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📘 Home Assignment for the Data Scientist Position (Curves) at Argus Media Group

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argus

Project Status: Inactive – The project has reached a stable, usable state but is no longer being actively developed; support/maintenance will be provided as time allows. License: CC0-1.0

Overview

This report presents a fully reproducible data pipeline developed as part of a home assignment for the Data Scientist position (Curves) at the Argus Media Group, a leading independent provider of global energy and commodity market intelligence.

You can access the report here.

The second part of this assignment—a Shiny application—can be accessed here, with its code repository available here.

Data Availability

The data used in this report were provided by the Argus Media Group as part of the home assignment for the Data Scientist position. The original dataset is available here.

How to Use

The pipeline was developed using the Quarto publishing system and the R programming language. To ensure consistent results, the renv package is used to manage and restore the R environment.

After installing the three dependencies mentioned above, follow these steps to reproduce the analyses:

  1. Clone this repository to your local machine.
  2. Open the project in your preferred IDE.
  3. Restore the R environment by running renv::restore() in R. This will install all required software dependencies.
  4. Open index.qmd and run the code as described in the report.

How to Cite

To cite this work, please use the following format:

Vartanian, D. (2025). Argus Media: home assignment — Data scientist, Curves [Report]. https://danielvartan.github.io/argus

A BibTeX entry for LaTeX users is

@techreport{vartanian2025,
  title = {Argus Media: Home assignment — Data scientist, Curves},
  author = {{Daniel Vartanian}},
  year = {2025},
  address = {São Paulo},
  langid = {en},
  url = {https://danielvartan.github.io/argus}
}

License

This content is licensed under CC0 1.0 Universal, placing these materials in the public domain. You may freely copy, modify, distribute, and use this work, even for commercial purposes, without permission or attribution.