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Sydney International School Of technology & commerce

ICT 653 Emerging Technologies in IT
1. Assignment
Write a Literature Review Report on any topic related to the following.
• Cloud Computing
• Edge Computing
• Cyber Security
• Internet of Things (IoT)
• Blockchain
• Data Analytics
• Machine Learning
You must discuss the topic with the instructor before you start the assignment.
2. Learning Outcomes
• Investigate and analyze advanced principles of Cloud Computing, Edge Computing,
Internet of Things (IoT), Blockchain, Artificial Intelligence, Machine Learning
exploring their applications and implications in depth.
• Critically review and synthesize contemporary literature on any emerging technology
in IT including Cloud Computing, Edge Computing, Internet of Things (IoT),
Blockchain, Artificial Intelligence and Machine Learning.
3. Submission Details
Thisis an individual assignment. Donotstart working on the assignment before getting approval
from your instructor. The report is to be submitted in a word or pdf format. The assessment
carries 40% weightage. 30% marks are for the report and 10% marks are for the 15-minute
recorded presentation. The report length should be not less than 2500 words and not more than
3000 words. The word count does not include the title page, table of contents, list of references
and figures. Ensure all pages are numbered. The similarity index of your report should be less
than 20%. The deadline for submission of both the report and the recorded 15-minute
presentation is midnight on Sunday, 25 August 2024. Do not use AI tools for this
assignment. Late submissions will incur a 10% deduction in marks for each day beyond the
due date. A delay of more than 5 days will result in a grade of 0.
4. Structure of the report
Title Page
The title page of your report must contain:
•The title of the report (make the title as informative as possible)
•Who the report is prepared for.
•Who it is prepared by.
•The date the report is submitted.

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Sydney International School Of technology & commerce

ICT 508 Management Information Systems

Assessment Details
Woodside and BHP have agreed to merge their petroleum businesses. A major IS/IT issue relating to
this merger is the need to integrate systems and eliminate duplicated efforts. Many IS/IT models and
tools can aid in this process and facilitate the delivery of business-driven solutions.
1. Identify and analyse a model and a tool that Woodside and BHP can use to analyse their
integration issue?
2. Discuss how Woodside and BHP can apply the model and tool to resolve the issue and
evaluate success.
Students will be assessed on the following criteria:
• Evidence and depth of research (20%)
• Relevance of content (20%)
• Application of tool and model (20%)
• Clarity of Structure (10%)
• Writing to the audience (15%)
• Correct referencing (15%)

Details about each criterion can be checked by assessing the marking guide listed in the document. It
is very important to note that research is a key criterion that will influence the mark you earn. The
best research is where students make their arguments by using peer-reviewed journal articles. While
using a minimum of six (6) sources is expected, students will only achieve a top mark in their research
component when they adequately use between ten (10) and fifteen (15) quality research sources.
Kindly note that using WIKIPEDIA and other online encyclopedia is not allowed and may result in being
given a fail grade for the task.

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Kaplan Business School

Assessment Instructions
Section #1: Machine Learning
1A: Installation & Module Imports
Step 1: Install Python libraries and import Python modules.
1) Run all the codes that will install and import the necessary Python libraries and models.
Step 2: Load and import macroeconomic data as a dataframe.
1) Load data: read csv file containing macroeconomic data. Create a suitable variable name for the
dataframe.
2) Write the Python code that displays information about the dataframe.

1B: Ordinary Least Squares
Step 1: Basic Dataframe Operations
Perform the necessary steps to transform the dataframe in a format ready for machine learning.
Step 2: Perform Ordinary Least Squares
Perform Ordinary Least Squares (OLS) and answer the following question: what proportion of the
outcome variable could be explained by the predictor, or feature, variables?

1C: Gradient Boosting Method
Step 1: XGBoost Machine Learning Algorithm
Create an XGBoost ML model in Python. Create a suitable variable name for the model.

Section #2: Explainable Machine Learning

2A: SHAP
Step 1: Create a SHAP Waterfall Plot of XGBoost ML
What are the Top 5 features that are correlated with the outcome variable?
Step 2: Create a SHAP Force Plot of XGBoost ML
Write no more than one paragraph summary of the insights shown by the SHAP force plot.
2B: Partial Dependence Plots (PDPs)
Step 1: Create a PDP of one feature against the outcome variable

Page 3 Kaplan Business School
Assessment Outline
Select one data feature and create a Python code that will display the PDP chart showing effects of
this feature on the outcome.
Step 2: Select a second data feature and create a PDP against the outcome variable
Select another, different, data feature and create a Python code that will display the PDP chart
showing effects of this feature on the outcome.

Section #3: Predict Causal Factors
Step 1: Use EconML AI Causal Learner
1) Create a suitable variable name for the causal machine learner.
2) Complete the Python code to create an EconML causal learner.
Step 2: ATE Chart – Visualisation of Causal Factors

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