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BUS5PB Principles Of Business Analytics

tag 0 Download 10 Pages / 2,377 Words tag 09-06-2021
  • Course Code: BUS5PB
  • University: La Trobe University
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  • Country: Australia


Principles of Business Analytics 

This 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 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 work in an analytics case from the real estate market. 

Task 1

 Compile a review report that:

  • defines business analytics, analytics ecosystem (descriptive, predictive, prescriptive and exploratory analytics) and illustrates how they are adopted by various industries in their keybusiness functions ranging from strategy, marketing and sales, operations (production), customer services, etc.
  • illustrates how the data mining process (CRISP-DM) can be implemented and in particular,challenges in implementing data mining and business analytics in agile business environments.
  • describes the difference between business intelligence and business analytics, the challenges of achieving/cultivating analytic leadership and culture in practice.

Use academic, industrial and technical references and 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 report guidelines noted below. 

Task 2

Domain sights, 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, close to 20,000 records from 2017. You have been hired as a descriptive analyst to demonstrate the application of descriptive analytics techniques using either excel or R programming, in the context of real estate buyer advocacy. You will be working on two sanitised subsets of data.

1: Identify the key descriptive statistics of the property price from sample 1 using Excel or R (BUS5PB_Ass2_Sample1.csv).

  • Perform initial distribution analysis on ‘Price’ from the given data set 1 using the histogram on the major two types of property (house and townhouse). Choose reasonable bin size anduse a logarithmic function to rescale price when necessary.
  • Calculate the key descriptive statistics (mean, median, mode, range, IQR, quartile, skewness,variance, standard deviation) for the price and the rescaled price on the major two types of property (house and townhouse).
  • Perform linear correlation analysis and specify which variables are linearly correlated with‘Price’ and the rescaled ‘Price’ for the major two types of property (house and townhouse).

  • Compare price distribution for ‘Eastern Metropolitan’ and ‘Western Metropolitan’ on houses only (not townhouses). What can you find out? Perform outlier analysis on ‘Price’ for thesetwo areas and what are the price ranges for these outliers? (Hint: use box plots)

2: Develop a simple linear regression model based on sample 2 data using Excel or R(BUS5PB_Ass2_Sample2.csv)

  • Develop a simple linear regression model using Excel or R. You need to use dependent (orresponse) variable ‘Price’ and independent (or explanatory) variable ‘Landsize’. You are required to submit the excel file or the R script file.
  • Refine and improve the developed linear regression model. Illustrate and explain why the model is enhanced. (Hint: Try to focus on the model on a subset and/or remove several influential points, use the coefficient of determination and other metrics to explain)

3: Write an essay to discuss key contributing factors for property price based on results on Tasks 02.1 and 02.2, some external research, own observations and experiences. You may use graphs, tables and external references to support your explanation. (500 words)

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