The aim of this assignment is to demonstrate your learning of key elements of data analytics and to build your knowledge

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Data Analytics 

Contribution towards course module: 100% (and scored out of 100 marks)

Keep a copy of all submitted coursework – i.e. your computer files. When you submit your coursework do take a screenshot of the completion screen as your receipt. Remember plagiarism is a serious offense – ensure that you use your own words even when referencing a source. Also reference libraries, tutorials or code that you used.

Learning Outcomes: To recognise and illustrate understanding of data analytics, modelling, data science workflows and pipelines. To operate integrated development environments for statistical computing as well as the practical use of statistical programming languages. Use intuition and complex reasoning skills for synthesising, exploring and understanding concepts in data analytics. To practice exercising research skills such as report writing and the articulation of complex ideas

Practical Data Analytics Report

Assignment Brief

Using R Studio, you are to carry out data analytics of an openly available dataset e.g. from UCI or an Open Data repository such as Open Data NI, and write a report detailing your methodology and analysis of this open dataset showing results and data visualisations. 

The aim of this assignment is to demonstrate your learning of key elements of data analytics and to build your knowledge of what is possible using R. Each week you should be able to incorporate additional components into your report in line with your practical work, lectures and directed reading. As the weeks progress, retain the analysis you have undertaken in earlier weeks, so the report and R script is your cumulative work. You will receive feedback within 20 working days.

Report 

You are required to submit a data analytics report not exceeding 3500 words. The report will consist of 5 sections: 1) Introduction, 2) Methods, 3) Results, 4) Discussion and 5) Conclusions.  Within each section of the report you should describe the technical details of the analysis and the techniques used, and in some cases describing the R code you developed. Further detail and examples are provided in Table 1. Please include references to any research articles and/or online resources that helped you during the data analysis

Table 1: Example details for each section in report

Introduction Provide an introduction to the chosen domain and dataset.  For example, where the data was obtained from, what data is contained in the set. The size of the dataset in terms of instances and features. What are the classes of the dataset. Descriptive statistics, data visualisation, exploratory data analysis etc.

Methods Provide detail on the data analytics process you have undertaken, e.g. data cleansing and preparation, feature selection, data modelling techniques (such as K-NN, Bayesian) and your rationale for selecting these along with code snippets.

Results Application of methods and metrics for model evaluation (e.g. k-fold cross validation, sensitivity, specificity, F-measure, receiver operator characteristic analysis and area under the curve). Visualization of results.

Discussion Provide a summary of the various results that you have obtained from the analysis and what these tell us.

Conclusion Overview of the overall data analytical process undertaken and the interpretation of the results. Any limitations noted or changes you could make in future analysis e.g. boosting algorithms etc.

Submission

The report will be electronically submitted online using Blackboard along with your R script file as an appendix (script file/s do NOT count towards the word limit). You will be given an individual mark based on the assessment criteria

Attachments:

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