docs(preface)📝: add learning goals section as a new notebook chapter

- Introduce 03-goals.py notebook outlining technical and intellectual goals.
- List technical takeaways: NetworkX, graph visualization, basic algorithms, and math connections.
- Describe intellectual goals: thinking in relationships and developing algorithmic thinking.

Author: ericmjl

notebooks/00-preface/03-goals.py

@@ -0,0 +1,90 @@
+import marimo
+
+__generated_with = "0.9.33"
+app = marimo.App(width="medium")
+
+
+@app.cell(hide_code=True)
+def __(mo):
+    mo.md(
+        r"""
+        Our learning goals for you with this book
+        can be split into the technical and the intellectual.
+        """
+    )
+    return
+
+
+@app.cell(hide_code=True)
+def __(mo):
+    mo.md(
+        r"""
+        ## Technical Takeaways
+
+        Firstly, we would like to equip you to be familiar
+        with the NetworkX application programming interface (API).
+        The reason for choosing NetworkX is because
+        it is extremely beginner-friendly,
+        and has an API that matches graph theory concepts very closely.
+
+        Secondly, we would like to show you how you can visualize graph data
+        in a fashion that doesn't involve showing mere hairballs.
+        Throughout the book, you will see examples of what we call
+        _rational graph visualizations_.
+        One of our authors, Eric Ma, has developed a companion package, `nxviz`,
+        that provides a declarative and convenient API
+        (in other words an attempt at a "grammar")
+        for graph visualization.
+
+        Thirdly, in this book, you will be introduced to basic graph algorithms,
+        such as finding special graph structures,
+        or finding paths in a graph.
+        Graph algorithms will show you how to "think on graphs",
+        and knowing how to do so will broaden your ability to interact with
+        graph data structures.
+
+        Fourthly, you will also be equipped with the connection between graph theory
+        and other areas of math and computing,
+        such as statistical inference and linear algebra.
+        """
+    )
+    return
+
+
+@app.cell(hide_code=True)
+def __(mo):
+    mo.md(
+        r"""
+        ## Intellectual Goals
+
+        Beyond the technical takeaways,
+        we hope to broaden how you think about data.
+
+        The first idea we hope to give you
+        the ability to think about your data
+        in terms of "relationships".
+        As you will learn,
+        relationships are what give rise to the interestingness of graphs.
+        That's where _relational insights_ can come to fore.
+
+        The second idea we hope to give you
+        is the ability to "think on graphs".
+        This comes with practice.
+        Once you master it, though,
+        you will find yourself becoming more and more familiar
+        with **algorithmic thinking**.
+        which is where you look at a problem
+        in terms of the **algorithm** that solves it.
+        """
+    )
+    return
+
+
+@app.cell(hide_code=True)
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()