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BUS5PB Principles Of Business Analytics

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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 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 01 (15%)

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 key business 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. Hint: please review all presentation 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 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 02 (25%)

Domainsights, 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.

  • 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 and use 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 ofproperty (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). (Hint: use linear coefficient, in R you have to deal with missing values)
  • Compare price distribution for ‘Eastern Metropolitan’ and ‘Western Metropolitan’ on housesonly (not townhouses). What can you find out? Perform outlier analysis on ‘Price’ for these two areas and what are the price ranges for these outliers? (Hint: use box plots)
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