specific course learning objectives

The key frameworks and concepts covered in modules 1–10 are particularly relevant for
this assignment. Assignment 3 relates to the specific course learning objectives 1, 2, 3
and 4:
1. apply knowledge of people, markets, finances, technology and management in a
global context of business intelligence practice (data warehousing and big data
architecture, data mining process, data visualisation and performance management)
and resulting organisational change and understand how these apply to the
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. comprehend and address complex ethical dilemmas that arise from evidence based
decision making and business performance management
4. communicate effectively in a clear and concise manner in written report style for
senior management with the correct and appropriate acknowledgment of the main
ideas presented and discussed.
Note you must use RapidMiner Studio for Task 1 and Tableau Desktop for Task 3
in this Assignment 3. Failure to do so may result in Task 1 and/or 3 not being marked
and zero marks awarded.
Note carefully University policy on Academic Misconduct such as plagiarism,
collusion and cheating. If any of these occur they will be found and dealt with by the
USQ Academic Integrity Procedures. If proven, Academic Misconduct may result in
failure of an individual assessment, the entire course or exclusion from a University
program or programs.
Assignment 3 Three main tasks and a number of sub tasks
Task 1 (35 Marks)
The goal of Task 1 is to predict the likelihood of rainfall tomorrow (next day) based on
today’s weather conditions. Task 1 of Assignment 3 requires you to use the data mining tool
RapidMiner to analyse and report on the weatherAUS.csv data set. Review the data
dictionary for weatherAUS.csv data set (Table 1). The dataset contains over 170,000 daily
observations from January 2008 through to December 2019 from 49 Australian weather
stations available from http://www.bom.gov.au/climate/data Bureau of Meteorology.
Definition of variables adapted http://www.bom.gov.au/climate/dwo/IDCJDW0000.shtml.
In completing Task 1 you will apply the business understanding, data understanding, data
preparation, modelling and evaluation phases of the CRISP DM data mining process.
Table 1 Data dictionary for Australian Weather Data set variables

An additional data set weatherAus-locations.csv is provided which can be joined with
weatherAus.csv on the common variable/field location in order to provide more location
specific data. See first record of weatherAUS-locations.csv data set

It’s a simple operation in RapidMiner to join two different files on a common variable/field
name – for Assignment 3 use join operator and default inner join on locations variable/field
Task 1.1 Conduct an exploratory data analysis and data preparation of weatherAUS.csv
data set using RapidMiner to understand the characteristics of each variable and the
relationship of each variable to the other variables in the data set. Summarise the findings
of your exploratory data analysis and data preparation in terms of describing key
characteristics of each of the variables in the weatherAUS.csv data set such as maximum,
minimum values, average, standard deviation, most frequent values (mode), missing values
and invalid values etc and relationships with other variables, transformation of existing
variables, creation of new variables in a table named Task 1.1 Results of Exploratory
Data Analysis and Data Preparation.
Hint: Statistics Tab and Chart Tab in RapidMiner provide a lot of descriptive statistical
information and useful charts like Barcharts, Scatterplots required for Task 1.1 etc. You
might also like to look at running some correlations and chi square tests depending on
whether a variable is a categorical variable or a numeric variable. Indicate in Task 1.1 Table
which variables you consider to be the key variables which contribute most to determining
whether it is likely to rain tomorrow. You could also consider transforming some variables
and creating new variables and converting target / label variable into a binominal variable
to facilitate analysis in Tasks 1.2, 1.3 and 1.4. Briefly discuss the key findings of your
exploratory data analysis and data preparation and the justification for which variables
are most likely to predict whether it is likely to rain tomorrow (15 marks 250 words).
Task 1.2 Build a Decision Tree model for predicting whether it is likely to rain tomorrow
based on today’s weather conditions and any other relevant variables using RapidMiner
and a set of data mining operators and a reduced weatherAUS.csv data set in part
determined by your exploratory data analysis in Task 1.1. Provide these outputs from
RapidMiner (1) Final Decision Tree Model process, (2) Final Decision Tree diagram, and
(3) Decision tree rules.
Briefly explain your final Decision Tree Model Process, and discuss the results of the Final
Decision Tree Model drawing on the key outputs (Decision Tree Diagram, Decision Tree
Rules) for predicting whether it is likely to rain tomorrow based on today’s weather
conditions and any other relevant variables and relevant supporting literature on
interpretation of decision trees (6 marks 250 words).
Task 1.3 Build a Logistic Regression model for predicting whether it is likely to rain
tomorrow based on today’s weather conditions and any other relevant variables using
RapidMiner and an appropriate set of data mining operators and a reduced weatherAUS.csv
data set determined in part by your exploratory data analysis in Task 1.1. Provide these
outputs from RapidMiner (1) Final Logistic Regression Model process and (2) Coefficients,
and (3) Odds Ratios. Hint for Task 1.3 Logistic Regression Model you may need to change
data types of some variables.
Briefly explain your final Logistic Regression Model Process, and discuss the results of the
Final Logistic Regression Model drawing on the key outputs (Coefficients, Odds Ratios) for
predicting whether it is likely to rain tomorrow and other relevant variables and relevant
supporting literature on interpretation of logistic regression models (7 marks 250 words).
Task 1.4 You will need to validate your Final Decision Tree Model and Final Logistic
Regression Model using the Cross-Validation Operator, Apply Model Operator and
Performance Operator in your data mining processes.
Discuss and compare the performance of the Final Decision Tree Model with the Final
Logistic Regression Model for predicting whether a customer is likely to churn based the
results of the confusion matrix, and ROC charts for each final model. You should use a
table here to compare the key results of the confusion matrix for the Final Decision Tree
Model and Final Logistic Regression Model using the model performance metrics – (1)
accuracy (2) sensitivity (3) specificity and (4) F1 score (7 marks 250 words).
Note the important outputs from the data mining analyses conducted in RapidMiner for
Task 1 should be included in your Assignment 3 report to provide support for your
conclusions reached regarding each analysis conducted for Task 1.1, Task 1.2, Task 1.3 and
Task 1.4. Note you can export important outputs from RapidMiner as jpg image files and
include these screenshots in the relevant Task 1 parts of your Assignment 3 Report.
Note you will find the North Text book and RapidMiner Tutorials useful references for the
data mining process activities conducted in Task 1 in relation to the exploratory data
analysis and data preparation, decision tree analysis, logistic regression analysis and
evaluation of the performance of the Final Decision Tree model and the Final Logistic
Regression model. These concepts are covered in Chapters 9, 10 and 13 of North Textbook
and in the tutorials contained within RapidMiner.
Task 2 Deep Learning, Artificial Intelligence and Ethics (30 marks)
Task 2.1 Define the concept of deep learning in context of artificial intelligence (AI) and
identify and discuss an application of deep learning deploying a commonly used deep
learning algorithm (advanced neural networks) successfully adopted in an industry and/or
government sector drawing the relevant and current literature (15 marks 1000 words)
Task 2.2 Based on your review of deep learning in context of AI identify and discuss the
key ethical concerns raised by using a deep learning algorithm to support decision making
in a specific context with appropriate in-text referencing support. (15 marks 1000 words).
Task 3 Tableau Dashboard (25 marks)
Dashboard Scenario: You are required build a dashboard Australian Weather Location
(AWL)that shows four different views of using Australian Weather data set that you used in
Task 1 as specified in sub Tasks 3.1, 3.2, 3.3 and 3.4. An additional data set weatherAuslocations.csv is provided which will need to be joined with weatherAus.csv on the common
variable/field location in order to provide location specific data views in the AWL
dashboard. See first record of weatherAUS-locations.csv data set

It’s a simple operation in Tableau to join two different files on a common variable/field
name – for Assignment 3 it is locations variable/field
You might want to consider creating fields with bins interval ranges for variables in weatheraus.csv data set where it makes sense. Note you can create bin variables in Tableau data
source view and set appropriate range names as aliases names for each variable. You will also
need to know how to create a geomap view of a data set in Tableau.
Task 3.1 Create a Tableau View of rainfall over month for each location and a specific state
Provide a screen capture and describe Tableau view created and comment on the rainfall over
one month across different locations and does this differ much for different states (5 marks
125 words).
Task 3.2 Create a Tableau View of total rainfall by year for each location and a specific
state. Provide a screen capture of and describe the Tableau view you have created and
comment on variation of total rain over year across different locations for a specific state (5
marks 125 words)
Task 3.3 Create a Tableau View of total evaporation over month by location for a specific
state. Provide a screen capture and describe Tableau view created and comment on the levels
of evaporation over month for different locations and states (5 marks 125 words).
Task 3.4 Create a Tableau GeoMap View of all Australian weather stations that provides
latitude, longitude and total rainfall for a selected year. Provide a screen capture and describe
the Tableau Geomap view you have created and comment on two or more selected locations
for a state. (5 marks 125 words).
Note: you need copy the four Text Table / Graph worksheet views and dashboard view
created in Tableau using Worksheet Menu Copy or Export Image option and include in Task
3 section where relevant or in Appendix 3 of Assignment 3 report.
Task 3.5 Provide screen snapshot of AWL Dashboard and an accompanying rationale
(drawing on relevant literature for good dashboard design) for the graphic design and
functionality that is provided by AWL Dashboard for four specified Tableau views for sub
Tasks 3.1, 3.2, 3.3 and 3.4 (5 marks 500 words). Note Stephen Few is considered to be the
Guru for good Dashboard Design and has wrote a number of books on this topic. Worth
having a look at his website https://www.perceptualedge.com/about.php and his
examples of poorly designed dashboard views and suggestions for better dashboard views.
Report structure presentation writing style and referencing (10 marks)
Presentation: Cover page, table of contents, page numbers, headings, sub
headings, tables and diagrams, use of formatting, spacing, paragraphs,
Writing style: Use of English, report written in a clear and concise manner for an intended
management audience (Correct use of language and grammar. Also, is there evidence of
spelling-checking and proofreading?)
Quality of research evident by appropriate referencing: Appropriate level of referencing
in text where required for a sub task, reference list provided, used Harvard Referencing Style
correctly
Assignment 3 Report should be structured as follows:
Assignment 3 Cover page
Table of Contents
Task 1 Heading – Sub headings for Tasks 1.1, 1.2, 1.3 and 1.4
Task 2 Heading – Sub headings for Task 2.1 and 2.2
Task 3 Heading – Sub headings for Task 3.1, 3.2, 3.3, 3.4 and 3.5
List of References
List of Appendices
You must submit two files for Assignment 3:
1. Assignment 3 Report for Tasks 1, 2 and 3 in Word document format with
extension .docx
2. Tableau packaged workbook with the extension .twbx which must contain
required four Text Table / Graph worksheet views and a dashboard
worksheet view which consolidates these four Tableau views for Task 3
You must use the following file naming convention:
1. Studentno-Studentname-CIS8008Ass3.docx
2. Studentno-Studentname-CIS8008Ass3.twbx
You must use Harvard referencing style – Harvard referencing resources
Install a bibliography referencing tool – Endnote which integrates with your word processor.
http://www.usq.edu.au/library/referencing/endnote-bibliographic-software or alternatively
use an online citation tool such as Zetoro or You Cite This For Me
USQ Library – how to reference correctly using Harvard referencing system
https://www.usq.edu.au/library/referencing/harvard-agps-referencing-guide

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