Writing a Data Science Essay: Requirements and Guidelines

Purpose of the Assignment

The internet has countless articles on data science use cases, reports, tutorials, competition entries, etc. Your task is to write a short essay on a post of your choice. The essay should cover a topic you want to learn more about. You must pick an article from one of these outlets:

Submission Requirements  

1.Your essay in PDF format

2.Your essay in Word docx format.

The content of these must be identical.

This is an individual assignment. You are not allowed to share your work with others. Your submission will be checked using Turnitin and suspected cases of collusion and/or plagiarism will be reported.

The usual penalties for late submissions apply and will be applied automatically. The automatic lateness penalty will kick in at the time of the designated submission deadline, no exceptions. Please make sure you submit sufficiently long ahead of the deadline, since even one second over the specified deadline will incur the full lateness penalty of 10% per day. See the module handbook for details. 

Required content and marking scheme

This assignment is worth 30 % of the overall assessment for this module.

This assignment has a maximum score of 20 marks.

Required content of the essay:

·State the purpose of the blog post/report of your choice: Report, educational, competition entry etc. (2 marks).

·State your personal motivation why you have chosen this post (2 marks). 

·Summarize the data science problem that is being addressed, or, in case of educational posts, the data science technique that is explained (2 marks).

·List the techniques and tools that have been used (4 marks).

oTechniques are the actual methods, e.g. linear regression, deep learning, Principal component analysis etc.

oTools are the software packages used, e.g. numpy, scipy, scikit-learn, or Java/Hadoop

·Summarise the outcome of the data science campaign (if report or competition entry), or the gained knowledge (if educational blog post) (2 marks).

·Critically discuss whether you think the chosen techniques and tools are appropriate (2 marks).

Remarks

·You must use a blog post from the the provided sources and specify which source you used. Using a different source will lead to downmarking by up to -7 marks. 

·Add a headline and your name on the top. Add a reference section to the end. The reference section must contain the full link to the blog post to , If the link to the blog post is missing or wrong your essay will be downmarked by up to 7 marks. 

·Write in a clear and coherent style. An essay lacking coherence and/or clarity will be downmarked by up to -7 marks.

·Do not plagiarise the blog post in your essay.  Any instance of plagiarism, such as copied sentences, or parts of sentences, even with nouns replaced with synonyms, will incur downmarking by up to -14 marks.

·The total penalty for the content, coherence and clarity is -14 marks.

·You must add a word count to the end of your essay. A missing word count will incur -3 marks. Headline, your name, the reference section and the word count itself are excluded from the word count. 

·Adhere to the word count of 450 words +/- 10 %. No penalty will be incurred if the word count is between 405 and 495. Word counts beyond these limits will incur a penalty of -6 marks.

·Use a spell and grammar checker. Reductions for grammar and spelling errors: -1 mark per error, max -6 marks.

This criterion is linked to a learning outcomeWhy have you chosen this post

Your personal reasoning behind choosing this post

This criterion is linked to a learning outcomeData science problem or topic covered

Summarize the data science problem that is being addressed, or if educational the topic that is covered.

This criterion is linked to a learning outcomeList the techniques and tools that have been used

Techniques are the actual methods, e.g. linear regression, deep learning, Principal component analysis etc.

Tools are the software packages used, e.g. numpy, scipy, scikit-learn, or Java/Hadoop

This criterion is linked to a learning outcomeoutcome of the data science campaign or gained knowledge

Summarise the outcome of the data science campaign (if report or competition entry), or the gained knowledge (if educational blog post)

This criterion is linked to a learning outcomeCritical discussion

Critically discuss whether you think the chosen techniques and tools are appropriate