Critical analysis essay on data science

Critical analysis essay on data science

Your task is to select and write an essay on an existing Data Science application study in a domain of
your preference: Science, Medicine, Public Health, Society, Sports, Government, Industry, Finance,
Commerce, etc. Notice that it is not expected that you conduct the study, instead, you should select
an adequate application, based on a paper published in a high-quality journal.
The list of allowed Journals is given here: Week 0: Getting Ready for the Module
Guidelines for Selecting a Paper on Critical analysis essay on data science
There is no constraint on the size and structure of the dataset(s) employed in your selected case
study. However, it is expected that the study is based on a large and complex dataset. The study
should be evidence-based and ideally contain insights and value extracted from data.
In summary, the idea is to select an application Case Study. There has to be a clear application
domain and a dataset of substantial size is analysed.
Do not select papers that:
Are mainly a survey or review of the literature
Use a small data set (smaller than 100)
Focus mainly on reporting on or proposing a new methodology
When reading the methodology used in the paper it can help to consider the following:
Do they explain clearly exactly which data they collected?
Do they explain clearly exactly what analysis they performed on those data?
Essay Structure and Guidelines
Part 1 Summary of the Case Study Critical analysis essay on data science
04/11/2020 Critical Essay on a Data Science Case Study
https://canvas.stir.ac.uk/courses/7988/assignments/48998 2/3
Summarise the 5 stages of the Data Science process as described below, writing a maximum of 300
words for each stage. If you feel you need to write more about one stage than the other, it is possible,
as soon as you do not exceed the maximum of 1,500 words.
1. The problem addressed: some background and motivation, the problem(s) or question(s)
addressed, the aims of the study. What type of question(s), according to the types discussed in
the lectures, is/are addressed?
2. Data acquisition and preparation: the sources of the data and how it was collected. Any
preprocessing, data cleaning and data transformation conducted. You can also describe the size
and format of the dataset(s) whenever possible.
3. Data modelling and/or analysis: any modelling technique, machine learning and/or statistical
method used. How the method was evaluated.
4. Results: how the results of the study were presented (tables, figures), you can copy and paste
some tables and figures of the paper (they do not add to the word count) and discuss them, or you
can not include them and instead refer fo their number in the original article. What is the
punchline, the main results, the part of the analysis that offers the biggest value?
5. Conclusions, actions or decision making: What are the main insights gained? Discuss
whether the insights gained were turned into action, or whether suggestions for future decisionmaking are considered in the study
Word Count Part 1: max. 1500 words (approx. 5×300)
Part 2 Critical Assessment of the Case Study
This part should contain your critical analysis and reflections of the case study. As much as possible,
you should give evidence to support your critical views, in doing so, you can consider and cite other
references to the literature.
Structure this part in the following sub-sections, writing a maximum of 250 words for each part. If you
feel you need to write more about one part than the other, it is possible, as soon as you do not
exceed the maximum of 1,000 words.
1. Strengths: what are the main strengths or positive aspects of the study.
2. Originality and novelty: is the study novel/original, in what way?
3. Concerns and limitations: what are the weaknesses or limitations of the study. You can look at
limitations or concerns across different stages of the data science process.
4. Potential societal impact: what are the implications of the research conducted. Are there any
potential benefits to individuals, organisations and/or nations, that can be derived from this
research

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