Reference no: EM132281271

Assignment relates to the course objectives 1, 2 and 4:

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

4. 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.

Assignment consists of three main tasks and a number of sub tasks

Task 1 Data Lakes and Data Warehousing

Conduct a desktop research by critically reviewing relevant literature on data lakes in context of a more traditional approach to data management including data warehousing. Drawing on the relevant and current literature, write a short essay on data lakes that addresses two sub tasks:

Task 1.1) Provide a concise definition of a data lake in context of an organisational approach to data management (about 250 words) and

Task 1.2) Explain what are two advantages of deploying a data lake and what are two disadvantages of deploying a data lake as part of an organisational data management strategy (about 500 words)

Task 2 Exploratory Data Analysis and Linear Regression Analysis (Worth 35 Marks) Carefully study the Data Dictionary for Salary Data Set (See Table 1) and accompanying description of each variable in the salary.csv data set. It is important you understand this data set as it is used for Task 2 and Task 3 in Assignment 2.

Note: You should conduct some desktop research on determinates/drivers of a person's salary level in order to fully understand and interpret the key findings of the exploratory data analysis (EDA) and Linear Regression Models for the salary.csv data set for Task 2 and visual presentation of the salary.csv data set in Task 3.

Table 1 Data Dictionary for the salary.csv Data Set

Task 2.1) Conduct an exploratory data analysis (EDA) of the salary.csv data set using the RapidMiner Studio data mining tool. Note this will require use of a number of RapidMiner operators

Provide the following for Task 2.1:
(i) a screen capture of your final EDA process, briefly describe your EDA process
(ii) summarise key results of your exploratory data analysis in Table 2.1 Results of Exploratory Data Analysis for salary.csv.
(iii) Discuss the key results of exploratory data analysis presented in Table 2.1 and provide a rationale for selecting top 5 variables for predicting salary of a person and in particular their relationship with dependent/target variable salary drawing on the results of EDA analysis and relevant literature (About 300 words).

Table 2.1 should include the key characteristics of each variable in the salary.csv data set such as maximum, minimum values, average, standard deviation, most frequent values (mode), missing values and invalid values etc.

Hint: The Statistics Tab and the Chart Tab in RapidMiner Studio provide a lot of descriptive statistical information and the ability to create useful charts like Barcharts, Scatterplots etc for the EDA analysis. You might also like to look at running some correlations and/or chi square tests as appropriate for the salary.csv data set to determine which variables contribute most to predicting house values.

Task 2.2) Build a Linear Regression model for predicting salary of a person using a RapidMiner data mining process and an appropriate set of data mining operators and a reduced set of variables from the salary.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) A table named Table 2.2 named Results of Final Linear Regression Model for Task 2.2 for salary.csv data set.

(iii) Discuss the results of the Final Linear Regression Model for salary.csv data set drawing on the key outputs (coefficients, standardised coefficients, t-statistics values, p-values and significance levels etc) for predicting salary and relevant supporting literature on the interpretation of a Linear Regression Model (About 300 words).

Include all appropriate outputs such as RapidMiner Processes, Graphs and Tables that support key aspects of exploratory data analysis and linear regression model analysis of the salary.csv data set in your Assignment 2 report.

Note you need export Processes and Graphs from RapidMiner using File/Print/Export Image option and include in Task 2 section where relevant or in Appendix 2 of Assignment 2 report.

Task 3 Tableau Desktop View of salary data set

Task 3.1) Create a Tableau Text Table or Graph view that displays salary values, average hours worked per week and other relevant data using the data set salary.csv. Comment on the (1) process of preparing a Text Table or Graph view using Tableau Desktop and (2) key trends and patterns that are apparent (about 75 words).

Task 3.2) Create a Tableau Text Table or Graph view that displays salary values, education level and other relevant data using salary.csv. Comment on the (1) process of preparing a Text Table or Graph view using Tableau Desktop and (2) key trends and patterns that are apparent (about 75 words).

Note: you need copy the two Text Table / Graph views you have created in Tableau using the Worksheet Menu Copy or Export Image option and include in the Task 3 section where relevant or in Appendix 3 of Assignment 2 report.

Report presentation, writing style and referencing

Your Assignment 2 must be presented in report format, written in an appropriate style and supported where required with appropriate in text references using Harvard Referencing Style

Attachment:- Written Practical Report.rar