docs: replace "data set" with "dataset" everywhere

docs/tutorial/pytorch.qmd

@@ -28,7 +28,7 @@ pip install 'ibis-framework[duckdb,examples]' ibis-ml skorch torch
 
 ## The New York City flight data
 
-Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This data set contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:
+Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This dataset contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:
 
 ```{python}
 #| output: false
@@ -107,7 +107,7 @@ flight_data = (
 flight_data
 ```
 
-We can see that about 16% of the flights in this data set arrived more than 30 minutes late.
+We can see that about 16% of the flights in this dataset arrived more than 30 minutes late.
 
 ```{python}
 flight_data.arr_delay.value_counts().rename(n="arr_delay_count").mutate(

docs/tutorial/scikit-learn.qmd

@@ -28,7 +28,7 @@ pip install 'ibis-framework[duckdb,examples]' ibis-ml scikit-learn
 
 ## The New York City flight data
 
-Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This data set contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:
+Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This dataset contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:
 
 ```{python}
 #| output: false
@@ -106,7 +106,7 @@ flight_data = (
 flight_data
 ```
 
-We can see that about 16% of the flights in this data set arrived more than 30 minutes late.
+We can see that about 16% of the flights in this dataset arrived more than 30 minutes late.
 
 ```{python}
 flight_data.arr_delay.value_counts().rename(n="arr_delay_count").mutate(

docs/tutorial/xgboost.qmd

@@ -28,7 +28,7 @@ pip install 'ibis-framework[duckdb,examples]' ibis-ml 'xgboost[scikit-learn]'
 
 ## The New York City flight data
 
-Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This data set contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:
+Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This dataset contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:
 
 ```{python}
 #| output: false
@@ -106,7 +106,7 @@ flight_data = (
 flight_data
 ```
 
-We can see that about 16% of the flights in this data set arrived more than 30 minutes late.
+We can see that about 16% of the flights in this dataset arrived more than 30 minutes late.
 
 ```{python}
 flight_data.arr_delay.value_counts().rename(n="arr_delay_count").mutate(

examples/Preprocess your data with recipes.ipynb

@@ -33,7 +33,7 @@
    "source": [
     "## The New York City flight data\n",
     "\n",
-    "Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This data set contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:"
+    "Let's use the [nycflights13 data](https://github.com/hadley/nycflights13) to predict whether a plane arrives more than 30 minutes late. This dataset contains information on 325,819 flights departing near New York City in 2013. Let's start by loading the data and making a few changes to the variables:"
    ]
   },
   {
@@ -317,7 +317,7 @@
    "id": "722b2213-3b84-4f03-9006-59bf72591613",
    "metadata": {},
    "source": [
-    "We can see that about 16% of the flights in this data set arrived more than 30 minutes late."
+    "We can see that about 16% of the flights in this dataset arrived more than 30 minutes late."
    ]
   },
   {