Principles of Business Analytics

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BUS5PB Principles of Business Analytics S1- 2023

Assignment 2: Descriptive Analytics in PracticeMicroeconomics Assignment

Due: Sunday 14th May 11:59 AEST 2023

Release Date: 21st April 2023

Due Date: Sunday 14th May 11:59 AEST 2023

Assignment Type: Individual

Weight: 40%

Submission Format: A written report (electronic form) and electronic submissions of analytics solution files on the LMS site.

Learning Objective

The aim of the second assignment is to enhance your understanding of business analytics and its implementations in industry. This assignment also provides a chance for you to practise descriptive analytics techniques in the real-world analytics setting. The assignment comprises of two main tasks. The first task is to develop an extensive review report of the landscape of business analytics in industry. In the second task, you are required to work in an analytics case from the real estate market.

Your Tasks

Task 1: (14 marks)

Compile a review report (approximately 1000-1200 words) that:

• describes the purpose, importance and role of business analytics in creating strategic value and competitive advantage.

• defines the analytics ecosystem (descriptive, predictive, prescriptive and exploratory analytics) and illustrates how they are adopted by various industries in their key business functions ranging from strategy, marketing and sales, operations (production), customer services, etc.

• illustrates how the data analytics lifecycle can be implemented and in particular, challenges in implementing business analytics and artificial intelligence in an agile business environment.

• demonstrates how Big Tech (Facebook, Apple, Microsoft, Google and Amazon) are leveraging analytics and artificial intelligence to generate organisational value for internal and external stakeholders

(Please review all lecture slides and select the relevant knowledge points. You may also need to perform research on literature and industrial cases to explain and support your discussion points.)

Use academic, industrial and technical references as well as real case examples to support your views on each of the above. The report is required to be written in a professional format conforming to the report guidelines (on Page 3).

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BUS5PB S1– 2023

Task 2: (26 marks)

PropertyExperts, a recently formed real estate buyer’s advocacy firm is looking to enter the Melbourne property market. The senior management is keen to capitalise on large volumes of historical real estate data to generate insights into various aspects of this booming market. The firm has acquired a relatively large dataset of real estate sales in Melbourne, over 2000 records from 2019. You have been hired as a business analyst to demonstrate the application of descriptive analytics techniques using Excel, in the context of real estate buyer’s advocacy. You will be working on two sanitised subsets of data.

• Task 2.1 (8 marks): Identify key descriptive statistics of the property price found in the first dataset given [BUS5PB_Assignment2_Task2_1.xlsx]

a) Perform the initial distribution analysis on ‘Price’ from the given dataset using the histogram.

Make sure to choose the reasonable bin size.

b) Calculate and discuss the key descriptive statistics (mean, median, mode, range, IQR, quartile,

skewness, variance, standard deviation) for the ‘Price’.

c) Compare the price distribution for ‘Eastern Metropolitan’ and ‘Western Metropolitan’.

What can you find out? Perform the outlier analysis on ‘Price’ for these two areas and identify

the price ranges for these outliers. (Hint: Use box plots.)

d) Can you identify which suburbs have the highest and lowest house prices?

• Task 2.2 (8 marks): Perform linear correlation analysis using Excel on the second dataset given [BUS5PB_Assignment2_Task2_2.xlsx]

a) Develop a simple linear regression model using Excel. You need to use ‘Price’ as the dependent

(or response) variable and ‘Distance’ as the independent (or explanatory) variable.

You are required to submit the Excel file.

b) Refine and improve the developed linear regression model. Illustrate and explain why the model is enhanced.

(Hint: Try to focus on the model and/or remove several influential points, use the coefficient of determination and other appropriate metrics to explain.)

• Task 2.3 (10 marks): Write a report (approximately 800-1000 words) to discuss key contributing factors for the property price based on the results obtained from Tasks 2.1 and 2.2.

Extend your analysis from Task 2.2 to include other independent variables available in the given dataset. You may include some external research – use graphs, tables and external references to support your explanation.

Data Dictionary:

Metadata

1. Suburb: Suburb

2. Rooms: Number of bedrooms

3. Price: Price in Australian dollars

4. Date: Date sold

5. Distance: Distance from CBD in kilometres

6. Postcode: postcode

7. Landsize: Land size in square metres

8. Regionname: General region (West, North West, North, North East, etc.)

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Report Guidelines

1. The report should consist of a ‘table of contents’, an ‘introduction’, logically organised sections or topics, a ‘conclusion’ and a ‘list of references’.

2. Choose a fitting sequence of sections or topics for the body of the report. For task 1, the number of sections covering the points of requirements is essential, you may add other sections deemed relevant. For task 2, you may organise relevant sections to explain the obtained results.

3. You may include diagrams, tables and charts from the analytics solutions to effectively present your recommendations. (Consider using Alt + Print Screen or Snipping to capture screenshots).

4. The reports should be written in Microsoft Word (font size 11) and submitted as one Word file reporting all the answers for both Tasks 1 and 2.

5. Use either APA or Harvard reference style and be consistent with the reference style throughout your report.

6. Final submission will comprise two separate files, one analytics solution file in Excel and the written report as a Word file. Do not compress these files into a zipped archive, submit as two separate files.

• <Student_ID>_Assignment2_Report.doc

• <Student_ID>_Assignment2_Solution.xlsx

(Note: For each of the subtasks in Task 2, present the solutio

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