Module title Databases Systems and Data Analytics

ASSIGNMENT BRIEF

Module title Databases Systems and Data Analytics

Module code COM736

Module leader Dr Phoey Teh

Assessment title Assignment 2

Launch date 7th Feb 2023

Submission deadline 9th May 2023

Expected date for return of marks and feedback Students will be given feedback within three working weeks of the assignment deadline. Feedback and grades will be available on Moodle under the Assignments and Feedback section of the Moodle page.

Module outcomes assessed 3. Critically evaluate advanced aspects of data science and data analytics encompassing the principles, research results and commercial application of the technologies.

Assessment weighting 50%

Word count (if relevant) Estimation 1600-2000 Words (NOT including text in tabulations, citation, graphs and references)

Assessment task details - provide a description of the task

Assignment Description

The assignment will entail reading various academic papers, journals and books. You will be writing reports by critically evaluating the adoption of data analytics and the issues.

Task 1: Critically Evaluate the adoption of any one modern data analysis and visualisation technology for any specific practices (e.g.: Medical, Insurance, business or etc) (40%)

As data is being used increasingly to personalise, aggregate, and measure our everyday experiences, there is a growing need for using various models, methods, tools and techniques to convert data into information and knowledge that can be

understood by non-specialists to make informed decisions for governments, companies, and other organisations.

Seeking references from appropriate scholarly/research data sources/e-library (e.g.: ACM or IEEE journal articles), you need to investigate the techniques that had been used in comparison to other research, to evaluate how each of the techniques are applied in different industry (e.g.: medical, business and etc). You are expected to critically evaluate with the example applied in that research and cite them appropriately between your statement in your report. You should discuss the technical part of your data analysis's (later in Task 2) in relevant to the selected technique you have discussed above. You are expected to source at least 7 10 journal articles from the related techniques of your choice in this critical evaluation.

The overall estimated number of words is between 800 - 1000 words.

Task 2: Data Analysis & Visualisation using Oracle Analytics Cloud or another tool of your choice (60%)

Your tasks are:

Data preparation and calculations:

Analysts usually go through a data preparation stage in any data analysis/business analytics activity. Often, raw data is generated in formats that are not immediately amenable to visualisation or comparison. You must consider the two datasets and identify how you can match the two datasets for comparative analysis. Some tasks require you to perform additional calculations/format the data differently, and build a new dataset. If that is necessary, you should do so and submit the revised data and calculations. Calculations can be done using Excel or equivalent. The file containing your calculations must be submitted.

Visualisation of data:

Based on your scenario, provide 2 or 3 meaningful visualisations of your choices. You must give brief comments about the information conveyed by the

visualisation. The visualisations must be downloaded in PDF or MS PowerPoint format from Oracle Analytics Cloud or with a clear snapshot generated/screenshot from your tools (Tableau, R or Python etc.).

c. Data Interpretation and commentary:

It is not enough to create graphs and visualisations. Data interpretation and reporting are critical to decision-making. You must, therefore, separately provide brief comments about what information is conveyed by the visualisation. The comments must be no more than 100 words per visualisation, including discussing the implications of the specific representation.

Overall, your work must make references to the given scenario wherever needed. You should support your views and points with relevant examples and justify your findings and recommendations with appropriate and suitable references. References can be drawn from books, journals, databases, and other quality information resources. IEEE Referencing style must be strictly followed. The overall estimated number of words for task 2 is between 800 - 1000 words.

Submission instructions - What should be the format of the submission? / Where should it be submitted?

Academic integrity is an important part of your learning and assessment. On submission of an assignment, software called Turnitin is automatically used to check the similarity of your work to other sources of information and the work produced by other students. The similarity scores are reviewed by the marking team and any similarity scores that are a cause for concern will be flagged and investigated. By submitting this work, you confirm that you have read, understand and accept the universitys regulations regarding academic integrity

and academic misconduct such as plagiarism and collusion and agree to be subject to the academic integrity process if any such situation should arise.

The assignment should be word-processed

. All Tasks must be completed and submitted as a single file to University VLE (Moodle) by the due date stated above. The Glyndwr policy on assignment submission will be rigidly adhered to (see your Student Handbook).

Hints and tips

You will work on this assignment individually and submit your IEEE-formatted report in a single file to Moodle. In working individually you must not share your ideas, data or other work product with other students.

All submitted work is expected to observe academic standards in terms of referencing, academic writing, use of language etc. Failure to adhere to these instructions may result in your work being awarded a lower grade than it would otherwise deserve.

Your deliverables/submission should include the following:

Marking and moderation

All work is fully marked by the module leader or a team associate. A

representative sample of work from each cohort will be second-marked to ensure quality and consistency.

All required work must be submitted, in full and as directed and described, by the due time and date, to achieve marks reflecting its full worth. Work submitted after the due time and date, but within one calendar week, will be capped at 40%. Work more than a week late will not be marked and will be entered as 0%.

Please study the marking criteria provided to fully understand how marks will be awarded for your work.

Employability Skills Applied

On successful completion of this module, a student will have had opportunities to demonstrate achievement of the following Employability Skills; Tick all that apply.

CORE ATTRIBUTES

Engaged

Creative

Enterprising

Ethical

KEY ATTITUDES Commitment

Curiosity

Resilient

Confidence

Adaptability

PRACTICAL SKILLSETS Digital fluency

Organisation

Leadership and team working

Critical thinking

Emotional intelligence

Communication

Marking Criteria

Task 1 (Overall Weighting 40%)

Learning Outcome 3

TASK Refer <40 C 40-49% B 50-59% B+ 60-69% A >70%

Investigation and critical Evaluation of tools and techniques

Weighting: 20% Inappropriate or inaccurate investigation and/or

critical evaluation A mainly reasonable investigation

and/or critical evaluation. With significant deficits in accuracy or depth A generally good investigation and critical evaluation.

With some minor deficits in accuracy or depth A very good investigation and critical evaluation. With lack of depth in some areas An excellent and professional investigation and critical analysis

Discussion of Technical Aspects of the Data Analysis

Weighting: 20% Inappropriate or inaccurate discussion A mainly reasonable discussion. With significant

deficits in accuracy or depth A generally good discussion.

With some minor deficits in accuracy or depth A very good discussion. With lack of depth in some areas An excellent and professional discussion

Task 2 (Overall Weighting 60%)

Learning Outcome 3

a)Data preparation and calculations

Weighting: 20% Inappropriate or inaccurate, no evidence of references to data source. Provided with URL link to the source of dataset and explanation of reason of

selecting those datasets. Provided with URL link to the source of dataset and good explanation/ approach in preparation Good

sources of

URL link with very good approach to

data preparation with in-depth discussion in areas. Excellent source of IRL link with professional approach of

data preparation and calculations.

and calculation with some minor deficits in accuracy or depth b) Visualisation of data

Weighting 20% Inappropriate or inaccurate visualisation Mainly reasonable with some significant errors Generally good approach.

With some minor deficits in accuracy or depth Very good approach. Lack of depth in areas. Excellent and professional approach

c) Data

Interpretation and commentary

Weighting 20% Inappropriate or inaccurate Mainly reasonable with some significant errors Generally good approach.

With some minor deficits in accuracy or depth Very good approach. Lack of depth in areas. Excellent and professional approach