refine lessons and break up final lesson into 2

Author: jenna-jordan

_episodes/02-data-wrangling.md

@@ -121,7 +121,7 @@ df_final
 Finally, we will export the dataframe to a CSV file.
 
 ~~~
-df_final.to_csv("data/gapminder_tidy.csv", index=False)
+df_final.to_csv("Data/gapminder_tidy.csv", index=False)
 ~~~
 {: .language-python}
 

_episodes/03-create-visualizations.md

@@ -1,6 +1,6 @@
 ---
 title: "Create Visualizations"
-teaching: 15
+teaching: 10
 exercises: 0
 questions:
 - "How can I create an interactive visualization using Plotly Express?"
@@ -15,6 +15,13 @@ keypoints:
 
 Now that our data is in a tidy format, we can start creating some visualizations. Let's start by creating a new notebook (make sure to select the `dataviz` kernel in the Launcher) and renaming it `data_visualizations.ipynb`.
 
+Let's make our first cell into a markdown cell, and give this notebook a title:
+
+~~~
+# Data Visualizations
+~~~
+{: .source}
+
 ## Import our newly tidy data
 
 First, we need to import pandas and Plotly Express, and then read in our dataframe.

_episodes/04-create-streamlit-app.md

@@ -1,5 +1,5 @@
 ---
-title: "Create Streamlit App"
+title: "Create the Streamlit App"
 teaching: 10
 exercises: 0
 questions:
@@ -68,7 +68,7 @@ We can add other text to our app with `st.write()` and other functions.
 Now, let's go ahead and add the visualization of GDP in Oceania that we created in the previous lesson. We can copy and paste the code over from our Jupyter Notebook - but leave out the `fig.show()`. We're going to use a different function to display the plot in the Streamlit app: `st.plotly_chart()`
 
 ~~~
-df = pd.read_csv("data/gapminder_tidy.csv")
+df = pd.read_csv("Data/gapminder_tidy.csv")
 df_gdp_o = df.query("continent=='Oceania' & metric=='gdpPercap'")
 
 title = "GDP for countries in Oceania"
@@ -93,7 +93,7 @@ import plotly.express as px
 st.set_page_config(layout="wide")
 st.title("Interact with Gapminder Data")
 
-df = pd.read_csv("data/gapminder_tidy.csv")
+df = pd.read_csv("Data/gapminder_tidy.csv")
 df_gdp_o = df.query("continent=='Oceania' & metric=='gdpPercap'")
 
 title = "GDP for countries in Oceania"
@@ -107,7 +107,7 @@ You know the drill! Save, switch over to the Streamlit app, and click "Rerun".
 We now have a web application that can allow you to share your interactive visualizations.
 
 > ## Share your app online
-> Right now, our app only lives on our computer. Like Jupyter Lab, the app is displaying in a web browser but has the URL `localhost:####` (where #### represent the port number).
+> Right now, our app only lives on our computer. Like Jupyter Lab, the app is displaying in a web browser but has the URL `localhost:####` (where #### represents the port number).
 > To easily make this app public and shared online, you can sign up for a [Streamlit Sharing](https://streamlit.io/sharing-sign-up) account. This will let you share up to 3 apps. 
 > All you need (in addition to an account) is to have your code in a public GitHub repo.
 > Detailed instructions can be found in [Streamlit's Documentation](https://docs.streamlit.io/en/stable/deploy_streamlit_app.html)