Data Science Tools

Owned by Teddy
Last updated: Mar 09, 2020
3 min read

Loro

Website: https://www.freethink.com/articles/assistive-devices

Type: Hardware

Accessibility: To be determined

Features:

  • Assists disabled persons to communicate and navigate

Teachable Machine

Website: https://teachablemachine.withgoogle.com/

Type: Software Program

Accessibility: To be determined

Features:

  • Image classification 
  • Sound classification

Python

Website: https://www.python.org/

Type: Programming Language

Accessibility: To be determined

Features:

  • Simple and consistent
  • Extensive selection of libraries and frameworks:
    • PyTorch 
    • Keras, TensorFlow, and Scikit-learn for machine learning
    • NumPy for high-performance scientific computing and data analysis
    • SciPy for advanced computing
    • Pandas for general-purpose data analysis
    • Seaborn for data visualization
  • Platform independence
  • Great community and popularity

R

Website: https://www.r-project.org/

Type: Programming Language

Accessibility: To be determined

Features:

  • Well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

  • Has an effective data handling and storage facility,

  • Provides a suite of operators for calculations on arrays, lists, vectors and matrices.

  • Provides a large, coherent and integrated collection of tools for data analysis.

  • Provides graphical facilities for data analysis and display either directly at the computer or printing at the papers.

Julia

Website: https://julialang.org/

Type: Programming Language

Accessibility: To be determined

Features:

  • Multiple dispatch: providing ability to define function behavior across many combinations of argument types
  • Dynamic type system: types for documentation, optimization, and dispatch
  • Good performance, approaching that of statically-typed languages like C
  • A built-in package manager
  • Lisp-like macros and other metaprogramming facilities
  • Call Python functions: use the PyCall package[b]
  • Call C functions directly: no wrappers or special APIs
  • Powerful shell-like abilities to manage other processes
  • Designed for parallel and distributed computing
  • Coroutines: lightweight green threading
  • User-defined types are as fast and compact as built-ins
  • Automatic generation of efficient, specialized code for different argument types
  • Elegant and extensible conversions and promotions for numeric and other types
  • Efficient support for Unicode, including but not limited to UTF-8

Jupyter Notebook

Website: https://jupyter.org/

Type: Notebook

Accessibility: To be determined

Features:

  • In-browser editing for code, with automatic syntax highlighting, indentation, and tab-completion/introspection.

  • The ability to execute code from the browser, with the results of computations attached to the code which generated them.

  • Displaying the result of computation using rich media representations, such as HTML, LaTeX, PNG, SVG, etc. For example, publication-quality figures rendered by the matplotlib library, can be included inline.

  • In-browser editing for rich text using the Markdown markup language, which can provide commentary for the code, is not limited to plain text.

  • The ability to easily include mathematical notation within markdown cells using LaTeX, and rendered natively by MathJax.

Jupyter Lab

Website: https://jupyterlab.readthedocs.io/en/stable/

Type: Notebook

Accessibility: To be determined

Features:

  • Enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner.

  • You can arrange multiple documents and activities side by side in the work area using tabs and splitters.

  • Documents and activities integrate with each other, enabling new workflows for interactive computing, for example:

    • Code Consoles provide transient scratchpads for running code interactively, with full support for rich output. A code console can be linked to a notebook kernel as a computation log from the notebook, for example.

    • Kernel-backed documents enable code in any text file (Markdown, Python, R, LaTeX, etc.) to be run interactively in any Jupyter kernel.

    • Notebook cell outputs can be mirrored into their own tab, side by side with the notebook, enabling simple dashboards with interactive controls backed by a kernel.

    • Multiple views of documents with different editors or viewers enable live editing of documents reflected in other viewers. For example, it is easy to have live preview of Markdown, Delimiter-separated Values, or Vega/Vega-Lite documents.


  • JupyterLab also offers a unified model for viewing and handling data formats.

R Studio

Website: https://rstudio.com/

Type: Integrated Development Environment

Accessibility: To be determined

Features:

  • Access RStudio locally
  • Syntax highlighting, code completion, and smart indentation
  • Execute R code directly from the source editor
  • Quickly jump to function definitions
  • Easily manage multiple working directories using projects
  • Integrated R help and documentation
  • Interactive debugger to diagnose and fix errors quickly
  • Extensive package development tools

Ancile Privacy Project

Website: https://ancile-project.github.io/

Type: Framework

Accessibility: To be determined

Features:

  • enforces use-based privacy for applications wishing to access users' personal data.

Small Data Lab

Website: https://smalldata.io/

Type: Collective of data privacy projects

Accessibility: To be determined

Features:

  • Immersive Recommendation: New user-centric recommendation framework that incorporates cross-platform and diverse personal digital traces into recommendations.
  • Ancile: The Ancile Project is developing a new software platform for managing microscale data in a privacy-sensitive manner.  
  • Retrospective Data Learning: By analyzing personal retrospective data traces, we aim to learn temporal patterns and deviations that reveal individual behaviour patterns.
  • Research Stack: This SDK and UX framework for building research study apps on Android and iOS supports scientific research.