BUS5PB - Principles of Business Analytics Assignment 02: Descriptive Analytics in Practice Marks: 40% | Type: Individual Release Date: Wednesday 10th April 2019 Due Date: Sunday 5 th May 2019 at 11:00...

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BUS5PB - Principles of Business Analytics Assignment 02: Descriptive Analytics in Practice Marks: 40% | Type: Individual Release Date: Wednesday 10th April 2019 Due Date: Sunday 5 th May 2019 at 11:00 pm (submit on LMS). 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 work in an analytics case from the real estate market


La Trobe Business School BUS5PB 1 BUS5PB - Principles of Business Analytics Assignment 02: Descriptive Analytics in Practice Marks: 40% | Type: Individual Release Date: Wednesday 10th April 2019 Due Date: Sunday 5th May 2019 at 11:00 pm (submit on LMS). 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 work in an analytics case from the real estate market. Task 01 (15%) 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%) 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 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). La Trobe Business School BUS5PB 2 • 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. You may also include some external research, use graphs, tables and external references to support your explanation. You may extend your analysis from Task 02.2 to include other independent variables available in the given data set. (500 words) 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. Reports should be written in Microsoft Word (font size 11) and submitted as one Word file and one solution file (in Excel). La Trobe Business School BUS5PB 3 Marking rubric A grade will be awarded to each of the tasks and then an overall mark determined for the entire assessment. The rubric below gives you an idea of what you must achieve to earn a certain ‘grade’. As a general rule, to meet a ‘C’, you must first satisfy the requirements of a ‘D’. And for an ‘A’, you must first satisfy the requirements of a ‘B’, which must of course first meet the requirements of a ‘C’ and so on. Criterion Pass (D) Credit (C) Distinction (B) High Distinction (A) Task 01 Reasonable coverage Good coverage of Excellent coverage of Extensive coverage of (15 marks) of requirement points. requirement points. all requirement all requirement Limited understanding Average points. points. of required points. understanding of Good understanding Highly-detailed and required points. of required points. relevant or creative understanding of required points. Task 02.1 Limited effort to Average effort to Excellent effort to Exceptional effort to (10 marks) demonstrate and demonstrate demonstrate demonstrate all appropriate appropriate appropriate techniques and techniques and techniques and techniques and interpret output. interpret output. interpret output. interpret output. Exceptional Limited knowledge in Average knowledge in Excellent knowledge knowledge in descriptive analytics descriptive analytics in descriptive analytics descriptive analytics techniques. techniques. techniques. techniques. Task 02.2 Limited effort to Average effort to Excellent effort to Exceptional effort to (5 marks) develop and present a develop and present a develop and present a develop and present a linear regression linear regression linear regression linear regression model and address model and address model and address model and address the requirements. the requirements. the requirements. the requirements. Task 02.3 Limited effort to Average effort to Excellent effort to Exceptional effort to (10 marks) interpret and present interpret and present interpret and present interpret and present output based on output based on output with high output with high external research. external research. business relevance business relevance based on external based on external research research • Standard plagiarism and collusion policy, and extension and special consideration policy of this university apply to this assignment. • A cover sheet is NOT required. By submitting your work online, the declaration on the university’s assignment cover sheet is implied and agreed to by you. 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.)
Answered Same DayApr 26, 2021BUS5PBLa Trobe University

Answer To: BUS5PB - Principles of Business Analytics Assignment 02: Descriptive Analytics in Practice Marks:...

Pooja answered on Apr 29 2021
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Table of Contents
Task 1    3
1)    3
2)    3
3)    4
4)    4
Task 2    8
Part a)    8
Part b)    9
References    11
Task 1
1)
Strategic management make use of company resources, abilities, skills, competency to the maximum extent of the output simply put the ability of a company to create the required structure and function necessary for the growth of the company in order to constantly
be able to manage their resources, the needs of the customer, the competition in the market as well as in order to achieve the mission and vision. Pearce, J. A., Robinson, R. B., & Subramanian, R. (2000).
Competitive advantage can be understood as the presence of factors which in the minds of customers provide a factor of value or differentiation which allows them to deal with the factor of competition and make use of the market better. Porter, M. E., & Millar, V. E. (1985).
The value proposition can be defined as any processes of the company which and strategic, operational supply chain value to the company, making it easy for them to develop new innovations. Competitive factors of being able to attract customers as well as the values that provide the factors of the distinction.
2)
Descriptive analytics summarizes key features of available data in quantitative format which is uses to evaluate sales and marketing numbers. The main features of descriptive statistics are measures of central tendency, measures of variation, distribution of variable, and graphical representation. Abbasi, A., Li, W., Benjamin, V., Hu, S., & Chen, H. (2014, September).
Prescriptive analytics is more of prescribe analysis in qualitative manner just like recommendations helpful in operational plans and budgeting.
Predictive analytics as name suggests is more of accurate prediction based on data crunching algorithms which gives forecasting and trends in future helpful to forecast revenue and profits in accounting. Predictive analysis helps us to predict the dependent variable on the basis of independent variables. We can predict sales on the basis of gender, income, quantity etc. Siegel, E. (2013).
Exploratory analytics is more of data visualization and summarisation of key parameters used for assessing business performance and trends. Frequency distribution, pie chart and bar chart are representative of variables measured by the nominal or ordinal scale of measurement. Scatterplot is used to measure linear relationship between sales and profit. For scatterplot both variables should be measured by the ratio scale of measurement. Gibson, D., & de Freitas, S. (2016).
3)
CRISP-DM stands for Cross-industry process for data mining. This method provides a structured approach to planning on the data mining project. This method is useful when using analytics to solve the theory of business issues. Wirth, R., & Hipp, J. (2000, April).
This model is an idealized sequence of events. And practice many of the tasks can be performed in a different order and it will often be necessary to Back track to the previous task and repeat certain actions. Process of CRISP-DM is as follows.
(i) Business understanding
(ii) Understanding of the data
(iii) Data preparation
(iv) Modelling
(v) Evaluation
(vi) Deployment
4)
Business Intelligence is utilizing the data to take business decisions by collecting, monitoring and reporting data for interpretation. It mainly helps to filter out the relevant data out of the vast existing data to find out the trends and patterns of the past and present so that it can be used to make better decisions for current operations. Minelli, M., Chambers, M., & Dhiraj, A. (2012).
It improves the overall operational efficiency and fosters growth and productivity in an organization. Also, it provides rich data...
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