La Trobe Business School BUS5PB 1 BUS5PB – Principles of Business Analytics Assignment 02: Descriptive Analytics in Practice Marks: 30% | Type: Individual The second assignment aims to enhance your understanding of business analytics and its implementations in industry. This assignment also provides a chance for students to practise descriptive analytics techniques in real-world analytics setting. The assignment comprises of two tasks. The first task is to develop

La Trobe Business School BUS5PB
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BUS5PB – Principles of Business Analytics
Assignment 02: Descriptive Analytics in Practice
Marks: 30% | Type: Individual
The second assignment aims to enhance your understanding of business analytics and its
implementations in industry. This assignment also provides a chance for students to practise
descriptive analytics techniques in real-world analytics setting. The assignment comprises of two
tasks. The first task is to develop an extensive review report of the landscape of business analytics
in industry. In the second task, students are required to apply descriptive analytics on a case study
from the real estate market.
Task 1 (10%)
Compile a review report that (approximately 1000 words):
• 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.
Hint: 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
points.
Use academic, industrial and technical references and real world examples to support your views
on each of the above. The report is required to be written in a professional format conforming to
report guidelines noted below.
Task 2 (20%)
DomainExperts, 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 large dataset of real estate sales in Melbourne, over 2000 records from 2019.
You have been hired as a descriptive analyst to demonstrate the application of descriptive
analytics techniques using excel, in the context of real estate buyer advocacy. You will be working
on two sanitised subsets of data.
 Task 2.1 (8%): Identify the key descriptive statistics of the property price found in the
first dataset given in BUS5PB_Ass2_Task2_1.xlsx.
a) Perform initial distribution analysis on ‘Price’ from the given data set using the
histogram. Make sure to choose reasonable bin size.
La Trobe Business School BUS5PB
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b) Calculate the key descriptive statistics (mean, median, mode, range, IQR, quartile,
skewness, variance, standard deviation) for the ‘Price’.
c) Compare price distribution for ‘Eastern Metropolitan’ and ‘Western Metropolitan’. What
can you find out? Perform outlier analysis on ‘Price’ for these two areas. What are 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 (6%): Perform linear correlation analysis on the second data set given in
BUS5PB_Ass2_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. Discuss the result.
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 metrics to
explain)
 Task 2.3 (6%): Write an essay to discuss key contributing factors for property price based
on results obtained from Tasks 2.1 and 2.2. You may also include some external research,
use graphs, tables and external references to support your explanation. You may extend your
analysis from Task 2.2 to include other independent variables available in the given data
set. (500 words)
Data dictionary/Metadata
1. Suburb
2. Price: price in thousand Australian dollars
3. Date: Date sold
4. Rooms: Number of rooms
5. Distance: Distance from CBD in kilometres
6. Landsize: Land size in square metres
7. Postcode: postcode
8. Regionname: General region (West, North West, North, North east, …etc.)
Report guidelines
1. The report should consist of a ‘cover page’, ‘table of contents’, ‘introduction’, logically organized
sections/topics, a ‘conclusion’ and a ‘list of references’.
2. Choose a fitting sequence of sections/topics for the body of the report. For task 1, the number
of sections covering 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. The report should be written in Microsoft Word (font size 11) and submitted as one Word file
reporting all the answers to both tasks 1 and 2 and one solution file (in Excel) with all
the analyses for Task 2 (name the Excel sheets with the corresponding subtask, e.g., “Task
2_1_a”, “Task 2_1_b”).
4. Use Harvard reference style.
Marking rubric
TBA