Getting started guide tweaks (#1664)

* Fixed installation page link in introduction

* Added link to reference gallery in the introduction

* Fixed remaining links in introduction guide

* Fixed typo in tabular datasets getting started guide

examples/getting_started/1-Introduction.ipynb

@@ -44,7 +44,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is the standard way to make the numpy and pandas libraries available in the namespace. We recommend always importing HoloViews as ``hv`` and if you haven't already installed HoloViews, check out our [installation page].\n",
+    "This is the standard way to make the numpy and pandas libraries available in the namespace. We recommend always importing HoloViews as ``hv`` and if you haven't already installed HoloViews, check out the install instructions on our [homepage](www.holoviews.org).\n",
     "\n",
     "Note that after importing HoloViews as ``hv`` we run ``hv.extension('bokeh')`` to load the bokeh plotting extension, allowing us to generate visualizations with [Bokeh](http://bokeh.pydata.org/). In the next section we will see how you can use other plotting libraries such as [matplotlib](http://matplotlib.org) and even how you can mix and match between them.\n",
     "\n",
@@ -91,7 +91,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here we passed our dataframe to [``hv.Scatter``](../reference/elements/elements/bokeh/Scatter.ipynb) to create an *object* called `scatter`, which is independent of any plotting library.  `scatter` is a simple wrapper around our dataframe that knows that the 'services' column is the independent variable, normally plotted along the x-axis, and that the 'ridership' column is a dependent variable, plotted on the y-axis. These are our *dimensions* which we will describe in more detail a little later.\n",
+    "Here we passed our dataframe to [``hv.Scatter``](../reference/elements/elements/bokeh/Scatter.ipynb) to create an *object* called `scatter`, which is independent of any plotting library. HoloViews provides a wide range of Element types, all visible in the [Reference Gallery](http://holoviews.org/reference/index.html).\n",
+    "\n",
+    "In this example, `scatter` is a simple wrapper around our dataframe that knows that the 'services' column is the independent variable, normally plotted along the x-axis, and that the 'ridership' column is a dependent variable, plotted on the y-axis. These are our *dimensions* which we will describe in more detail a little later.\n",
     "\n",
     "Given that we have the handle ``scatter`` on our ``Scatter`` object, we can show that it is indeed an object and not a plot by printing it:"
    ]
@@ -118,7 +120,7 @@
    "source": [
     "## Compositional ``Layouts``\n",
     "\n",
-    "The class [``Scatter``](../reference/elements/elements/bokeh/Scatter.ipynb) is a subclass of ``Element``.  As shown in our [element gallery], Elements are the simplest viewable components in HoloViews. Now that we have a handle on ``scatter``, we can demonstrate the compositionality of these objects:"
+    "The class [``Scatter``](../reference/elements/elements/bokeh/Scatter.ipynb) is a subclass of ``Element``.  As shown in our [element gallery](http://holoviews.org/reference/index.html), Elements are the simplest viewable components in HoloViews. Now that we have a handle on ``scatter``, we can demonstrate the compositionality of these objects:"
    ]
   },
   {
@@ -153,7 +155,7 @@
    "source": [
     "## Array data: taxi dropoffs\n",
     "\n",
-    "So far we have visualized data in a [pandas ``DataFrame``](http://pandas.pydata.org/) but ``HoloViews`` is as agnostic to data formats as it is to plotting libraries; see [XXXX] for more information. This means we can work with array data as easily as we can work with  tabular data. To demonstrate this, here are some [numpy arrays](http://www.numpy.org/) relating to taxi dropoff locations in New York City:"
+    "So far we have visualized data in a [pandas ``DataFrame``](http://pandas.pydata.org/) but ``HoloViews`` is as agnostic to data formats as it is to plotting libraries; see [Customizing Plots](../user_guide/03-Customizing_Plots.ipynb) for more information. This means we can work with array data as easily as we can work with  tabular data. To demonstrate this, here are some [numpy arrays](http://www.numpy.org/) relating to taxi dropoff locations in New York City:"
    ]
   },
   {

examples/getting_started/3-Tabular_Datasets.ipynb

@@ -58,7 +58,7 @@
    "source": [
     "This particular dataset was the subject of an excellent piece of visual journalism in the [Wall Street Journal](http://graphics.wsj.com/infectious-diseases-and-vaccines/#b02g20t20w15). The WSJ data details the incidence of various diseases over time, and was downloaded from the [University of Pittsburgh's Project Tycho](http://www.tycho.pitt.edu/). We can see we have 5 data columns, which each correspond either to independent variables that specify a particular measurement ('Year', 'Week', 'State'), or observed/dependent variables reporting what was then actually measured (the 'measles' or 'pertussis' incidence). \n",
     "\n",
-    "Knowing the distinction between those two types of variables is crucial for doing visualizations, but unfortunately the tabular format does not declare this information. Plotting 'Week' against 'State' would not be meaningful, whereas 'measles' for each 'State' (averaging or summing across the other dimensions) would be fine, and there's no way to deduce those constraints from the tabular format.  Accordingly, we will first make a HoloViews object called a ``Dataset`` that declares the independent variables (called key dimeansions or **kdims** in HoloViews) and dependent variables (called value dimensions or **vdims**) that you want to work with:"
+    "Knowing the distinction between those two types of variables is crucial for doing visualizations, but unfortunately the tabular format does not declare this information. Plotting 'Week' against 'State' would not be meaningful, whereas 'measles' for each 'State' (averaging or summing across the other dimensions) would be fine, and there's no way to deduce those constraints from the tabular format.  Accordingly, we will first make a HoloViews object called a ``Dataset`` that declares the independent variables (called key dimensions or **kdims** in HoloViews) and dependent variables (called value dimensions or **vdims**) that you want to work with:"
    ]
   },
   {