diff --git a/4-Data-Science-Lifecycle/15-analyzing/README.md b/4-Data-Science-Lifecycle/15-analyzing/README.md
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--- a/4-Data-Science-Lifecycle/15-analyzing/README.md
+++ b/4-Data-Science-Lifecycle/15-analyzing/README.md
@@ -34,10 +34,10 @@ General querying of the data can help you answer some general questions and theo
 The [`query() `function](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html) in the Pandas library allows you to select columns and receive simple answers about the data through the rows retrieved.
 
 ## Exploring with Visualizations
-You don’t have to wait until the data is thoroughly cleaned and analyzed to start creating visualizations. In fact, having a visual representation while exploring can help identify patterns, relationships, and problems in the data. Furthermore, visualizations provide a means of communication with those who are not involved with managing the data and can be an opportunity to share and clarify additional questions that were not addressed in the capture stage. Refer to the [section on Visualizations](3-Data-Visualization) to learn more about some popular ways to explore visually.
+You don’t have to wait until the data is thoroughly cleaned and analyzed to start creating visualizations. In fact, having a visual representation while exploring can help identify patterns, relationships, and problems in the data. Furthermore, visualizations provide a means of communication with those who are not involved with managing the data and can be an opportunity to share and clarify additional questions that were not addressed in the capture stage. Refer to the [section on Visualizations](/3-Data-Visualization) to learn more about some popular ways to explore visually.
 
 ## Exploring to identify inconsistencies
-All the topics in this lesson can help identify missing or inconsistent values, but Pandas provides functions to check for some of these. [isna() or isnull()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.isna.html) can check for missing values. One important piece of exploring for these values within your data is to explore why they ended up that way in the first place. This can help you decide on what [actions to take to resolve them](2-Working-With-Data\08-data-preparation\notebook.ipynb).
+All the topics in this lesson can help identify missing or inconsistent values, but Pandas provides functions to check for some of these. [isna() or isnull()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.isna.html) can check for missing values. One important piece of exploring for these values within your data is to explore why they ended up that way in the first place. This can help you decide on what [actions to take to resolve them](/2-Working-With-Data/08-data-preparation/notebook.ipynb).
 
 
 ## [Pre-Lecture Quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/27)