MITS6005 Big Data Major Assignment

The purpose of assessment is to assess students on the following Learning Outcomes:
LO2: Expertly apply techniques to perform big data query manipulation, evaluate various
data storage option and type of aggregated data modelling. Through a critical study, choose
an appropriate storage model based on the application requirements for processing large
amounts of structured and unstructured data.
LO3: Independently perform data manipulation and querying (including updates,
transactions, and indexes) big data applications dealing with high volume using NoSQL.
Organize, store the collected data and manipulate by crafting queries. For example, using
Hive, HBase and related data tools.
LO4: Carry out research on emerging Big Data technologies to evolve models/solutions such
as configurable and executable compute jobs on top of using distributed and shared memory
architecture and Resilient Distributed Data Sets (RDDs).
LO5: Implement typical solution use cases in big data context using technologies such as
MapReduce and Spark Framework and using ecosystems such as Hadoop (or other similar
platform).

Objective
You will work with your group and leverage the data provided by Pixystems (see the
Data Overview above for a high-level explanation of data provided) and information obtained
from the CFO to identify areas for financial and operational improvement, address issues and
errors found in processing of data. To do this your group must create analytics that will assess
the validity of data and provide the insight the CFO is looking for.
It is expecting that you have a visualization report representing the analytical procedures
you performed and assumptions you made. The project data required for the given case study is
Major Assignment – Group (2-3 members)
Weightage: 30%
Submission deadline: Session 12
MITS6005 Major Assignment
Copyright © 2020 VIT, All Rights Reserved. 3
provided in “Fictional Project Data.xlsx” with the relevant records as tabs in the spreadsheets.
You will have 10 mins minutes to present this visualization, assumptions made, and
recommendations. Each member of your group must have an active speaking role within the presentation.

Description:
Read carefully “Pixystems_Toys_Information.pdf” file. You are going to do the analytics
using Packages in Python. You can use “Tableau Server Client” and write your code in Python
https://github.com/tableau/server-client-python , or you can use other libraries in Python; it is
your choice. Python is one of the most frequently used programming languages in many fields,
particularly in data science. There are many libraries in Python for various tasks including big
data and data visualization. You must do some research on python packages, find proper ones for
the below task and use them for analysis and writing your report. But at the end, we expect a
quality work from you. 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.
https://colab.research.google.com/notebooks/welcome.ipynb
Your report should have 1000-1500 words addressing the business questions, challenges,
analytics and data visualization in “Pixystems_Toys_Information.pdf”. It should cover what you
are going to solve and how, plots and recommendations. The report should have at least 6-10
plots (screenshots) from your findings with explanations. The python code needs to be added at
the end of the project. The template of the word file is provided as “MITS6005-Report format for
assignment-3.doc”.
The presentation should be a maximum of 10 minutes for the whole team. Each member
should talk at least 2 minutes related to the project and findings. The whole presentation should
cover the data, business questions, research findings and visualization and step by step
discussion on how you’ve achieved this project.
You will also prepare a final report outlining the following:
MITS6005 Major Assignment
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• Results of the analytics you performed along with your rational for performing
and assumptions made.
• Insight that the analytics provided management
• Explanation of any analytics you decided not to perform
• Recommendations your team has for improving Pixystems’ processes
• Overview of any other issues that Pixystems should follow-up on
• Recommendations on system controls that could be put in place
• Any other data you would like to have obtained from Pixystems

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 1,500-2,500 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 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 python code needs to be attached at the end of the report as an Appendix.
8. The referencing style must follow the IEEE referencing style.
   Submission Guidelines
9. Follow the links in Moodle to upload your report on or before the deadline. The report
   must be submitted on the LMS in the respective link i.e. MITS6005 Major assignment-
   (Melbourne/Sydney)
10. 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.
11. Late penalty applies on late submission, 10% per day would be deducted. Submissions
    must be made by the due date and time (which will be in the session detailed above) and
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    determined by your Unit coordinator. Submissions made after the due date and time will be penalized at the rate of 10% per day (including weekend days).
12. Incidence of plagiarism will be penalized. The turn-it-in similarity score will be used in determining the level if any of 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.

Marking Guide: 100 Marks (scale to 30%)

1. Structure of the written report: Background information is relevant, issues are logically ordered, recommendations clearly relate to the issues.
2. Choice of the analytics: Method chosen and Rationale for performing analysis.
3. Results of the analytics: Output of Analytics and Discussion of it in context of case study.
4. Write clearly and concisely: Arguments are explicit and succinct, appropriate headings are used, grammar and spelling are accurate.
5. Presentation: Presentation Style, Content and Q&A
   Rubrics for MITS6005 Major Assignment
   Task
   Description
   Marks
   Report Structure
   Relevant background information clearing relating the issue
   10
   Choice of Analytics
   Method chosen and Rationale for performing analysis.
   10
   Results of the analytics
   Proper insight analysis is shown using appropriate charts/graphs using python as given in the case study.
   15
   Demonstration of Python code
   Valid and error-free code demonstrated and submitted along with the report.
   25
   Arguments
   Each individual recommendations/output of the analytics are discussed specified appropriately in context with the case study.
   20
   Presentation
   Content and Style of Presentation, effective communication
   20
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   and satisfactory Q/A session.