3
Steps to Get a Perfectly Written Assignment
One
Click “order this assignment now”
Two
Choose your deadline & pay for it
Three
Get custom-written work ready for submission
100% Pass and No-Plagiarism Guaranteed
Assignment Briefs 10-25-2022

Data sets vary from domain to another. In this coursework, you will select a dataset related to a real-world problem that best suits your area of interest.

BM9717 Data Management and Visualization

Programme:

MSc Business Analytics

Module Code:

BM9717

Module Title:

Data Management and Visualization

Distributed on:

Teaching Week 1/2

Submission Time and Date:

Component 1: Group presentation in week 12.

Component 2: Submission date is Monday 09/01/2023 before 12:00 noon.

Note: Re-Assessment will be the same assignments – Date & Time (TBC)

  • Component 1: presentation
  • Component 2: individual report

Weighting

This coursework accounts for [100]% of the total mark for this module

Component 1: a group presentation and demonstration of work result (weighted 40%)

Component 2: a 3,000 word report (weighted 60%)

Submission of Assessment

All assignments must be submitted via Turnitin. Wherever possible marked assignments will be returned to you 20 working days following submission (excluding public or statutory holidays). You must retain an electronic copy of this assignment and it must be made available within 24 hours of them requesting it be submitted.

It is your responsibility to ensure that your assignment arrives before the submission deadline stated above. See the University policy on late submission of work (the relevant extract is set out below).

Electronic Management of Assessment (EMA): Please note if your assignment is submitted electronically it will be submitted online via Turnitin by the given deadline. You will find a Turnitin link on the module’s eLP site.

Assessment Instructions

Task Description

Data sets vary from domain to another. In this coursework, you will select a dataset related to a real-world problem that best suits your area of interest. There are abundant of websites that provide publicly available datasets. A categorised list of datasets from GitHub can be found at https://github.com/caesar0301/awesome- public-datasets. The UCI Data Repository at https://archive.ics.uci.edu/ml/index.php is another long-standing source of benchmark datasets for data analysis research. Kaggle https://www.kaggle.com/datasets has interesting real-world problems and datasets.

You can select a dataset from the above sources, or another one that is available online. The dataset should be publicly available. The chosen dataset should have a reasonable size. You have to complete the following stages in this assignment:

    1. Import a real life data set.
    2. Identify the insights that the data set is potentially can provide.
    3. Data exploration and preparation: The nature of the dataset may dictate some data exploration and preparation that can help inform the decision
  1. Perform necessary data manipulation.
  2. Perform basic exploratory data analysis.
  3. Use appropriate visualisation for the results.
  4. Critically evaluate and interpret the results and how they can support business decision making.
  5. Reflect on professional, ethical and legal issues in relation to the problem and the data set.

Component 1 Deliverable – Contribute 40% to the Module Mark

Component 1 will assess learning outcomes LO 2, 3, and 4

Deadline: Electronic copy of your presentation needs to be submitted on Blackboard by Monday 12/12/2022 before 12:00 noon; group presentation will be made in the seminar sessions in the 12th teaching week (w/c 12th December 2022).

What to Hand In

  • Online - Each member in the group will be required to submit an electronic copy of your presentation in a PDF format that includes code and screenshots from your experiments appropriately labelled and commented

You need to present your group work, demonstrate your code and results in the seminar sessions in the 12th teaching week (w/c 12th December 2022).

Component 2 – Contribute 60% to the Module Mark

Component 2 will assess learning outcomes LO 1, 2, 3, 4 and 5

What to Hand In

  • A case study individual report maximum of 3000 words that documents the process of the entire case study, including data set, problems, data preparation, transformation, visualization, analysis, and critical evaluation and justification of the findings.
  • Online – file in a PDF format via Turnitin on Blackboard

Please note that for Component 2 Individual Report, you need to choose a data set which is different than the data set you used for Component 1 Group Presentation.

The submission will be done electronically via Blackboard, all deliverables shall be labelled with project name, your student name and university number

The report will be assessed on:

  • understanding of different tools in R
  • review of relevant literature
  • development methodology
  • justification of design decisions
  • consideration of professional, ethical and legal issues The report could broadly include the following sections:
  • Abstract
  • Introduction (introduce the data set and its significance of embedded insights)
  • Literature review of related work
  • Data exploration
  • Experiments (data preparation, manipulation, analysis, visualization)
  • Results
  • Discussion, Conclusions and Future Work
  • References

These are generic section titles, which you may adapt appropriately to the application/problem that is investigated. You may include sections describing modifications of algorithms or developments that are novel and specific to your work.

Late submission of work

Where coursework is submitted without approval, after the published hand-in deadline, the following penalties will apply.

For coursework submitted up to 1 working day (24 hours) after the published hand-in deadline without approval, 10% of the total marks available for the assessment (i.e.100%) shall be deducted from the assessment mark.

For clarity: a late piece of work that would have scored 65%, 55% or 45% had it been handed in on time will be awarded 55%, 45% or 35% respectively as 10% of the total available marks will have been deducted.

The Penalty does not apply to Pass/Fail Modules, i.e. there will be no penalty for late submission if assessments on Pass/Fail are submitted up to 1 working day (24 hours) after the published hand-in deadline.

Coursework submitted more than 1 working day (24 hours) after the published hand-in deadline without approval will be regarded as not having been completed. A mark of zero will be awarded for the assessment and the module will be failed, irrespective of the overall module mark.

For clarity: if the original hand-in time on working day A is 12noon the 24 hour late submission allowance will end at 12noon on working day B.

These provisions apply to all assessments, including those assessed on a Pass/Fail basis.

Word limits and penalties

If the assignment is within +10% of the stated word limit no penalty will apply.

The word count is to be declared on the front page of your assignment and the assignment cover sheet. The word count does not include:

  • Title and Contents page
  • Reference list
  • Appendices
   

 

  • Appropriate tables, figures and illustrations

 

  • Glossary

 

Bibliography

Quotes from interviews and focus groups.

 

 

Please note, in text citations [e.g. (Smith, 2011)] and direct secondary quotations [e.g. “dib-dab nonsense analysis” (Smith, 2011 p.123)] are INCLUDED in the word count.

If this word count is falsified, students are reminded that under ARTA this will be regarded as academic misconduct.

If the word limit of the full assignment exceeds the +10% limit, 10% of the mark provisionally awarded to the assignment will be deducted. For example: if the assignment is worth 70 marks but is above the word limit by more than 10%, a penalty of 7 marks will be imposed, giving a final mark of 63.

Academic Misconduct

The Assessment Regulations for Taught Awards (ARTA) contain the Regulations and procedures applying to cheating, plagiarism and other forms of academic misconduct.

The full policy is available at: http://www.northumbria.ac.uk/sd/central/ar/qualitysupport/asspolicies/

You are reminded that plagiarism, collusion and other forms of academic misconduct as referred to in the Academic Misconduct procedure of the assessment regulations are taken very seriously by Newcastle Business School. Assignments in which evidence of plagiarism or other forms of academic misconduct is found may receive a mark of zero

Mapping to Programme Goals and Objectives

This assessment will contribute directly to the following programme goals and objectives.

Knowledge & Understanding:

  • Acquire, interpret and apply specialist functional knowledge in relation to their programme of study [PLO 7.4.3]

Intellectual / Professional skills & abilities:

  • Demonstrate competence in contemporary analytical and ICT applications [PLO 7.1.3]
  • Analyse and communicate complex issues effectively [PLO 7.3.1]
  • Demonstrate decision making, problem solving and project management skills. [PLO 7.3.2]

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):

  • Demonstrate skills of analysis and synthesis in the application of research methods to the exploration of contemporary business and management issues. [PLO 7.5.2]
  • Reflect on their own ethical values [PLO 7.2.2]

Module Specific Assessment Criteria

 

0 - 39

40 - 49

50 - 54

55 - 59

60 - 69

70 - 100

Group Presentation

- weighted 40%

Student either fails to deliver presentation or does not address the core content, providing a very poor understanding of data management and visualization methods with very limited evidence of utilizing R toolkits.

Presentation materials and delivery are of a poor standard.

A poor standard presentation which provides some relevant touchpoints. Little evidence of utilizing R toolkits to discover and visualize the business insights in the data set.

Data management and visualization methods selected are not appropriate for the given task.

Poor quality of analysing implementation results.

Poor interpretation of the business insights discovered in the data set with suitable recommendations.

Poor understanding.

Presentation shows some of the understanding of data management and visualization methods but is not thorough.

Presentation shows some evidence of utilizing R toolkits. Data management and visualization methods selected are appropriate for the given task. The analysis and interpretation of results could be further improved. Presentation could be delivered more professionally.

Presentation is generally easy to follow.

Satisfactory evidence of utilizing R toolkits to discover and visualize the business insights in the data set.

Data management and visualization methods selected are appropriate for the given task.

Satisfactory quality of analysing implementation results.

Satisfactory interpretation of the business insights discovered in the data set with suitable recommendations.

Satisfactory understanding.

Presentation of materials is generally strong. Very good evidence of utilizing R toolkits to discover and visualize the business insights in the data set.

Data management and visualization methods selected are appropriate for the given task. Very good quality of analysing implementation results. Very good interpretation of the business insights discovered in the data set with suitable recommendations. Very good understanding.

A professional materials and delivery are of a very high standard. Clear evidence of utilizing R toolkits to discover and visualize the business insights in the data set.

Data management and visualization methods selected are appropriate for the given task.

Excellent quality of analysing implementation results.

Excellent interpretation of the business insights discovered in the data set with suitable recommendations. Deep understanding shown.

 

0 - 39

40 - 49

50 - 54

55 - 59

60 - 69

70 - 100

Individual Report

- weighted 60%

Report is either not submitted or completed to a poor standard. Report may be poorly presented or lacking supporting information. The core methods of data management and visualization taught in the module are not shown in the report. The report has very little relevant literature referred and cited. The report has very limited evaluation and discussion of the business insights of the results. Very limited consideration of legal, social, ethical, security and professional issues.

The report doesn’t have adequate technical quality.

Produced and demonstrated a solution to the problem, which is flawed, despite some effort.

Poor evidence of reviewing academic sources.

Little evaluation and discussion of the results.

Little consideration of legal, social, ethical, security and professional issues.

Narrative difficult to follow. Poor quality of references and citations.

The student demonstrated a sound knowledge of the data management and visualization methods covered within the module and has produced a report which has a limited research base and could be presented more professionally. The student has provided a basic evaluation and discussion of the business insights of the results, but does this with a limited literature base and does not offer any critical analysis.

The student understanding of the data management and visualization methods is largely evident and report is generally well presented. Satisfactory technical quality.

Produced and demonstrated good quality solution to the problem.

Good evidence of reviewing multiple academic sources. Some references and citations.

Good evaluation and discussion of the business insights of the results.

Legal, social, ethical, security and professional issues fully considered.

Report is presented to a high standard and are easy to follow. Very good technical quality. Produced and demonstrated very good quality solution to the problem. Sufficient information for the reader is provided to reproduce the results. Very good evidence of systematic review using multiple high quality academic sources. Logical, clear development of narrative.

Appropriate references and citations. Very good evaluation and discussion of the business insights of the results. Legal, social, ethical, security and professional issues fully considered.

Excellent technical quality report.

Produced and demonstrated a comprehensive, high quality solution to the problem.

Sufficient information for the reader is provided to reproduce the results. Outstanding evidence of systematic review using multiple high quality academic sources. Logical, clear development of narrative. High quality references and citations.

Outstanding evaluation and discussion of the business insights of the results. Legal, social, ethical, security and professional issues fully considered.

100% Plagiarism Free & Custom Written, Tailored to Your Instructions
paypal checkout

Our Giveaways

Plagiarism Report

for £20 Free

Formatting

for £12 Free

Title page

for £10 Free

Bibliography

for £18 Free

Outline

for £9 Free

Limitless Amendments

for £14 Free

Get all these features for
£83.00 FREE