebooks for techniques and discussions of business
concerns
Use Slack to exchange code and discuss detail of the task
Questions
Questions related to the assessment should be directed initially to your tutor during the lab session or
on the appropriate slack channel. Your tutor may address these for the benefit of the whole class.
The teaching team will not be available to answer questions outside business hours, nor immediately
before the assessment is due.
Criteria Sheet – Assessment 1 Workbook - IAB303 Data Analytics for Business Insight
Criteria 7 6 5 4 3 2
[1] Evidence of a
meaningful connection
between data analytics
and a business
concern.
Makes a meaningful
connection between data
analytics and a business
concern with a
consistently clear
narrative that is interesting
and engaging.
Makes a meaningful
connection between
data analytics and a
business concern
through a consistently
clear narrative.
Mostly establishes a
meaningful connection
between data analytics and
a business concern but
lacks some consistency in
the clarity of the narrative.
Sufficiently connects the
data analytics to a
business concern to
establish a meaningful
relationship through the
use of a suitable narrative.
Some elements of the
narrative make it difficult to
see a meaningful
connection between the
data analytics and a
business concern.
There is little or no
evidence of a
meaningful connection
between the data
analytics and a
business concern.
[2] Demonstration of
appropriate techniques
for addressing a
business concern with
analytics.
All techniques are clearly
appropriate and are
consistently implemented
in an exemplary way.
All techniques are
clearly appropriate and
are implemented well.
All techniques are
appropriate but some
implementations could be
improved.
Techniques are sufficiently
appropriate and are
implemented adequately.
Techniques are either
inappropriate and/or are
used incorrectly.
There is little or no
demonstration of
appropriate technique
selection or use.
[3] Evidence of
understanding analytics
visualisation and its
significance to the
business concern.
Provides exemplary
evidence of a deep
understanding of analytics
visualisation and its
significance.
Provides evidence of a
robust understanding
of analytics
visualisation and its
significance.
Mostly provides evidence of
an understanding of
analytics visualisation and
its significance.
Provides evidence of a
basic understanding of
analytics visualisation and
its significance.
There is a lack of evidence
of understanding analytics
visualisation and/or its
significance.
This is little or no
evidence of
understanding of
analytics visualisation.
[4] Evidence of an
understanding of data
selection and analysis
techniques and their
importance to the data
analytics.
Provides exemplary
evidence of a deep
understanding of data
selection and analysis
techniques and their
importance.
Provides evidence of a
robust understanding
of data selection and
analysis technique and
their significance.
Mostly provides evidence of
an understanding of data
selection and analysis
techniques and their
significance.
Provides evidence of a
basic understanding of
data selection and analysis
techniques and their
significance.
There is a lack of evidence
of understanding of data
selection and/or analysis
techniques and/or their
significance.
There is little or no
evidence of
understanding of data
selection and analysis
techniques.
[5] Demonstration of
appropriate data
selection, processing
and analysis techniques
in order to yield a
desired result.
Data selection is excellent
for the task and all
techniques are clearly
appropriate and
implemented in an
exemplary way.
Data selection is well
suited to the task and
all techniques are
appropriate and
implemented well.
Data selection, processing
and analysis is mostly
appropriate and suitable to
the task. Most are
implemented well.
Data selection, processing
and analysis is
demonstrated sufficiently
to achieve a desired result.
Some processes or
techniques are missing,
incomplete and/or are
insufficient to achieve a
required result.
There is little or no
demonstration of data
selection and/or
analysis.
[6] Demonstration of
effective English
expression and use of
markdown.
Excellent English
expression and use of
markdown.
Very good English
expression and use of
markdown.
Generally good English
expression and use of
markdown.
English expression and use
of markdown is
satisfactory for the tasks.
English expression and/or
use of markdown is
insufficient for the tasks.
There is little or no
evidence of a
demonstration of
English expression.
[7] Demonstration of
good quality
programming practices
in the notebook code.
Excellent code quality due
to adherence to quality
programming practices.
Good code quality due
to mostly adhering to
quality programming
practices.
Generally good code quality
by mostly adhering to
quality programming
practices.
Code implementations are
sufficient for the required
tasks.
Code implementations are
inappropriate and/or
insufficient for the tasks.
There is little or no
evidence of good
programming
practices.

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