diff --git a/docs/quick-start.md b/docs/quick-start.md
index a85e5b28a6996..200b97230e866 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -153,7 +153,7 @@ This first maps a line to an integer value and aliases it as "numWords", creatin
 One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can implement MapReduce flows easily:
 
 {% highlight python %}
->>> wordCounts = textFile.select(explode(split(textFile.value, "\s+")).as("word")).groupBy("word").count()
+>>> wordCounts = textFile.select(explode(split(textFile.value, "\s+")).alias("word")).groupBy("word").count()
 {% endhighlight %}
 
 Here, we use the `explode` function in `select`, to transfrom a Dataset of lines to a Dataset of words, and then combine `groupBy` and `count` to compute the per-word counts in the file as a DataFrame of 2 columns: "word" and "count". To collect the word counts in our shell, we can call `collect`: