|immutable||142.59 kB||MIT||10 Years||28 Aug 2023|
|get-stream||6.74 kB||MIT||8 Years||17 Aug 2023|
|formidable||38.07 kB||MIT||12 Years||25 Aug 2023|
|mongoose||614.29 kB||MIT||12 Years||15 Sep 2023|
|pretty-bytes||3.54 kB||MIT||9 Years||4 Feb 2023|
|chart.js||1.16 MB||MIT||9 Years||24 Aug 2023|
|yocto-queue||2.85 kB||MIT||3 Years||12 Aug 2021|
|istanbul-lib-coverage||7.88 kB||BSD-3-Clause||8 Years||18 Oct 2021|
|configstore||3.33 kB||BSD-2-Clause||11 Years||5 Apr 2021|
|data-uri-to-buffer||3.42 kB||MIT||9 Years||5 May 2023|
|env-paths||3.18 kB||MIT||7 Years||27 Aug 2021|
|papaparse||44.81 kB||MIT||9 Years||23 Mar 2023|
|mdn-data||87.52 kB||CC0-1.0||6 Years||29 Aug 2023|
|internal-slot||6.53 kB||MIT||4 Years||9 Feb 2023|
|functional-red-black-tree||9.86 kB||MIT||10 Years||29 Sep 2014|
Data Processing: When you need to transform, filter, aggregate, and otherwise process large datasets, these packages can dramatically simplify tasks that would otherwise be cumbersome to write from scratch.
Statistical Analysis: These packages often contain pre-built functions for statistical operations such as regression, correlation, and variance.
Machine Learning: There are numerous packages available designed to help with the development and training of machine-learning models.
Data Visualization: Many packages also provide functionality for data visualization, simplifying the process of generating graphs, charts, heat maps, etc.
Here are some common functionalities found in many data science libraries:
Data Wrangling: They provide functions to clean and transform raw data into an actionable format. This includes handling missing data, filtering rows, grouping, and reshaping the data.
Mathematical and Statistical Functions: Many packages offer a wide variety of mathematical and statistical functions for data analysis. This can include averaging, standard deviation, max/min values, etc.
Data Visualization: They often provide built-in scripts to produce graphs and charts. These can range from simple pie and line charts to complex 3D visualizations.
Machine Learning: Many packages offer pre-built functions for training machine-learning models, encompassing a wide variety of algorithms from linear regression to neural networks.
Dependency security: One of the major concerns with third-party packages, whether for data science or general purposes, is security. All dependencies should be reviewed for potential security vulnerabilities.
Package Updates: It's vital to keep dependencies up-to-date. However, updates can sometimes break existing functionality, so apply updates with caution.
Data Privacy: If the packages require external APIs for some operations, data privacy could become an issue.
Licence Compliance: Be cautious about the licenses of the packages you use. Some could restrict commercial usage or require publishing your source code.
Understanding the functionalities and potential downfalls of NPM data science packages can aid developers in effectively choosing and using the correct ones for their projects. While they can add robust functionality and help to simplify complex data processing and analysis tasks, one must also take into consideration the associated risks and costs. Understanding the boundaries and effective usage of these dependencies is a step in the right direction for smooth and effective project execution.