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. Anunderstandingofhowavarietyofanalysistechniquescanbeusedtotakerawdata
and turn it into information that is meaningful to a business concern.
2. Howaparticularbusinessconcernshapesthedecision-makingprocessindata
analytics.
3. Anabilitytoselect,prepare,anduseappropriatedata,analysistechniques,and
visualisations.
4. Anunderstandingofavarietyofdatasourcesandthewaythatthedataisstructured.
Essential Elements
You must submit 1 Jupyter notebook which will: 1. Demonstrateanunderstandingof:
a. Selecting and processing data appropriate for required analysis
b. Selectingandperforminganalysistechniquesappropriatetoabusinessconcern c. Addressing a business concern through visualisation of analysis
2. Documentyourdecisionmakingwithexplanationsofyourchoices
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:Thiswillbrieflydescribethebusiness,thebusinessconcernanditssignificance 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:Youwillchooseadatasourceappropriatetoyourscenario,andwritethenecessary code to obtain the data and make it available for analysis in your notebook.
3. Processing:Thedatamayneedtobeprocessedpriortoanalysis.Ataminimumitshould be cleaned, but it may need to be processed in other ways appropriate to your chosen analysis technique.
4. Analysis:Youwillneedtoselectananalysisthatisappropriatetoyourscenario,andwhich also includes:
a. At least two of: reading and cleaning a text file, parsing unstructured data, analysing with social media data.
b. Atleastoneof:useofopendataAPIorweb-scraping.
5. Visualisation:Youwillneedtocreateavisualisationthatisappropriatetoyourscenarioand the results of your analysis. You must include at least two different types of visualisation (e.g. tabular, graph or chart, annotated text).
6. Connectwithconcern:Youneedtoconnectyourvisualisationbacktothebusiness 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.