ICT701 Business Intelligence Assignment Help Semester 1, 2025

Note: * denotes ‘Hurdle  

Assessment Item’ that students must achieve at least 40% in this item to pass the unit. 

Referencing guides 

You must reference all the sources of information you have used in your assessments. Please use the IEEE referencing style when referencing in your assessments in this unit. Refer to the library’s referencing guides for more information.

Academic misconduct 

VIT enforces that the integrity of its students’ academic studies follows an acceptable level of excellence. VIT will adhere to its VIT Policies, Procedures and Forms where it explains the importance of staff and student honesty in relation to academic work. It outlines the kinds of behaviours that are “academic misconduct”, including plagiarism. 

Late submissions 

In cases where there are no accepted mitigating circumstances as determined through VIT Policies, Procedures and Forms, late submission of assessments will lead automatically to the imposition of a penalty. Penalties will be applied as soon as the deadline is reached. 

Short extensions and special consideration 

Special Consideration is a request for: 

• Extensions of the due date for an assessment, other than an examination (e.g. assignment extension). 

• Special Consideration (Special Consideration in relation to a Completed assessment, including an end-of-unit Examination). 

Students wishing to request Special Consideration in relation to an assessment the due date of which has not yet passed must engage in written emails to the teaching team to Request for Special Consideration as early as possible and prior to start time of the assessment due date, along with any accompanying documents, such as medical certificates. 

For more information, visit VIT Policies, Procedures and Forms. 

Inclusive and equitable assessment 

Reasonable adjustment in assessment methods will be made to accommodate students with a documented disability or impairment. Contact the unit teaching team for more information. 

Contract Cheating 

Contract cheating usually involves the purchase of an assignment or piece of research from another party. This may be facilitated by a fellow student, friend or purchased on a website. Other forms of contract cheating include paying another person to sit an exam in the student’s place. Contract cheating warning: 

• By paying someone else to complete your academic work, you don’t learn as much as you could have if you did the work yourself. 

• You are not prepared forthe demands of your future employment. 

• You could be found guilty of academic misconduct. 

• Many of for pay contract cheating companies recycle assignments despite guarantees of “original, plagiarism-free work” so similarity is easily detected by TurnitIn. 

• Penaltiesfor academic misconduct include suspension and exclusion. 

• Students in some disciplines are required to disclose any findings of guilt for academic misconduct before being accepted into certain professions (e.g., law).

• You might disclose your personal and financial information in an unsafe way, leaving yourself open to many risks including possible identity theft. 

• You also leave yourself open to blackmail – if you pay someone else to do an assignment for you, they know you have engaged in fraudulent behaviour and can always blackmail you. 

Grades 

We determine your gradesto the following Grading Scheme:

 

Introduction 

This assignment necessitates the analysis of a dataset, the interpretation of findings, and the presentation of conclusions through a written report. It is imperative  that you complete this assignment on an individual basis and submit it electronically via the Learning Management System (LMS) before the specified due date. Ensure  that you follow the LMS instructions to verify the correct submission of your work. Please note that we do not accept hard copies or assignments submitted via email.  The assignment relies on the dataset found in the file Assignment1_RetailStore_Dataset.xlsx, which can be downloaded from LMS. 

Case Study: Retail Store Data Set: 

Supermarkets are on the rise in densely populated urban areas, leading to heightened market competition. This data set represents historical sales data from a  supermarket company with records from three different branches over a three-month period. Utilizing predictive data analytics techniques with this dataset is highly  accessible and straightforward. 

Data Description:

Victorian Institute of Technology CRICOS Provider No. 02044E, RTO No: 20829 

The “Data Description” sheet describes all the variables used in the “Retail Store Dataset” and is copied below for your convenience. Invoice id: Computer generated sales slip invoice identification number 

Branch: Branch of supercenter (3 branches are available identified by X, Y and Z). 

City: Location of supercenters 

Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card. Gender: Gender type of customer 

Product line: General item categorization groups – Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports,  and travel 

Unit price: Price of each product in $ 

Quantity: Number of products purchased by customer 

Tax: 5% tax fee for customer buying 

Total: Total price including tax 

Date: Date of purchase (Record available from January 2022 to March 2022) 

Time: Purchase time (10am to 9pm) 

Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet) 

COGS: Cost of goods sold 

Gross margin percentage: Gross margin percentage 

Gross income: Gross income 

Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10) 

Task: 

The task of designing a comprehensive Decision Support System (DSS) for a retail business based on the retail score dataset is a multifaceted assignment that requires  students to apply their knowledge and skills in the domain of business intelligence and data analysis. 

Let’s elaborate on this assignment: 

Designing a Comprehensive DSS: 

Understanding the Retail Score Dataset: To begin with, students should thoroughly understand the given retail score dataset. This entails examining the dataset’s  structure, variables, and the kind of information it contains. They should also consider the specific objectives and needs of the retail business in question. 

Defining DSS Components: Next, students need to design the components of the Decision Support System. A DSS typically includes various elements, such as a  database, user interface, analytical tools, and reporting capabilities. Students should explain how each of these components will be integrated into the system. 

Data Integration and Transformation: The retail score dataset might not be in the ideal format for decision support. Students should describe how they will integrate  the dataset into the DSS and what preprocessing steps, like data cleansing and transformation, will be necessary to make the data suitable for analysis. 

Analytical Tools and Algorithms: The heart of the DSS lies in its analytical capabilities. Students should select and justify the specific analytical tools, algorithms, and models they will use to extract insights from the data. For example, they might opt for clustering algorithms to segment customers or time series forecasting to predict  sales trends. 

User-Friendly Interface: Designing a user-friendly interface is critical. Students should discuss how they plan to present the data and insights to end-users, which may  include retail managers and executives. This interface should be intuitive and facilitate data exploration and decision-making. 

Aiding in Strategic Decision-Making: 

Identifying Key Business Objectives: Students should define the strategic objectives of the retail business. These objectives could include enhancing customer  experience or increasing sales. They need to explain how the DSS will align with and contribute to achieving these goals. 

Data-Driven Insights: The core function of the DSS is to provide data-driven insights that support decision-making. Students should illustrate how the DSS will generate  actionable insights from the retail score dataset. This could involve identifying customer preferences, forecasting demand, or detecting sales trends. 

Scenarios and “What-If” Analysis: A robust DSS allows for scenario analysis. Students should describe how their system will enable users to conduct “what-if” analyses,  helping decision-makers explore the potential impact of different strategies or market conditions. 

Visualization and Reporting: Effective communication of insights is crucial. Students should outline how the DSS will present findings through visualization tools,  dashboards, and reports. Visualizations can make complex data more understandable and actionable. 

Monitoring and Adaptation: A good DSS should not be static. Students should discuss how the system will monitor the retail environment, collect real-time data, and  adapt its recommendations based on changing conditions. 

Overall, this assignment challenges students to think holistically about designing a DSS that leverages the retail score dataset to aid in strategic decision-making. It  also highlights the importance of aligning the DSS with the specific needs and objectives of the retail business. 

The report’s length should be approximately 1500 words (excluding references). Utilize 1.5 line spacing and a 12-point Times New Roman font. Employ both numerical  and graphical statistical summaries, as sometimes insights can be gained from one that are not apparent in the other. 

Once you have drafted your report, it can be valuable to set it aside for a day and then revisit it with fresh eyes. Read it as if you were unfamiliar with the analysis.  Does it flow smoothly? Is it comprehensible? Can someone without prior knowledge understand your conclusions from the written material? This review process  often reveals opportunities to edit the report for greater clarity and directness. 

Note: Students can use any of the softwares such as Excel, PowerBI, Python, Statistica, Data Miner, Weka, RapidMiner, KNIME and MATLAB etc. Your submission should consist of two separate files:

1. Ensure the inclusion of the results produced by the software that was employed. 

2. Provide a Microsoft Word document containing your comprehensive report. 

Submission Instructions 

All submissions are to be submitted through the assignment 1 Drop-boxes that will be set up in the Moodle account for this Unit of Study. Assignments not  submitted through these drop boxes will not be considered. Submissions must be made by the due date and time (which will be in the session detailed above)  and determined by your Unit coordinator 

Note: All work is due by the due date and time. Late submissions will be penalized at 20% of the assessment final grade per day, including weekends.

Marking Criteria/Rubric 

You will be assessed on the following marking criteria/Rubric: 

 Total Marks: 20

Assessment Details for Assessment Item 2: Report – Design business intelligence system  and data warehouse  

Overview

 

Introduction 

In this independent assessment, you will leverage the case study presented in Assessment Item 1 as a foundation for your tasks. A) Develop the architecture for a business intelligence system and formulate a data warehouse framework. B) Employ visual analytics to convey your discoveries. Your work will be presented in the format of a report. The assignment relies on the dataset found in the file Assignment1_RetailStore_Dataset.xlsx, which can be downloaded from LMS. Case Study: Retail Store Data Set: 

The proliferation of supermarkets in densely populated urban regions has intensified market rivalry. This dataset contains historical sales  information from a supermarket enterprise, encompassing records from three distinct branches during a three-month timeframe. Employing  predictive data analytics methods with this dataset is easily accessible and uncomplicated. 

Data Description:

The “Data Description” sheet describes all the variables used in the “Retail Store Dataset” and is copied below for your convenience. Invoice id: Computer generated sales slip invoice identification number 

Branch: Branch of supercenter (3 branches are available identified by X, Y and Z). 

City: Location of supercenters 

Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card. Gender: Gender type of customer 

Product line: General item categorization groups – Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and  lifestyle, Sports and travel 

Unit price: Price of each product in $ 

Quantity: Number of products purchased by customer 

Tax: 5% tax fee for customer buying 

Total: Total price including tax 

Date: Date of purchase (Record available from January 2022 to March 2022) 

Time: Purchase time (10am to 9pm) 

Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet) 

COGS: Cost of goods sold 

Gross margin percentage: Gross margin percentage 

Gross income: Gross income 

Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10) 

Tasks: 

Let’s break down the key components of this assessment:

you have access to a dataset that contains information related to a retail store. This dataset likely includes data on sales, customer information,  inventory, and other relevant aspects of the retail business. 

 1. Designing Business Intelligence (BI) System and Data Warehouse Framework:  

Your first task is to design the architecture of a Business Intelligence (BI) system and a data warehouse framework.  

a. Business Intelligence System: A BI system is a set of tools and technologies that help in gathering, processing, storing, and analyzing data to  provide valuable insights to support business decision-making. Your role in this assessment is to plan and design the structure and components of  this system. You’ll need to decide how data will be collected, processed, and presented to the end-users. 

b. Data Warehouse Framework: A data warehouse is a central repository of data that is specifically designed for querying and reporting. You’ll need  to define how data from the retail store dataset will be stored in the data warehouse. This involves decisions regarding data modeling, ETL (Extract,  Transform, Load) processes, data storage technologies, and overall architecture. 

C. Utilizing Visual Analytics: Visual analytics is a process of analyzing data through interactive and visual methods such as charts, graphs, and  dashboards. In this assessment, you are expected to use visual analytics techniques to analyze the retail store dataset. This means you’ll be creating  visual representations of data to uncover insights, trends, and patterns. Your findings should help us to understand the retail business better.

Submission as a Report: Finally, you are required to present your work in the form of a report. This report should document the following: 

• a. Your design of the BI system and data warehouse framework, explaining the rationale behind your choices. 

• b. Visualizations and insights obtained from the retail store dataset using visual analytics techniques. 

• c. Any recommendations or conclusions drawn from your analysis. 

• d. The report should be well-structured, clearly written, and include visual aids like charts or graphs to support your finding.

 

Following the successful completion of these tasks using the appropriate tools, produce an analytical report that leverages visual analytics to convey  the insights uncovered to the Retail Store Directors.  

The report should span roughly 2000 words (excluding references), adhere to 1.5 line spacing, and employ a 12-point Times New Roman font. Make  use of both numerical and graphical statistical summaries, as certain insights may become apparent through one form of representation that might  not be evident in the other. 

Note: Students can use software such as Excel, PowerBI, Python, Statistica Data Miner, Weka, RapidMiner, KNIME and MATLAB etc. Your submission should consist of two separate files: 

1. Ensure the inclusion of the results produced by the open-source software that was employed. 

2. Present a Microsoft Word document that includes your in-depth Strategic Advancement report, encompassing the insights derived from the  completion of the tasks. 

Submission Instructions 

All submissions are to be submitted through turn-it-in. Drop-boxes linked to turn-it-in will be set up in the Unit of Study Moodle account. Assignments not  submitted through these drop-boxes will not be considered. 

Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made  after the due date and time will be penalized at the rate of 20% per day (including weekend days). 

The turn-it-in similarity score will be used in determining the level if any of plagiarism. Turn-it-in will check conference websites, Journal articles, the Web and  your own class member submissions for plagiarism. You can see your turn-it-in similarity score when you submit your assignment to the appropriate drop-box.  If this is a concern you will have a chance to change your assignment and re-submit. However, re-submission is only allowed prior to the submission due date  and time. After the due date and time have elapsed you cannot make re-submissions and you will have to live with the similarity score as there will be no  chance for changing. Thus, plan early and submit early to take advantage of this feature. You can make multiple submissions, but please remember we only  see the last submission, and the date and time you submitted will be taken from that submission. 

Your document should be a single word or pdf document containing your report

Note: All work is due by the due date and time. Late submissions will be penalized at 20% of the assessment final grade per day, including weekends. 

Marking Criteria/Rubric 

You will be assessed on the following marking criteria/Rubric: 

 Total Marks: 30

Overview

 

*This is a Hurdle task 

Introduction 

In the context of this group evaluation, you will: 

1. Analyze the methods applicable for predictive and prescriptive analytics using provided datasets. 

2. Create and put into action a business intelligence solution. 

3. Construct components of the proposed solution. 

As a team, you will be responsible for delivering a written report and delivering a presentation. 

The assignment relies on the dataset found in the file Assignment3_Question_Dataset.xlsx, which can be downloaded from LMS. Case Study: Loan Prediction Dataset: 

Data Description: 

Loan_ID: This is a unique identifier or reference number for each loan application. It is used to distinguish one loan application from another.

Gender: This column likely records the gender of the loan applicant, indicating whether they are male or female. 

Married: This column may indicate the marital status of the applicant, specifying whether the applicant is married or not. 

Dependents: This column typically records the number of dependents or family members financially reliant on the applicant. Education: This column indicates the educational background of the applicant, specifying whether they are educated or not. Self Employed: This column may show whether the applicant is self-employed or works for someone else. 

Monthly Applicant Income ($): This column likely records the monthly income of the primary applicant in dollars. 

Monthly Coapplicant Income ($): This column probably records the monthly income of any coapplicants, like a spouse or partner, in dollars. Loan Amount ($): This column typically indicates the amount of the loan applied for, usually in dollars. 

Loan Amount Term: This column is likely used to specify the term or duration of the loan, such as the number of months for repayment. Credit History: This column may contain information about the credit history of the applicant, often indicating whether it is good or bad. Property Area: This column likely represents the geographical area or location of the property for which the loan is sought. 

Loan Status: This column usually indicates the status or outcome of the loan application, such as whether it was approved or denied. 

Task

The major assessment task is a comprehensive project involving predictive and prescriptive analytics on loan prediction datasets, which will ultimately result in the  design and implementation of a business intelligence solution. 

Let’s break down the task and elaborate on each component: 

Examination of Techniques for Predictive and Prescriptive Analytics: 

In this phase, your group will explore and analyze various data analytics techniques and methods used for loan prediction. This typically involves studying statistical,  machine learning, and data mining techniques that can be applied to historical loan data to make predictions about future loans. Predictive analytics aims to 

forecast future events, while prescriptive analytics goes a step further to provide recommendations on what actions to take based on the predictions. Your group  will need to research and understand these techniques, including the data preprocessing steps, model selection, and evaluation metrics. 

Design and Implementation of a Business Intelligence Solution: 

After gaining a deep understanding of the techniques, your group will be tasked with designing a business intelligence (BI) solution. A BI solution involves creating a  system or platform that integrates and analyzes data to provide valuable insights for decision-making. In this context, it means creating a system that can handle  loan data and provide insights into whether a loan applicant is likely to be approved or denied. The design phase involves planning how the system will be  structured, what data sources will be used, and how the analytics will be applied. 

The implementation phase is about actually building the BI solution. This may involve developing software applications, setting up databases, and integrating  various tools and technologies. You’ll also need to implement the predictive and prescriptive analytics models that were examined in the first phase. This might  include using programming languages like Python or R, and machine learning libraries such as Scikit-Learn or TensorFlow. 

Development of Elements of the Proposed Solution: 

This component refers to the practical work of creating different components of the BI solution. This could include data collection and cleaning, model training and  testing, integration with visualization tools, and the creation of a user interface if necessary. It’s the hands-on work that transforms your design into a functional  system. 

Report and Presentation: 

Once the design and implementation phases are complete, your group will need to compile a report that documents the entire process. The report should detail the  techniques examined, the design of the BI solution, the steps taken in the development phase, and the results obtained. It should also include insights gained from  the analytics, any challenges faced, and recommendations for improving the solution or addressing potential issues. 

The presentation component involves summarizing the report’s key findings and presenting them to an audience, such as your peers or instructors. This is an  opportunity to showcase your work, explain your methodology, and share the insights your solution has generated. Effective communication and visualization of  your results are crucial during this phase. 

In summary, this assessment task encompasses a full cycle of data analytics and business intelligence development, from research and analysis to the practical  implementation and reporting. It’s a comprehensive project that allows your group to apply theoretical knowledge to a real-world problem, demonstrating your  ability to harness data for decision-making in the context of loan prediction. 

Tasks – PART B 

Each member of the group will deliver a concise 5-minute oral presentation on the submitted business report and the accompanied visual dashboard.

Submission: 

Your submission should be divided into two distinct files: 

1. Submit a Microsoft Word document containing your comprehensive business report, detailing the insights obtained from the completion of Part A. 

2. Provide a separate Microsoft PowerPoint presentation containing the slides used for your presentation. 

Submission Guidelines: 

➢ The Analysis report of 4000 words must be submitted digitally, either in PDF or Word document format. The report should include an appendix at the end containing screenshots of the Python code along with its corresponding output 

➢ The oral presentation can be delivered using presentation software (e.g., PowerPoint, Google Slides). 

➢ Ensure proper citation and referencing for any external sources or datasets used. 

➢ Please submit two files, the Report and the Oral Presentation, through the link provided in the LMS before the specified deadline. 

Note: Collaboration within the group is encouraged, but each group member must contribute substantially to the analysis, report writing, and presentation. Plagiarism  or unauthorized use of external sources will result in penalties. 

Marking Criteria/Rubric 

You will be assessed on the following marking criteria/Rubric: 

 Total Marks: 50