DataScienceEcosystem.ipynb

datascienceecosystem.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Data Science Tools and Ecosystem"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, Data Science Tools and Ecosystem are summarized."
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "**Objectives:**\n",
        "\n",
        "- List popular languages for Data Science\n",
        "- List common data visualization tools\n",
        "- Introduce basic statistical concepts for data analysis"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "Some of the popular languages that Data Scientists use are:\n",
        "1.   Python\n",
        "2.   R\n",
        "3.   Java\n",
        "\n"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "Some of the commonly used libraries used by Data Scientists include:\n",
        "1.   Numpy\n",
        "2.   Pandas\n",
        "3.   Seaborn\n",
        "\n"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "| Data Science Tools     |\n",
        "|------------------------|\n",
        "| Jupyter Notebook       |\n",
        "| RStudio                |\n",
        "| Apache Zeppelin        |\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "### Below are a few examples of evaluating arithmetic expressions in Python"
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      "cell_type": "code",
      "source": [
        "# This a simple arithmetic expression to mutiply then add integers\n",
        "(3*4)+5"
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      "execution_count": null,
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      "cell_type": "code",
      "source": [
        "# This will convert 200 minutes to hours by dividing by 60.\n",
        "hours = 200 / 60\n",
        "hours"
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    {
      "cell_type": "markdown",
      "source": [
        "## Author\n",
        "Minh-Hoang DANG"
      ],
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