diff --git a/docs/dagster-university/pages/dagster-essentials/lesson-3/asset-materialization.md b/docs/dagster-university/pages/dagster-essentials/lesson-3/asset-materialization.md
index 46638c1e5d94b..4a2f78b71085c 100644
--- a/docs/dagster-university/pages/dagster-essentials/lesson-3/asset-materialization.md
+++ b/docs/dagster-university/pages/dagster-essentials/lesson-3/asset-materialization.md
@@ -33,7 +33,7 @@ def taxi_trips_file():
 
    Using the `month_to_fetch` variable, the URL to retrieve the file from becomes: `https://.../trip-data/yellow_tripdata_2023-03.parquet`
 
-4. Next, the path of the file will be stored at is constructed. The value of `TAXI_TRIPS_TEMPLATE_FILE_PATH`, stored in your project’s `assets/constants.py` file, is retrieved: `data/raw/taxi_trips_{}.parquet`
+4. Next, the path where the file will be stored is constructed. The value of `TAXI_TRIPS_TEMPLATE_FILE_PATH`, stored in your project’s `assets/constants.py` file, is retrieved: `data/raw/taxi_trips_{}.parquet`
 
 5. The parquet file is created and saved at `data/raw/taxi_trips_2023-03.parquet`
 
diff --git a/docs/dagster-university/pages/dagster-essentials/lesson-3/coding-practice-taxi-zones-file-asset.md b/docs/dagster-university/pages/dagster-essentials/lesson-3/coding-practice-taxi-zones-file-asset.md
index de776fcdc9336..99a62515d5f8f 100644
--- a/docs/dagster-university/pages/dagster-essentials/lesson-3/coding-practice-taxi-zones-file-asset.md
+++ b/docs/dagster-university/pages/dagster-essentials/lesson-3/coding-practice-taxi-zones-file-asset.md
@@ -30,6 +30,6 @@ def taxi_zones_file():
         "https://data.cityofnewyork.us/api/views/755u-8jsi/rows.csv?accessType=DOWNLOAD"
     )
 
-    with open("data/raw/taxi_zones.csv", "wb") as output_file:
+    with open(constants.TAXI_ZONES_FILE_PATH, "wb") as output_file:
         output_file.write(raw_taxi_zones.content)
 ```
diff --git a/docs/dagster-university/pages/dagster-essentials/lesson-6/coding-practice-refactoring-assets.md b/docs/dagster-university/pages/dagster-essentials/lesson-6/coding-practice-refactoring-assets.md
index 991d078b65fdb..0b70501fc241f 100644
--- a/docs/dagster-university/pages/dagster-essentials/lesson-6/coding-practice-refactoring-assets.md
+++ b/docs/dagster-university/pages/dagster-essentials/lesson-6/coding-practice-refactoring-assets.md
@@ -56,8 +56,7 @@ Update the imports in `assets/metrics.py` to the following:
 
 ```python {% obfuscated="true" %}
 import requests
-from dagster_duckdb
-import DuckDBResource
+from dagster_duckdb import DuckDBResource
 from . import constants
 from dagster import asset
 ```
diff --git a/docs/dagster-university/pages/dagster-essentials/lesson-6/overview.md b/docs/dagster-university/pages/dagster-essentials/lesson-6/overview.md
index cf5d97f7a8ced..49977548a43f7 100644
--- a/docs/dagster-university/pages/dagster-essentials/lesson-6/overview.md
+++ b/docs/dagster-university/pages/dagster-essentials/lesson-6/overview.md
@@ -8,7 +8,7 @@ lesson: '6'
 
 In previous lessons, you learned about assets, how to connect assets to represent a data pipeline, and how to start a run that materializes the assets.
 
-Dagster’s role is to be the single pane of glass across all data pipelines in an organization. To do make this possible, Dagster needs to know about the services and systems used in your data pipelines, such as cloud storage or a data warehouse. In this lesson, we’ll show you how to accomplish this using software engineering best practices.
+Dagster’s role is to be the single pane of glass across all data pipelines in an organization. To make this possible, Dagster needs to know about the services and systems used in your data pipelines, such as cloud storage or a data warehouse. In this lesson, we’ll show you how to accomplish this using software engineering best practices.
 
 With this in mind, the best practice we’ll focus on in this lesson is called **Don’t Repeat Yourself**, or **DRY** for short. This principle recommends that engineers do something once and only once, thereby reducing duplication and redundancy. By being intentional and writing DRY code, you can reduce the number of bugs, increase the ability to understand the project’s codebase, and improve observability over how logic and functionality are used.