Part I-A Data Exploration and Contextualisation (10 points)

•   Commence your assessment by thoroughly explaining your chosen dataset to provide an in-depth understanding of its structure, variables, and contextual relevance.

•   Provide an exhaustive overview of the dataset, inspecting for missing values, means, and standard deviations. You are expected to interpret those descriptive findings.

•   Articulate  the  rationale  behind  your  selection  of  this  dataset,   emphasising  its significance from a social science perspective.

Part I-B Data Processing and Functional Programming (25 points)

•   Using  libraries  such  as  Tidyverse  packages  (e.g.,  dplyr),  you  will  execute  data- wrangling operations (at least 3 operations: selecting variables; recoding variables; dealing with NAs; creating new variables; reshaping data frames; etc.) on the dataset.

•   Methodically  document  each  step  of  your  data  wrangling  process  in  the  report, elucidating the rationale behind every decision.

•   Provide code snippets complemented by explanatory comments, ensuring that your data wrangling procedures are both transparent and reproducible.

•   You should write at least one function for one of the data wrangling tasks. Please clarify the need of a function to be written and rationale for your approach in the report.

Assessment Part II

Part II-A Data Visualisation and Functional Programming (25 points)

•   Execute a minimum of three insightful data visualisations using libraries like ggplot2, derived from your refined dataset.

•   Offer clear and concise interpretations of each visualisation in the report, elucidating any emerging trends, patterns, or noteworthy observations.

•   Elaborate  on  the   specific  visualisations   chosen,  justifying  their   selection   and elucidating their contributions to a deeper comprehension of the dataset in the report.

•   You should write at least one function for one of the data visualisations. Please clarify the need of a function to be written and rationale for your approach in the report.

Part II-B Reproducibility (20 points)

•   Uphold the principles of reproducibility by  sharing your replication materials  in the compressed (zipped) folder, encompassing all pertinent code and a meticulously crafted

README.md  file.  Prioritise  the  meticulous  documentation  of  your   codebase, rendering it accessible and comprehensible for potential replication.

Part II-C Critical Engagement with AI: ChatGPT (20 points)

You are expected to provide your reflections based on the points below in the report.

•   Embrace ChatGPT as a collaborative tool in your computational process, soliciting its code refinement in data wrangling and visualisation.

•   Engage in a critical evaluation of ChatGPT's  contributions to your project within a dedicated section of your report:

•   Analyse the value-added by ChatGPT, highlighting instances where it offered insightful perspectives, refined code, or innovative problem-solving approaches.

•   Dilate  upon  any  constraints  or  challenges  that  surfaced  during  interactions  with ChatGPT, contributing to a well-rounded assessment.

•   Reflect upon how ChatGPT impacted your assessment's trajectory, shaping its outcome and affecting your overall learning experience.

AI-Usage Policy in This Assessment:

In this module/assignment, students are permitted to use only ChatGPT for specific defined processes within the assessment.

This can be utilised to enhance and support the development of specific skills in specific ways, as specified by the module leader and required by the assessment. As per the requirements, for instance,  students  are  asked  to  use  ChatGPT  for  critically  evaluating  their  code  in  data wrangling and visualisation operations in this assessment. In doing so, students are expected to highlight  instances  where  ChatGPT  offers  insightful  solutions,  refined  code,  or  worsens student’s solutions and code.

Except critical engagement with ChatGPT through code refinement, this module prohibits all other use of artificial intelligence (AI), including large language models, to author or co-author formative or  summative work. This prohibition includes the  following practices  and  any practices similar to them:

•   Writing parts or all of an assessment;

•   Generating outlines, structures and high-level arguments for essays;

•   Rewriting or paraphrasing text from other sources for use in written work.

Language and writing review are not prohibited, defined as having a third-party or software check areas of academic writing such as structure, fluency, presentation, grammar, spelling, punctuation,  and  language  translation.  However,  language  review  may  be  considered Academic Misconduct if substantive changes to content have been made by the reviewer or software or at their recommendation, which would suggest that the reviewer or software had either produced or determined the substantive content of the work.

Including content generated by AI tools will not be considered academic misconduct only if it is clearly signposted (by, for example, quotation marks) and attributed (by including a reference to the tool and date of use). However, similarly to quoting Wikipedia, quoting an AI system is unlikely to be a valuable addition to your work and unless clearly relevant to an argument may negatively impact the perceived quality of your work.