diff --git a/TimeSeries/1-Introduction/README.md b/TimeSeries/1-Introduction/README.md
index 538dd84e0..4e92e5fe7 100644
--- a/TimeSeries/1-Introduction/README.md
+++ b/TimeSeries/1-Introduction/README.md
@@ -146,4 +146,4 @@ Make a list of all the industries and areas of inquiry you can think of that wou
 
 Although we won't cover them here, neural networks are sometimes used to enhance classic methods of Time Series Forecasting. Read more about them [in this article](https://medium.com/microsoftazure/neural-networks-for-forecasting-financial-and-economic-time-series-6aca370ff412)
 
-**Assignment**: [Assignment Name](assignment.md)
+**Assignment**: [Visualize some more Time Series](assignment.md)
diff --git a/TimeSeries/1-Introduction/assignment.md b/TimeSeries/1-Introduction/assignment.md
index d4badb79f..967ce8d59 100644
--- a/TimeSeries/1-Introduction/assignment.md
+++ b/TimeSeries/1-Introduction/assignment.md
@@ -1,9 +1,11 @@
-# [Assignment Name]
+# Visualize some more Time Series
 
 ## Instructions
 
+You've begun to learn about Time Series Forecasting by looking at the type of data that requires this special modeling. You've visualized some data around energy. Now, look around for some other data that would benefit from Time Series Forecasting. Find three examples (try [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog/?WT.mc_id=academic-15963-cxa)) and create a notebook to visualize them. Notate any special characteristics they have (seasonality, abrupt changes, or other trends) in the notebook.
+
 ## Rubric
 
-| Criteria | Exemplary | Adequate | Needs Improvement |
-| -------- | --------- | -------- | ----------------- |
-|          |           |          |                   |
+| Criteria | Exemplary                                              | Adequate                                             | Needs Improvement                                                                         |
+| -------- | ------------------------------------------------------ | ---------------------------------------------------- | ----------------------------------------------------------------------------------------- |
+|          | Three datasets are plotted and explained in a notebook | Two datasets are plotted and explained in a notebook | Few datasets are plotted or explained in a notebook or the data presented is insufficient |