Reference no: EM132594228

Practical Report

1. demonstrate applied knowledge of people, markets, finances, technology and management in a global context of business intelligence practice (data warehouse design, data mining process, data visualisation and performance management) and resulting organisational change and how these apply to implementation of business intelligence in organisation systems and business processes

2. identify and solve complex organisational problems creatively and practically through the use of business intelligence and critically reflect on how evidence based decision making and sustainable business performance management can effectively address real world problems

3. demonstrate the ability to communicate effectively in a clear and concise manner in written report style for senior management with correct and appropriate acknowledgment of main ideas presented and discussed.

Task 1 Data Analytics and Data Warehousing Concepts
Drawing on relevant and current literature write a short essay that addresses three sub tasks:

Task1.1) Define the concept ‘Predictive Analytics' and describe an example how an organisation has deployed predictive analytics to improve a business process or service (10 marks 250 words)

Task 1.2) Identify and describe five main types of data warehouse architectures including a diagram representation for each type of data warehouse architecture (9 marks 250 words)

Task 1.3 Identify and discuss five key factors that would determine the choice of a type of data warehouse architecture (20 marks 500 words)

Task 2 Exploratory Data Analysis and Linear Regression Analysis
Carefully study the Data Dictionary for Melbourne Housing Data Set (See Table 1) and accompanying description of each variable. It is important to understand this data set as it is used for Task 2 and Task 3 in Assignment 2. Each record in the housing.csv data set describes a property that was listed for sale and sold.

Task 2.1) Conduct and report on exploratory data analysis (EDA) of housing.csv data set using RapidMiner Studio data mining tool and RapidMiner Studio operators
Provide following for Task 2.1:
(i) a screen capture of final EDA process, briefly describe EDA process
(ii) summarise key results of exploratory data analysis in Table 2.1 Results of Exploratory Data Analysis for housing.csv. Table 2.1 should include key characteristics of each variable in housing.csv set such as maximum, minimum values, average, standard deviation, most frequent values (mode), missing values and invalid values etc.
(iii) Discuss key results of exploratory data analysis presented in Table 2.1 and provide a rationale for selecting top 5 variables for predicting sale price of a property (Price), in particular focusing on the relationships of independent variables with each other and with dependent variable Price drawing on results of EDA analysis and relevant literature on what determinates property prices

Task 2.2) Build and report on Linear Regression model for predicting property sale price (Price) using RapidMiner data mining process and appropriate set of data mining operators and a reduced set of variables from housing.csv data set as determined by your exploratory data analysis in Task 2.1.
Provide the following for Task 2.2:
(i) A screen capture of Final Linear Regression Model process and briefly describe your Final Linear Regression Model process
(ii) Table 2.2 named Results of Final Linear Regression Model for Task 2.2 for housing.csv
data set.
(iii) Discuss the results of Final Linear Regression Model for housing.csv data set drawing on key outputs (coefficients, standardised coefficients, t-statistics values, p-values and significance levels etc) for predicting property sale price (Price) and relevant supporting literature on interpretation of a Linear Regression Model.

Task 3 Tableau Desktop View of Housing Data
After connecting to housing.csv data set in Tableau Desktop you consider binning variables such as Price and recoding variables such as Type and Method to create new or more informative categorical variables

Task 3.1) Create a Tableau Text Table or Graph view that displays properties by sale price and type of property and other relevant data using the data set housing.csv. Comment on the (1) process of preparing a Text Table or Graph view using Tableau Desktop and (2) key trends and patterns apparent in Tableau view created (8 marks 50 words).
Task 3.2) Create a Tableau Text Table or Graph view that displays property sale price and potential impact of distance to CBD on sale price (Price) and other relevant data using data set housing.csv. Comment on the (1) process of preparing a Text Table or Graph view using Tableau Desktop and (2) key trends and patterns apparent in Tableau view created (7 marks 50 words).

Attachment:- Written Practical Report.rar