Abstract
The daily work in data science involves a set of essential tools: the programming languages Python and R, the version control tool Git and the virtualization tool Docker. Proficiency in at least one programming language is required for data science. R is tied to a computing environment that focuses on statistics, in which many new algorithms in genomics and biomedicine are first published. Python has a root in system administration, and is a superb language for general programming. Version control is critical to managing complex projects, even if software development is not involved. Docker container is becoming a key tool for deployment, portability, and reproducibility. This chapter provides a self-contained practical guide of these topics so that readers can use it as a reference and to plan their training.
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This work has been funded in part by the US National Institutes of Health via grants UH2 AI132345 (Li), U2C ES030163 (Jones, Li, Morgan, Miller), U01 CA235493 (Li, Xia, Siuzdak), U2C ES026560 (Miller), P30 ES019776 (Marsit), P50 ES026071 (McCauley), and the US EPA grant 83615301 (McCauley).
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Pittard, W.S., Li, S. (2020). The Essential Toolbox of Data Science: Python, R, Git, and Docker. In: Li, S. (eds) Computational Methods and Data Analysis for Metabolomics. Methods in Molecular Biology, vol 2104. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0239-3_15
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DOI: https://doi.org/10.1007/978-1-0716-0239-3_15
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Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-0238-6
Online ISBN: 978-1-0716-0239-3
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