diff --git a/lectures/_config.yml b/lectures/_config.yml
index fccfa67a..e4a8a23d 100644
--- a/lectures/_config.yml
+++ b/lectures/_config.yml
@@ -33,7 +33,7 @@ sphinx:
     html_theme_options:
       authors:
         - name: Thomas J. Sargent
-          url: https://www.tomsargent.com/
+          url: http://www.tomsargent.com/
         - name: John Stachurski
           url: https://johnstachurski.net/      
       dark_logo: quantecon-logo-transparent.png
diff --git a/lectures/about_py.md b/lectures/about_py.md
index fdd22c7f..96e8f1a1 100644
--- a/lectures/about_py.md
+++ b/lectures/about_py.md
@@ -311,7 +311,7 @@ But when we want to work with larger arrays in real programs we need more effici
 For this we need to use libraries for working with arrays.
 
 For Python, the most important matrix and array processing library is
-[NumPy](https://www.numpy.org/) library.
+[NumPy](https://numpy.org/) library.
 
 For example, let's build a NumPy array with 100 elements
 
@@ -365,7 +365,7 @@ This lecture series will provide you with extensive background in NumPy.
 
 ### SciPy
 
-The [SciPy](https://www.scipy.org) library is built on top of NumPy and provides additional functionality.
+The [SciPy](https://scipy.org/) library is built on top of NumPy and provides additional functionality.
 
 (tuple_unpacking_example)=
 For example, let's calculate $\int_{-2}^2 \phi(z) dz$ where $\phi$ is the standard normal density.
diff --git a/lectures/numpy.md b/lectures/numpy.md
index 62defc8c..d707ead5 100644
--- a/lectures/numpy.md
+++ b/lectures/numpy.md
@@ -1165,7 +1165,7 @@ np.linalg.inv(A)           # Compute the inverse
 ```{index} single: Python; SciPy
 ```
 
-Much of this functionality is also available in [SciPy](https://www.scipy.org/), a collection of modules that are built on top of NumPy.
+Much of this functionality is also available in [SciPy](https://scipy.org/), a collection of modules that are built on top of NumPy.
 
 We'll cover the SciPy versions in more detail {doc}`soon <scipy>`.
 
diff --git a/lectures/pandas.md b/lectures/pandas.md
index 46bebb31..3d2c809d 100644
--- a/lectures/pandas.md
+++ b/lectures/pandas.md
@@ -47,7 +47,7 @@ Here's a popularity comparison over time against Matlab and STATA courtesy of St
 :scale: 100
 ```
 
-Just as [NumPy](https://www.numpy.org/) provides the basic array data type plus core array operations, pandas
+Just as [NumPy](https://numpy.org/) provides the basic array data type plus core array operations, pandas
 
 1. defines fundamental structures for working with data and
 1. endows them with methods that facilitate operations such as
@@ -499,9 +499,9 @@ plt.show()
 
 Python makes it straightforward to query online databases programmatically.
 
-An important database for economists is [FRED](https://research.stlouisfed.org/fred2/) --- a vast collection of time series data maintained by the St. Louis Fed.
+An important database for economists is [FRED](https://fred.stlouisfed.org/) --- a vast collection of time series data maintained by the St. Louis Fed.
 
-For example, suppose that we are interested in the [unemployment rate](https://research.stlouisfed.org/fred2/series/UNRATE).
+For example, suppose that we are interested in the [unemployment rate](https://fred.stlouisfed.org/series/UNRATE).
 
 (To download the data as a csv, click on the top right `Download` and select the `CSV (data)` option).
 
diff --git a/lectures/scipy.md b/lectures/scipy.md
index f5eb01ae..8e91c82e 100644
--- a/lectures/scipy.md
+++ b/lectures/scipy.md
@@ -25,7 +25,7 @@ kernelspec:
 
 ## Overview
 
-[SciPy](https://www.scipy.org) builds on top of NumPy to provide common tools for scientific programming such as
+[SciPy](https://scipy.org/) builds on top of NumPy to provide common tools for scientific programming such as
 
 * [linear algebra](https://docs.scipy.org/doc/scipy/reference/linalg.html)
 * [numerical integration](https://docs.scipy.org/doc/scipy/reference/integrate.html)