Compile a review report that (approximately 1500 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.
• 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%)
Domain Experts, 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 2018. 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 02.1 (10%): Identify the key descriptive statistics of the property price found in (BUS5PB_Ass2_Task2.1.xlsx).
• Perform initial distribution analysis on ‘Price’ from the given data set using the histogram. Make sure to choose reasonable bin size.
• Calculate the key descriptive statistics (mean, median, mode, range, IQR, quartile, skewness, variance, standard deviation) for the ‘Price’.
• Compare price distribution for ‘Eastern Metropolitan’ and ‘Western Metropolitan’. 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)
• Can you identify which suburbs have the highest and lowest house prices?
Task 02.2 (5%): Perform linear correlation analysis on the sample data set using Excel. (BUS5PB_Ass2_Task2.2.xlsx)
• 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.
• 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 02.3 (10%): Write an essay to discuss key contributing factors for property price based on results obtained from Tasks 02.1 and 02.2.