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updates on part 2
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lessons/01_python_data_visualization.ipynb

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"🥊 **Challenge**: Interactive excersise. We'll work through these in the workshop!<br>\n",
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"💡 **Tip**: How to do something a bit more efficiently or effectively.<br>\n",
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"📝 **Poll:** A Zoom poll to help you learn!<br>\n",
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"🎬 **Demo**: Showing off something more advanced – so you know what Python can be used for!<br> \n",
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"\n",
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"### Sections\n",
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"1. [Theory of Data Visualization](#section1)\n",
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"plt.ylabel('Per-Capita GDP (International Dollars)');"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"📝 **Poll:** How to interpret the boxplot with the additional information from the reference line?"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a id='section6'></a>\n",
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"\n",
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"# 2D data: Scatter Plot"
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]
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},
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"cell_type": "code",
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"execution_count": 49,
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"g.ax_joint.set_ylabel('Life Expectancy (Years)');"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a id='section7'></a>\n",
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"\n",
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"# 2D data: Line Plot"
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]
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},
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"cell_type": "code",
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"execution_count": 67,
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"# add a poll to this challenge question -> the goal is to learn and interpret CI;"
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a id='section8'></a>\n",
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"\n",
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"# Subplot"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"g.savefig('../data/challenge.png', dpi=150);"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a id='section9'></a>\n",
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"\n",
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"# Customization"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},

lessons/02_python_data_visualization.ipynb

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lessons/1_Example_Jupyter.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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"version": "3.9.12"
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}
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},
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"nbformat": 4,

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