Pythonic Chemistry: The Beginner’s Guide to Digital Chemistry

The Pythonic Chemistry course, part of the SHARP training program for Ph.D. candidates at the Institute of Chemical Research of Catalonia (ICIQ), introduces chemistry students to digital research tools using Python. The goal of the course is to fill the gaps in scientific curricula where programming is not considered at the B.Sc. stage, employing a technology stack that includes Deepnote, Jupyter notebooks, and Docker, along with libraries like NumPy, Pandas, Matplotlib, and Seaborn for data analysis and visualization. Designed as a fast-track course for beginners, it covers basic programming for complex chemical problem-solving. Deepnote’s interactive features enhance collaboration and engagement in lectures and projects. The course has improved students’ programming and data analysis capabilities, receiving positive feedback for its hands-on and collaborative approach. Pythonic Chemistry aims to blend computational techniques with chemistry education, equipping students for future scientific advancements.

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Publication Details

Journal
Journal of Chemical Education
Published
2024-10-23
DOI
https://doi.org/10.1021/acs.jchemed.4c00840
Citations
5
Primary Topic
Genetics, Bioinformatics, and Biomedical Research
Type
article
Field-Weighted Citation Impact
1.17
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article

Pythonic Chemistry: The Beginner’s Guide to Digital Chemistry

Diego Garay‐Ruiz, Javier Heras‐Domingo
5 citations
Journal of Chemical Education
Genetics, Bioinformatics, and Biomedical Research
1.17
article

Pythonic Chemistry: The Beginner’s Guide to Digital Chemistry

Diego Garay‐Ruiz, Javier Heras‐Domingo
article en
5 citations

Abstract

The Pythonic Chemistry course, part of the SHARP training program for Ph.D. candidates at the Institute of Chemical Research of Catalonia (ICIQ), introduces chemistry students to digital research tools using Python. The goal of the course is to fill the gaps in scientific curricula where programming is not considered at the B.Sc. stage, employing a technology stack that includes Deepnote, Jupyter notebooks, and Docker, along with libraries like NumPy, Pandas, Matplotlib, and Seaborn for data analysis and visualization. Designed as a fast-track course for beginners, it covers basic programming for complex chemical problem-solving. Deepnote’s interactive features enhance collaboration and engagement in lectures and projects. The course has improved students’ programming and data analysis capabilities, receiving positive feedback for its hands-on and collaborative approach. Pythonic Chemistry aims to blend computational techniques with chemistry education, equipping students for future scientific advancements.

Journal of Chemical EducationVol. 101(11)
Institut Català d'Investigació Química (ES)
Openalex Percentile: Top 21%
Genetics, Bioinformatics, and Biomedical Research
1.17
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