IAB303 Data Analytics

Posted 2021-09-10 眼醉逢不人

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.
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:
5. 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
6. 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.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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).

12. 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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