GET BEST ASSIGNMENT HELP FOR MITS6005 BIG DATA
Assessment Overview
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 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, 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.
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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.
• 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:
Assessment Details for Assessment Item 1:
Overview
Introduction
In this assessment you will write a critical analysis report on an academic paper(s) approved by your lecturer in the field of Big Data, or a specific industry case study.
Objective(s)
In this assessment you will have to search for a recent paper/case study about the Big Data and write a report on the paper. In the report, you need to discuss the proposed idea, methods, findings, critiques, and your recommendations. Your report should be limited to approx. 1500 words (not including references). Use 1.5 spacing with a 12-point Times New Roman font. Though your paper will largely be based on the chosen article, you should use other sources to support your discussion or the chosen paper’s premises. Citation of sources is mandatory and must be in the IEEE style.
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 5.
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.10
Marking Criteria/Rubric
You will be assessed on the following marking criteria/Rubric:
Assessment Details for Assessment Item 2:
Overview
Introduction
In this assignment you will be given a small case study, and you will need to apply your knowledge to identify the main issues, prioritise, provide insights and to discuss alternatives.
Objective(s)
In this assessment you will be required to write a report and present a 5–10-minute presentation on real-world applications which required big data tools to store and process their data. You need to identify and describe the datasets in the application and discuss how the data will be handled using at least three big data techniques from Hadoop ecosystem. The dataset can be either structured or unstructured data.
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 9.
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.
Case Study:
GlobalHealth Innovations Ltd, a leading healthcare organization based in Melbourne, is gearing up for worldwide expansion. As a seasoned Big Data specialist in the company’s IT department, you have been assigned the responsibility of creating a comprehensive report. The organization is keen on harnessing the power of Big Data to enhance healthcare delivery, optimize resource management, and improve patient outcomes. With a strategic vision to establish a unified global health platform, the company aims to leverage advanced analytics and Hadoop ecosystem tools to process and analyze diverse healthcare datasets. The goal is to provide personalized patient care, streamline operations, and contribute to medical research by analyzing trends and patterns in healthcare data. Your report will be instrumental in guiding the organization’s technological strategy for the international healthcare landscape.
Specific requirements:
1. Identify a big data application from your choice
2. Explain the specification of the large dataset
3. Discuss the reasons to use big data tools
4. Explain how the large dataset will be handled using at least three big data tools from Hadoop ecosystem
5. Conclusion and References
6. Upload the video presentation to your drive, and then share the link. Copy the link into the report, ensuring that you provide access for me to check it out.
Marking Criteria/Rubric
You will be assessed on the following marking criteria/Rubric:
Assessment
Assessment Details for Assessment Item 3:
Introduction
In this assessment you will work in groups on a major practical based case to leverage data by applying big data techniques to implement a solution, provide insights on analytics performed and make recommendations.
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 12.
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.
Objective(s)
You will work with your group and leverage the Google Play Store Apps data set.
Context
While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.
Content
Each app (row) has values for catergory, rating, size, and more.
Acknowledgements
This information is scraped from the Google Play Store. This app information would not be available without it.
Inspiration
The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!
The dataset can be downloaded from the Kaggle website at:
You will prepare a final report outlining the following tasks:
Task 1: Using Tableau or any other visualization tool, explore the dataset by creating at least six different charts to visualize the attributes and the relationship between the attributes in the dataset. It is required to interpret the figures in the report.
Task 2: Propose a data analytics question and build a data analytic model based on this question (e.g. prediction or clustering model). Implement the model using Python or any other programming language. It is required to cover the following subtasks:
• Propose the data analytics question.
• Describe the method used to create the model.
• Discuss the model construction.
• Create the experiments and discuss the results.
• Discuss the challenges working with the large dataset and how did you overcome these challenges?
For Python code, you can use Python Anaconda or Google Colab. Colab is a free notebook environment that requires no setup and runs entirely in the cloud. You need to login to google Colab to enable to use it,
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Victorian Institute of TechnologyCRICOS Provider No. 02044E, RTO No: 20829
Submission requirements:
1) Your report should have 1500-2000 words addressing the tasks. The report structure includes the following: a cover page, introduction about the case study, dataset description, addressing the above tasks, and conclusion.
2) The presentation should be a maximum of 7 minutes for the whole team. Each member should talk for at least 2 minutes related to the project and findings. The entire presentation should cover the dataset, results, and conclusion.
General Instructions
1. Your writing should be clear and concise and be in your own words.
2. The report must be in the range of 1,500-2,000 words in length excluding references.
3. Your report should be a single word or pdf document containing your report and need to be submitted through Moodle. 4. One submission per group and make sure all group members are there with contribution table at the end of the report.
5. One submission per group and make sure all group members are active in the video with at least 2 minutes’ talk from the project. 6. Use headings to guide the reader and include tables or diagrams that make the case clearer.
7. The program code needs to be attached at the end of the report as an Appendix.
8. The referencing style must follow the IEEE referencing style.
9. Upload the video presentation to your drive, and then share the link. Copy the link into the report, ensuring that you provide access for me to check it out.
Marking Criteria/Rubric
You will be assessed on the following marking criteria/Rubric:
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