Essential Tools for Business Analytics (MBAS901)

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Essential Tools for Business Analytics (MBAS901)
Trimester 2, 2021
Sydney Business School, UOW
Lecturer: Rohan Wickramasuriya
Assessment 2: Exploratory Business Analytics project
Individual Assessment
Due Date: Wednesday 23rd June 2021, by 11.30 pm (Submission via Turnitin)
This task requires you to use exploratory data analytics skills to derive key insights from a given
dataset. You are free to use any pre-processing operation(s) on the data such as aggregations,
calculated items, etc. and use (appropriate) chart types to tell a compelling story from the data.
Make sure you present at least 5 key insights visually using charts and explain in writing the key
insights your charts are communicating. Try and present your insights in a logical order (i.e. a
cohesive narrative) where possible.
This dataset is pre-loaded into SAS Viya.
The dataset relates to a direct marketing campaign of a Portuguese banking institution. The
marketing campaign was based on phone calls, and aimed to promote a specific product (a term
deposit). Often, more than one contact to the same client was required to determine if the customer
would subscribe to the promoted product.
The data dictionary is provided in the appendix. Primary variable of interest is ‘y’, which identifies if
a customer subscribed to the product.
Assume you work as a Business Analytics expert in this bank. Your Marketing Manager hands this
dataset to you, and asks you to uncover useful insights that may be hidden in the dataset.
Apply your exploratory data analysis skills to derive some insights from this dataset. Present your
findings in a written report.
Report requirements
Your report should contain:
1. A brief introduction – your interpretation of the background and the problem.
2. A description of the dataset (e.g. what the dataset is about, number of categorical variables,
number of measures, descriptive statistics of key variables, etc.)
3. Overall aim of your analysis
4. At least five key questions/hypotheses you want to answer/test by interrogating the dataset
(in a logical order)
5. Methods:
a. any data pre-processing you carried out
b. what chart types were used to answer each question (justification of your selection)
6. Results and Discussion: images of charts and interpretation of the charts you generated. In
this section, attempt to tell a story driven by your analytical insights.
7. Conclusion (A short summary of the background and aim. What are the key insights? How do
they relate to your overall aim? Recommendation, etc.)
Marking Criteria
Following areas of your written report will be looked at when awarding marks.
Describing the dataset & setting an overall aim for the analysis
Stating relevant questions/hypotheses to be answered through exploratory analysis
Appropriateness of the chart types/visualisation and accuracy of the generated charts
Interpretation of the charts (discussion of results)
Language, logical flow of the data story (narrative) and conclusion
Appendix: Data dictionary
1 – age
2 – job : type of job
3 – marital : marital status
4 – education
5 – default: has credit in default?
6 – housing: has housing loan?
7 – loan: has personal loan?
8 – contact: contact communication type
9 – month: last contact month of year
10 – day: contact day of month
11 – duration: last contact duration, in seconds
12 – campaign: number of contacts performed during this campaign and for this client (numeric,
includes last contact)
13 – pdays: number of days that passed by after the client was last contacted from a previous
campaign (numeric; -1 means client was not previously contacted)
14 – previous: number of contacts performed before this campaign and for this client (numeric)
15 – poutcome: outcome of the previous marketing campaign
16 Balance: available bank balance in existing accounts
Primary variable of interest:
17 – y – has the client subscribed a term deposit? (binary: ‘yes’,’no’)

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