ICT702 Business Analytics & Visualization Semester 1, 2025 Assignment Help
Note: * denotes ‘Hurdle Assessment Item’ that students must achieve at least 40% in this item to passthe unit and 50% in overall assessments 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 your assessments in this unit. Refer to the library’s reference guides for more information.
Academic misconduct
VIT ensures 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 behaviors 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 asthe 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 for the 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. Penalties for 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:
Assessment Details for Assessment Item 1:
Assessment 1: Project Proposal
Overview
Introduction
This assessment item relates to the unit learning outcomes as in the unit descriptor. : The objective of this Project Proposal is to assess the ability of students to understand large data sets and apply their knowledge in analyticsto come up with useful insights.
You are provided with historical sales data for 45 stores located in different regions – each store contains a few departments. The company also runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks.
Collect the “Online Retail” dataset from Kaggle Repository . Carefully observe the dataset and apply analytics to find answers for the below queries
Task
1. Define the Business Context and Problem:
o Write an overview of Company, highlighting its market position, product range, and operational structure (100 words). o Describe the specific business problem you aim to address, detailing the impact on sales performance and profit margins (100 words). 2. Identify and Describe Data and Information Requirements:
o List the types of data required for the analysis (e.g., historical sales data, inventory data, customer orders) (100 words). o Explain the sources of these data types and how they will be accessed (100 words).
3. Outline the Data Analysis Methodology:
o Detail the analytical techniques to be used (e.g., outlier detection using statistical methods, profit margin analysis) (100 words). o Discuss the tools and technologies that will be employed (e.g., data visualization tools like Tableau) (100 words). 4. Assess Technical Feasibility:
o Assess the technical requirements such as hardware and software needed for the project (100 words).
o Evaluate the availability of necessary technical skills and resources within the team (100 words).
5. Discuss Ethical and Operational Factors:
o Highlight ethical considerations, including data privacy and consent issues (100 words).
o Discuss operational factors such as resource availability and organizational support for the project (100 words).
Submission Instructions
All submissions are to be submitted through Turnitin. Drop-boxes linked to Turnitin will be set up in Moodle. Assessments not submitted through these drop- boxes will not be considered. Submissions must be made by the end of session 3.
The Turnitin similarity score will be used to determine any plagiarism of your submitted assessment. Turnitin will check conference websites, Journal articles, online resources, and your peer’s submissions for plagiarism. You can see your Turnitin similarity score when you submit your assessments to the appropriate drop-box. If your similarity score is of concern, you can change your assessment and resubmit. However, re-submission is only allowed before the submission due date and time. You cannot make re-submissions after the due date and time have elapsed.
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:
Assessment Details for Assessment Item 2:
Overview
Introduction
This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment evaluates the progress and achievements of your group’s project since the first milestone. Building on the foundational work done in Assessment Item 1, this milestone focuses on assessing, reviewing, and confirming the outputs from your exploratory data analysis (EDA). Your task is to critically evaluate the artefacts produced, suggest necessary adjustments or refinements, and provide insights into future steps. This milestone aims to ensure that your analytics approach, data collection methods, and analysis processes are on the right track to achieve the project’s goals.
Task
Assessment Item 2: Exploratory Data Analysis Report (Group)
∙ Due: Session 6
∙ Weighting: 15%
∙ Word Limit: 1000 words
Description: Assess, review, and confirm the initial findings from exploratory data analysis (EDA) on the sales and supply chain data collected. The report should cover:
1. Document Data Collection and Preparation Steps:
o Provide a summary of the data collected, including the data sources and types (125 words).
o Describe the data cleaning and preprocessing steps taken to prepare the data for analysis (125 words).
2. Present Key Findings from EDA:
o Highlight the major findings from the EDA, including sales trends, profit margins, and any identified outliers (150 words). o Include visualizations (e.g., charts, graphs) to support the findings (150 words).
3. Propose Adjustments and Refinements:
o Suggest adjustments to the analytics approach based on the EDA findings (125 words).
o Recommend refinements to data collection and analysis processes to improve accuracy and insights (125 words).
4. Review and Document Code/Scripts:
o Review the code/scripts developed for the EDA (100 words).
o Provide documentation and comments on the code/scripts for clarity and future reference (100 words).
Based on your review you need to submit a report in IEEE format; see the word file in Moodle. Submit your report in a word or pdf format. Your report should be limited to 1000 words.
Submission Instructions
All submissions are to be submitted through Turnitin. Drop-boxes linked to Turnitin will be set up in Moodle. Assessments not submitted through these drop-boxes will not be considered. Submissions must be made by the end of session 6.
The Turnitin similarity score will be used to determine any plagiarism of your submitted assessment. Turnitin will check conference websites, Journal articles, online resources, and your peer’s submissions for plagiarism. You can see your Turnitin similarity score when you submit your assessments to the appropriate drop-box. If your similarity score is of concern, you can change your assessment and resubmit. However, re submission is only allowed before the submission due date and time. You cannot make re-submissions after the due date and time have elapsed.
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:
Assessment Details for Assessment Item 3: Retail Sales Data Analysis
Overview
Introduction
The third project milestone builds upon your group’s progress and focuses on the development of technical solutions through code and script creation. This ongoing assessment emphasizes practical application and technical proficiency using popular tools such as Power BI, Tableau, Python, or R. Alternative approaches will also be explored to ensure pragmatic and effective solutions. Continuous peer reviews and lecturer guidance will help address emerging issues and refine your approach.
Task 1: Exploratory Data Analysis (EDA)
Objective: Analyse historical sales data for 45 stores to identify trends, patterns, and factors influencing sales performance.
Overview of Data:
∙ Explore the structure and contents of the provided dataset.
∙ Identify variables in each tab (Stores, Features, Sales) and their significance for analysis.
Historical Sales Analysis:
∙ Analyze sales trends and patterns over time (2010-02-05 to 2012-11-01).
∙ Identify seasonal variations, sales peaks, and dips.
Store-wise Analysis:
∙ Identify stores with the highest and lowest sales revenue.
Task 2: Predictive Modeling
Objective: Develop predictive models to forecast future sales, predict the impact of markdown events, and predict holiday sales performance.
Sales Forecasting:
∙ Develop time-series forecasting models to predict future sales for each store and department.
∙ Evaluate model performance using appropriate metrics (e.g., Accuracy).
Holiday Sales Prediction:
∙ Develop models to predictsales performance during prominent holidays (Super Bowl, Labor Day, Thanksgiving, Christmas). Key Components for Submission:
1. Code/Script Listing (Group):
o Provide a comprehensive listing of all code and scripts developed by the group.
o Ensure that the code is well-organized, follows best practices, and includes comments for clarity and future reference. o This component will account for 10% of the total assessment grade.
2. Screenshots of Visual Outputs (Group):
o Include clear and relevant screenshots of visual outputs generated using the tools.
o Ensure that the visualizations effectively communicate key findings and insights.
o This component will also account for 10% of the total assessment grade.
3. Demonstration of Solution (Individual):
o Each group member will individually demonstrate their contribution to the project.
o The demonstration should last 15 minutes and cover the code/scripts developed, the visual outputs, and the overall solution. o This component will account for another 10% of the total assessment grade.
Submission Instructions
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 Moodleaccount. 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. Instruction: You are required to submit 2500± 10% words report (word/pdf file) on the below tasks. Use appropriate headings and subheading in your report. Please note that only group leaders will submit the file.
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 weekend