Data analytic
Budget: €12 – €18 EUR
I need to research 2 or more data sets of a topic of any choice using R programming language -R studio.
a) One data set has to be extracted from the web via an API, web crawler, HTML parser or
similar.
b) Pre-process the datasets (e.g., clean them, deal with missing values).
c) Programmatically analyse the combined datasets to provide at least 3 interesting insights into
the data you have chosen. Examples of insights might include visualisations, relationships,
trends/patterns, differences, etc
Report:
Abstract: A roughly 200-word executive summary of the work and the key results.
• Introduction: Set the scene and summarise the objectives (for example what are you trying
to find out).
• Literature Review: Discuss how other people have used the datasets you have chosen or
conducted similar analyses on other datasets. The literature review should be around half a
page
• Data: Discuss why the datasets are suitable for your topic, how the datasets complement each
other, are there alternative datasets you could use? Discuss how the datasets were acquired
(e.g., download, web scraping, APIs, etc.). Present and the main characteristics and structure
of the datasets. Recommended here is to use tables to summarise the main characteristics of
your datasets (e.g., file format, structured/semi-structured/unstructured, size in MB, number
of instances, number of attributes and their type, etc.).
• Methodology: Provide a step-by-step description of how you have applied the KDD or CRISPDM methodology to your work. This section should be broken into separate subsections
corresponding to the different steps of the chosen methodology. Each subsection should
provide a detailed discussion of the methods and techniques that you have applied at that
step of the methodology (e.g., emphasise which particular files/ subsets / attributes you
selected to actually use in the analysis, how did you prepare and transform the data, how did
you analyse the data, etc.).
• Implementation: Discuss (from a technical point of view) how have you built your application
workflow and how automatic it is (i.e., create and include a visual diagram showing your
application workflow/architecture), what technologies (e.g., programming language, API,
database systems) you have used, what components and forms of analytics (e.g., main
packages, functions) you have you used and why? Discuss any technical challenges you faced
and how did you overcome them?
• Results and Findings: Present the results of your analysis (e.g., visualisations) and interpret
the findings in your own words.
a) One data set has to be extracted from the web via an API, web crawler, HTML parser or
similar.
b) Pre-process the datasets (e.g., clean them, deal with missing values).
c) Programmatically analyse the combined datasets to provide at least 3 interesting insights into
the data you have chosen. Examples of insights might include visualisations, relationships,
trends/patterns, differences, etc
Report:
Abstract: A roughly 200-word executive summary of the work and the key results.
• Introduction: Set the scene and summarise the objectives (for example what are you trying
to find out).
• Literature Review: Discuss how other people have used the datasets you have chosen or
conducted similar analyses on other datasets. The literature review should be around half a
page
• Data: Discuss why the datasets are suitable for your topic, how the datasets complement each
other, are there alternative datasets you could use? Discuss how the datasets were acquired
(e.g., download, web scraping, APIs, etc.). Present and the main characteristics and structure
of the datasets. Recommended here is to use tables to summarise the main characteristics of
your datasets (e.g., file format, structured/semi-structured/unstructured, size in MB, number
of instances, number of attributes and their type, etc.).
• Methodology: Provide a step-by-step description of how you have applied the KDD or CRISPDM methodology to your work. This section should be broken into separate subsections
corresponding to the different steps of the chosen methodology. Each subsection should
provide a detailed discussion of the methods and techniques that you have applied at that
step of the methodology (e.g., emphasise which particular files/ subsets / attributes you
selected to actually use in the analysis, how did you prepare and transform the data, how did
you analyse the data, etc.).
• Implementation: Discuss (from a technical point of view) how have you built your application
workflow and how automatic it is (i.e., create and include a visual diagram showing your
application workflow/architecture), what technologies (e.g., programming language, API,
database systems) you have used, what components and forms of analytics (e.g., main
packages, functions) you have you used and why? Discuss any technical challenges you faced
and how did you overcome them?
• Results and Findings: Present the results of your analysis (e.g., visualisations) and interpret
the findings in your own words.