Reference no: EM133589825

Introduction to Business Analytics

Assessment Instructions

Part A: Using Descriptive Analytics to address a business need (300 words)

Imagine you are a car enthusiast interested in the performance of cars (horsepower) compared to their fuel efficiency, given rising fuel costs. Your analytics study involves generating insight on how cars differ in their fuel consumption and horsepower based on whether they are front wheel drive, rear wheel drive, 4 wheel drive, all wheel drive.

Your descriptive analytics exercise is therefore to explain how cars differ in their fuel consumption and horsepower based on whether they are front wheel drive, rear wheel drive, 4 wheel drive, all wheel drive.
To complete this exercise, download exploratory.io
• Click "+" next to data frames to upload cars data.
• Generate a cluster analysis using the variables of miles per gallon in the city, miles per gallon on the highway and horsepower of the vehicle, categorised by its driveline (front wheel drive, rear wheel drive, 4 wheel drive, all wheel drive).
• Explore the clusters generated using the radar chart, line chart, category (ratio) and category (path) visualisations.
• Explain the basis of k means clustering after reviewing the following YouTube clip.

• Use this YouTube clip to explain the number of clusters/ customer segments you have produced.

Part B: Using Predictive analytics to assist with a business problem (200 words)

Imagine you are an investor who has a special interest in trading shares in oil and gas companies. In so doing, you regularly monitor the prices of stocks in this sector. To assist with your future trading activity, you are presently monitoring the historical performance of major oil and gas companies.
To do so, you have decided to fit trend lines using Tableau to the oil and gas price stock data set provided.
This activity now requires you to explain your trend lines using the principles of predictive analytics. In so doing address a line of inquiry, eg. which stocks have performed better across time?
Next you need to develop a number of other predictive lines of inquiry based on this data set and illustrate your answer with visualisations using Tableau. For example, when is the best month to sell for maximising the sales price of your stock? Your analysis may include all oil and gas stocks provided or a select few, with justification as to why you selected these stocks.
Provide a reflection on whether you can rely on historical trends alone in making your investment decisions? What type of market conditions might influence your decision and are there any independent experts (Delphi method) that might influence your future judgement?

Part C: Example of Prescriptive Analytics in addressing a business problem (300 words)
• Describe how prescriptive analytics can be used as part of a solution to a business problem with reference to ONE real-world case study of your own choosing.

• You will need to conduct independent research and consult resources provided in the subject.
• NOTE: do not use ChatGPT for this section unless you can reference it and show your prompts. You must also verify that you did some research of your own in this section.

Part D: Storytelling (200 words)
Imagine you are a consultant tasked with an analytics exercise to report to a European Union inquiry about the status of resourcing in libraries in 3 Western European countries, namely England, Germany and the Netherlands.
Use the libraries data set in Tableau or Power BI to build a story as to why an English library is likely to differ from a German library, which in turn is likely to differ from a Dutch library.
Develop some metrics (eg. calculated fields in Tableau) and illustrate these metrics on geographic maps using Tableau.
Present your story as a narrative as to what future best practice of resourcing of libraries by wealthier member countries might be, supported by your visualisations.