GET BEST ASSIGNMENT HELP FOR MITS6005 BIG DATA

Assessment Overview

Assessment tasks				Learning Outcome Mapping
Assessment ID	Assessment Item	When due	Weighting	ULO#
1	Research Report	Session 5	10%	2, 4
2	Case Study	Session 9	10%	1, 2
3	Major Assignment	Session 12	30%	2, 3, 4, 5
4	Exam *	Scheduled during the end of semester exam period	50%	1, 2, 3
To pass the unit students are expected to achieve an overall mark of 50% or more. Additionally, students must achieve a minimum of 40% of any assessment marked as a hurdle task (i.e. Exam in this subject).

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.

For more information, visit

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:

Grade	Percentage
A	80% – 100%
B	70% – 79%
C	60% – 69%
D	50% – 59%
F	0% – 49%

Assessment Details for Assessment Item 1:

Overview

Assessment tasks				Learning Outcome Mapping
Assessment ID	Assessment Item	When due	Weighting	ULO#
1	Research Report	Session 5	10%	2, 4

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 Criteria

Excellent (HD)

Very Good (DI)

Good (CR)

Satisfactory (P)

Unsatisfactory (Fail)

Report Structure (2

marks)

Exceptional organization, flow, and coherence in the report (2)

Very good organization, flow, and coherence with minor improvements

possible (1.5)

Good organization, flow, and coherence with noticeable

areas for

improvement (1)

Satisfactory

organization and

flow with some room for improvement

(0.5)

Unsatisfactory

organization and flow, significant

improvements needed (0)

Identify Issue (2 marks)

Clear identification of the issue from the chosen paper/case study (2)

Well-identified issue with minor gaps or slight lack of clarity (1.5)

Adequate

identification with noticeable errors or gaps (1)

Partial identification with significant gaps or lack of clarity (0.5)

Unsatisfactory

identification of the issue (0)

Analysis (1 mark)

Thorough analysis of the proposed idea, methods, findings, and critiques (1)

Well-analyzed content with minor errors or slight lack of clarity (0.8)

Adequate analysis with noticeable

errors or gaps (0.5)

Partial analysis with significant errors or lack of clarity (0.3)

Unsatisfactory analysis (0)

Justify

Recommendation (1 mark)

Clear and well-justified recommendations based on the analysis (1)

Well-justified

recommendations with minor gaps or slight lack of clarity (0.8)

Adequate

justification with

noticeable errors or gaps (0.5)

Partial justification with significant

errors or lack of

clarity (0.3)

Unsatisfactory

justification (0)

Conclusion (1 mark)

Concise and well

structured conclusion summarizing key points (1)

Well-structured conclusion with minor errors or slight lack of clarity (0.8)

Adequate conclusion with noticeable

errors or gaps (0.5)

Partial conclusion

with significant

errors or lack of

clarity (0.3)

Unsatisfactory

conclusion (0)

Grammar (1 mark)

Exceptional grammar, spelling, and language usage (1)

Very good grammar,

spelling, and language usage with minor

improvements possible (0.8)

Good grammar,

spelling, and

language usage with noticeable areas for improvement (0.5)

Satisfactory

grammar, spelling, and language usage with some room for improvement (0.3)

Unsatisfactory

grammar, spelling, and language usage (0)

Reference Style (2

marks)

Accurate and consistent use of IEEE style for

citations (2)

Well-executed reference style with minor errors or slight lack of consistency (1.5)

Adequate reference style with noticeable errors or gaps (1)

Partial reference

style with significant errors or lack of

consistency (0.5)

Unsatisfactory

reference style (0)

Assessment Details for Assessment Item 2:

Overview

Assessment tasks				Learning Outcome Mapping
Assessment ID	Assessment Item	When due	Weighting	ULO#
2	Case Study	Session 9	10%	1, 2

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.

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

Criteria

Excellent (HD)

Very Good (DI)

Good (CR)

Satisfactory (P)

Unsatisfactory (Fail)

Unsatisfactory layout (0)

Unsatisfactory

identification and

description (0)

Report Layout (2 marks)

Exceptional layout,

formatting, and visual appeal (2)

Very good layout with minor improvements possible (1.5)

Good layout with

noticeable areas for improvement (1)

Satisfactory layout with some room for

improvement (0.5)

Identify and

Describe the

Application (1

mark)

Clear and detailed

identification and

description of the

application related to Big Data (1)

Well-identified and

described application with minor gaps or slight lack of clarity (0.8)

Adequate identification and description with noticeable errors or gaps (0.5)

Partial identification and description with significant gaps or lack of clarity (0.2)

Assessment

Assessment Details for Assessment Item 3:

Assessment tasks				Learning Outcome Mapping
Assessment ID	Assessment Item	When due	Weighting	ULO#
3	Major Assignment	Session 12	30%	2, 3, 4, 5

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

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,

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:

Assessment Criteria	Excellent (HD)	Very Good (DI)	Good (CR)	Satisfactory (P)	Unsatisfactory (Fail)
Report Structure (2 marks)	Exceptional organization, flow, and coherence in the report introduction (2)	Very good organization, flow, and coherence with minor improvements possible (1.5)	Good organization, flow, and coherence with noticeable areas for improvement (1)	Satisfactory organization and flow with some room for improvement (0.5)	Unsatisfactory organization and flow, significant improvements needed (0)
Introduction (2 marks)	Exceptional introduction providing clear context and objectives (2)	Very good introduction with minor improvements possible (1.5)	Good introduction with noticeable areas for improvement (1)	Satisfactory introduction with some room for improvement (0.5)	Unsatisfactory introduction, significant improvements needed (0)
Findings of	Thorough and insightful	Well-presented findings	Adequate findings with	Partial findings with	Unsatisfactory findings

Assessment Criteria	Excellent (HD)	Very Good (DI)	Good (CR)	Satisfactory (P)	Unsatisfactory (Fail)
Visualization Task (3 marks)	findings from the visualization task (3)	with minor errors or slight lack of clarity (2)	noticeable errors or gaps (1)	significant errors or lack of clarity (0.5)	of the visualization task (0)
Findings of Analytics Task (3 marks)	Thorough and insightful findings from the analytics task (3)	Well-presented findings with minor errors or slight lack of clarity (2)	Adequate findings with noticeable errors or gaps (1)	Partial findings with significant errors or lack of clarity (0.5)	Unsatisfactory findings of the analytics task (0)
MapReduce Task (3 marks)	Thorough and insightful findings from the MapReduce task (3)	Well-presented findings with minor errors or slight lack of clarity (2)	Adequate findings with noticeable errors or gaps (1)	Partial findings with significant errors or lack of clarity (0.5)	Unsatisfactory findings of the MapReduce task (0)
Code Provided (3 marks)	Clear and well documented code provided for tasks (3)	Well-documented code with minor errors or slight lack of clarity (2)	Adequate code with noticeable errors or gaps (1)	Partial code with significant errors or lack of clarity (0.5)	Unsatisfactory code provided (0)
Grammar (2 marks)	Exceptional grammar, spelling, and language usage (2)	Very good grammar, spelling, and language usage with minor improvements possible (1.5)	Good grammar, spelling, and language usage with noticeable areas for improvement (1)	Satisfactory grammar, spelling, and language usage with some room for improvement (0.5)	Unsatisfactory grammar, spelling, and language usage (0)
Reference Style (2 marks)	Accurate and consistent use of the specified reference style (2)	Well-executed reference style with minor errors or slight lack of consistency (1.5)	Adequate reference style with noticeable errors or gaps (1)	Partial reference style with significant errors or lack of consistency (0.5)	Unsatisfactory reference style (0)
Video Presentation (10 marks)	Exceptional video presentation skills, engaging, and clear communication (10)	Very good presentation with minor improvements possible (7)	Good presentation with noticeable areas for improvement (5)	Satisfactory presentation with some room for improvement (3)	Unsatisfactory video presentation, significant improvements needed (0)