Reference no: EM133674550

Data Analytics for Strategic Decision Makers

Task description:

In this unit we are interested in taking an area of concern, raising good quality significant questions that address that concern, and then using well documented data analytics to answer the questions and provide insights on the area of concern in a manner appropriate to key stakeholders. This is not possible without having a good understanding of (a) foundational data analytics techniques, and (b) how to appropriately apply those techniques to address an area of concern.

This assessment will involve the completion of a Jupyter notebook to demonstrate your understanding of foundational data analytics techniques and their appropriate application.

Learning Outcome 1: Interpret human information problems from an analytical perspective, with a focus on asking questions relevant to strategic decision making.

Learning Outcome 2: Select and apply a range of data analysis techniques on diverse data sources to address organisational concerns.

Learning Outcome 3: Synthesise relevant data using appropriate analytical and visualisation techniques in a way that provides useful insight for organisations.

What you need to do

This assignment is in 2 parts. In Part A you will provide evidence of your ability to use the computational technologies (Jupyter notebooks, writing and running code, creating visualisations, etc). In Part B, you will provide evidence of your understanding of how the foundational techniques can be applied to create a narrative that addresses significant questions.

The assignment will involve completing a Jupyter notebook template (distributed via the online JupyterLab environment by week 3) to show evidence of your understanding of the material introduced during the first half of the semester. Part A will also involve you demonstrating and explaining key techniques to your tutor (in a real time 1 on 1 session). In the first week of the unit, you will be shown how to access the unit Jupyter notebooks. This will enable you to retrieve the template for the assignment.

Detailed requirements for each part are below. These will be easier to understand once you have seen the template, and completed some of the classes in the early weeks of the unit.

PART A - computational processing and data analysis techniques
Success in this part will address criteria 3 (Basic techniques) & 4 (Verification) on the rubric, and when completed fully together with a reasonable attempt of Part B will guarantee you a minimum of a 4 for the assignment. For criteria 4, you will need to provide evidence during your tutorial and/or drop-in sessions of your understanding of how you completed Part A, including loading data into a dataframe, modifying the dataframe, obtaining descriptive statistics, and visualising important results, and other basic tasks. You may have multiple attempts at this drawing on the Exemplars PDF, info from lectures and tutorials, and help from your tutor. You need to be able to explain to your tutor why you have done what you have done or what the code is actually doing when it is run.

NOTE: To ensure academic integrity, students in online tutorials will be required to be visible to the tutor, and may be asked to verify their identity with photo id.

PART B - creating a narrative to answer significant questions

Success in this part will address criteria 1 (Narrative) & 2 (Investigation and Justification) on the rubric, and is essential to be completed well to get a 7 for the assignment. In this part, you will apply a full data analytics cycle to a given scenario with provided data. However, you will choose the questions that you address, the way that you approach the analysis, and the kind of visualisation. You will also be required to effectively tell the story (the narrative) how your analysis leads from questions to insights, together with the reasoning behind your decisions. Also included in an effective narrative is avoiding simple assumptions and taking an ethical nuanced approach to the data.
Using the same template as Part A, you will add your work into the Part B section undertaking the following steps:

1. Question: Using the provided scenario, and considering the provided data, write at least one question that you will answer with a complete data analytics cycle. Explain why the question is significant.

2. Data: Load the data provided, clean as necessary, and prepare for analysis. Explain why you take the steps that you do. Explain the code that you use and why.

3. Analysis: Analyse the data in a way that is appropriate for answering the question. You may need to undertake more than one analysis to answer the question. You may need to think about the visualisations that you want to create and the insights that you want to find during this process. Explain why you make the choices that you do, and explain what you code you are using and why.

4. Visualisation: Visualise the results of your analysis in ways that are appropriate to the scenario and to the insights that you want to convey. You may need to create more than one visualisation. Explain why you create the visualisations that you do, and explain the code you are using and why.

5. Insights: Write about the insights that you found through your analysis. Ensure that the insights directly address the question and are appropriate to the scenario. Explain clearly why the insights are significant for the question and in turn for the overall scenario. Point out any shortcomings in the insights and whether they relate to issues with the analysis. Identify ways that the analysis may be improved to provide better quality insights (Note that you can reflect on how something might be improved even if you don't have the technical ability to accomplish the improvement yourself).