CS5811 Distributed Data Analysis Assessment/Coursework for 2025/26 (Resit)

CS5811 Distributed Data Analysis

TABLE OF CONTENTS

Main Objective of the Assessment.........................................................................................................................1
Description of the Assessment...............................................................................................................................1
Learning Outcomes and Marking Criteria...............................................................................................................1
Format of the Assessment......................................................................................................................................1
Submission Instructions..........................................................................................................................................1
Avoiding Academic Misconduct..............................................................................................................................2
Expectations of Artificial intelligence Use..............................................................................................................2
Late Coursework.....................................................................................................................................................2

MAIN OBJECTIVE OF THE ASSESSMENT

The aim of this assignment is to generate value and insight from the processing of heterogeneous data. This will be achieved by implementing several analytic methods/techniques/algorithms, evaluating them and comparing the effectiveness of the adopted approaches.
The development and implementation of the data analysis project will be supported by 
team-based effort and weekly meeting.

DESCRIPTION OF THE ASSESSMENT

The final report should be an original and individual submission, but it will be underpinned by a group effort and by effective sharing of data and partial results. It is important that individual contributions shared among the group are clearly defined (see “Authorship Contribution” below for further details). These contributions should be agreed upfront in a designated meeting.

The management of the data during (and after the project) can be described in a formal Data Management Plan (DMP). A template is available on Blackboard Learn. The DMP should be discussed with the group in a designated meeting.

The following parts should be developed by shared group effort:

• Data collection
• Data preparation and cleaning
• Exploratory data analysis
• Each member of the group is expected to implement and apply at least two

methods/approaches including:

• One machine learning method for prediction (regression or classification)
• One High Performance Computational technique for distributed data analysis and  prediction (regression or classification).

For both of these two methods/approaches, each member of the group should produce their own results and distribute/exchange their results among all the other members.

Finally, each member is expected to independently compare, discuss and evaluate these 
shared results.Submissions will be graded on technical ability, creativity, practicality, and their use of concepts introduced in different study blocks, in particular CS5706 Machine Learning and CS5710 High Performance Computational Infrastructures.

RESIT Group Management/Options.

For the re-sit you have two options. With Option 1, you will need to submit your report as part of the group you have been assigned during Term 2. That means, you will base your report around any of the data/activities/results/research-question available from your allocated past group activities. If during Term 2 you have moved to a group different from the one you have been originally allocated (i.e. you changed group) and you would like to base your report  around any of the data/activities/results/research-question available from your chosen past group activities, then this still counts as Option 1.With Option 2, you will have an opportunity to have 'a new start'. That is, you will be working individually and, you will be able to identify your own data/research question and share/compare results (like groups did during Term 2 to produce your individual report).

The default option is Option 1. That means you are going to submit as part of the group you have been assigned to (or you have changed to) in Term 2. In other words, you are going to use the same dataset, research questions, and use any available results from the other members while preparing your report.

For the individual component of the report you must develop your own necessary ML techniques to obtain your own results.
If you would like to opt for Option 2, please contact the module leader (stasha.lauria@brunel.ac.uk) *AS_SOON_AS_POSSIBLE* to update your status to avoid any potential case of misconduct (see below on avoiding misconduct).

For both options you will have limited/reduced support from the teaching team.

The report should provide an "Authorship Contribution" statement (ACS). This statement should clarify how data generation and/or analyses made by other members of the group has contributed to the project.

AUTHORSHIP CONTRIBUTION: Authorship should meet all 2 of the following conditions:

1. Authors make substantial contributions to conception and design, and/or acquisition of data, and/or analysis and interpretation of data and/or evaluation, and/or visualisation;

2. Authors participate in discussing the results critically for important intellectual content;

Example of “Authorship Contribution” statement (ACS): Y.O. and Y.Z. designed the data collection. G.S., M.K.R. and Y.M. performed the exploratory data analysis. Y.O. implemented and applied the random forest predictor.

LEARNING OUTCOMES AND MARKING CRITERIA

LO1: Design and implement a data analytics solution for generating value and insight from the processing of heterogeneous data using statistical learning and distributed computing technologies.
LO2: Critically evaluate and reflect on the appropriate use of methods and technologies for distributed data analysis, their ability to deliver accurate predictions and the value and limitations of prediction.

The coursework will be marked according to the following criteria:

1.    Identifying a data analytics problem and formulating a relevant research question and plan [LO2]

2.    Preparing, integrating and exploring the data sets suitable to answer the research question [LO1]

3.    Implementing and executing a complete and coherent data analysis [LO1]

4.    Critically reflecting on the results of the data analysis (accuracy, limitations and interpretation) [LO2]

Descriptors for lower grade bands should be satisfied and evidenced for higher grade band award, i.e. B-band grades can only be awarded if all descriptors for C-band and D-band grades have been satisfied and evidenced. Within a grade band, all descriptors should be satisfied and evidenced for the +, three descriptors should be satisfied for the base grade, while the - grade requires at least one out of four of descriptors.

FORMAT OF THE ASSESSMENT

The report should be submitted as a single PDF file. The report should include exactly the following sections:

1)    Data description and research question

2)    Data preparation and cleaning

3)    Exploratory data analysis

4)    Machine learning prediction

5)    High Performance Computational implementation

6)    Performance evaluation and comparison of methods

7)    Discussion of the findings

8)    Data Management Plan and Author Contribution statement

The main text of the report (including the eight sections above) should not be more than 12 pages (11pt font minimum, the only content allowed beyond the 12th page is an appendix section and bibliography). Any software produced must be included as code in the Appendix or uploaded as a separate archive file along the PDF file. Software must include instructions on how to run it.

SUBMISSION INSTRUCTIONS

You must submit your coursework as a PDF file on WISEflow by the submission deadline specified above (page 1).  You can follow the link to WISEflow through the module’s section on Brightspace or login in directly at https://uk.wiseflow.net/brunel.  The name of your file should follow the normal convention and must therefore include your student ID number (e.g., 0612345.pdf).      It can also include the module code (e.g., CS5811_0612345.pdf).  

AVOIDING ACADEMIC MISCONDUCT

Before working on and then submitting your coursework, please ensure that you understand the meaning of plagiarism, collusion, and cheating (including contract cheating) and the seriousness of these offences.  Academic misconduct is serious and being found guilty of it results in penalties that can reduce the class of your degree and may lead to you being expelled from the University.  Information on what constitutes academic misconduct and the potential consequences for students can be found in Senate Regulation 6.
You may also find it useful to read this  PowerPoint presentation   which explains, in plain English, the different kinds of misconduct, how to avoid (even accidentally) committing them, how we detect misconduct, and the common reasons that students give for engaging in such activities.  

If you are experiencing difficulties with any part of your studies, remember there is always help available:

• Speak to your personal tutor.  If you’re not sure who your tutor is, you can find the information on eVision or you can ask at the Student Hub (studenthub@brunel.ac.uk).
  
  
• The Student Hub can also provide advice on support and welfare issues.
  EXPECTATIONS OF ARTIFICIAL INTELLIGENCE USE
  The University has general guidance on using artificial intelligence in your studies. (https://students.brunel.ac.uk/study/using-artificial-intelligence-in-your-studies)

In this module, we expect you to self-learn as much as needed to contribute positively to your group’s work. We will not stop you from learning from different resources, e.g., online tutorials, videos, code repositories, or generative AI programs like ChatGPT or BARD, etc. However, none of these must be used unethically to pass off others’ (or AI’s) work as your own.

So be transparent in your use of AI programs as you would any other resource. Use it for guidance, and support and be aware that it may generate erroneous responses. Include critical and insightful analysis of the   generated responses. In the portfolio and project demos, we expect you to be able to explain everything you present, justify why you did things a certain way, argue its limitations, and describe alternative solutions. We want to hear your authentic voice in your work; do not rely on AI to do the talk for you.

LATE COURSEWORK

The clear expectation is that you will submit your coursework by the submission deadline stated in the study guide. In line with the University’s Coursework Submission Policy

(revised in January 2025), coursework submitted up to 48 hours late will be accepted but the penalties set out in the ‘Late Submission’ section of the policy will be applied. Work submitted over 48 hours after the stated deadline will automatically be graded NS (nonsubmission).

Please refer to the Computer Science student information pages and the Coursework Submission Procedure pages for information on submitting late work, penalties applied and procedures in the case of Extenuating circumstances.

INCLUSIVITY STATEMENT

Group work in this module offers the opportunity to collaborate with students from a variety of genders, ethnicities, cultural backgrounds, disabilities, neurodivergent profiles, and sexual orientations. Such power of diversity reflects the professional environment you will encounter in the industry and is a strength in achieving innovative and effective solutions. You are expected to value and respect different perspectives, foster a collaborative environment, and ensure that all members have the opportunity to contribute. This approach will support the development of your interpersonal, communication, and collaboration skills, which are essential for success in your future career.

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