notebooks_Create a markdown cell to list data science languages.  (3 pts).ipynb

{
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    }
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  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "## Data Science Languages\n\n1. **Python:** Broadly utilized for its straightforwardness, flexibility, and a wealthy environment of libraries (e.g., NumPy, Pandas, scikit-learn).\n\n2. **R:** popular for measurable examination, information visualization, and machine learning.\n\n3. **SQL:** Basic for overseeing and questioning databases, a pivotal ability for extricating and controlling information.\n\n4. **Julia:** Known for its high-performance computing capabilities, especially in logical and data-intensive applications.\n\n5. **Java:** Utilized in enormous information preparing systems such as Apache Hadoop.\n\n6. **Scala:** Regularly utilized with Apache Start for disseminated information preparing.\n\n7. **MATLAB:** Commonly utilized in the scholarly community and industry for numerical computing and information examination.\n\n8. **SAS:** A program suite utilized for progressed analytics, trade insights, and information administration.\n\nChoose the dialect that best fits your needs and the prerequisites of your information science ventures!\n",
      "metadata": {}
    }
  ]
}