diff --git a/lectures/about_py.md b/lectures/about_py.md
index e719c6b3..9dd8851c 100644
--- a/lectures/about_py.md
+++ b/lectures/about_py.md
@@ -139,7 +139,7 @@ popularity of a single Python deep learning library
 
 ```{figure} /_static/lecture_specific/about_py/pytorch_vs_matlab.png
 ```
-Pytorch is just one of several Python libraries for deep learning and AI.
+PyTorch is just one of several Python libraries for deep learning and AI.
 
 
 
@@ -349,7 +349,7 @@ We will discuss the details later in the lecture series, where we cover NumPy in
 While NumPy is still the king of array processing in Python, there are now
 important competitors.
 
-Libraries such as [JAX](https://github.com/jax-ml/jax), [Pytorch](https://pytorch.org/), and [CuPy](https://cupy.dev/) also have
+Libraries such as [JAX](https://github.com/jax-ml/jax), [PyTorch](https://pytorch.org/), and [CuPy](https://cupy.dev/) also have
 built in array types and array operations that can be very fast and efficient.
 
 In fact these libraries are better at exploiting parallelization and fast hardware, as
diff --git a/lectures/autodiff.md b/lectures/autodiff.md
index 843c8a8d..99c4e8f8 100644
--- a/lectures/autodiff.md
+++ b/lectures/autodiff.md
@@ -350,11 +350,11 @@ Let's generate some simulated data:
 ```{code-cell} ipython3
 n = 100
 key = jax.random.key(1234)
-x = jax.random.uniform(key, (n,))
+key, x_key, ϵ_key = jax.random.split(key, 3)
+x = jax.random.uniform(x_key, (n,))
 
 α, β, σ = 0.5, 1.0, 0.1  # Set the true intercept and slope.
-key, subkey = jax.random.split(key)
-ϵ = jax.random.normal(subkey, (n,))
+ϵ = jax.random.normal(ϵ_key, (n,))
 
 y = α * x + β + σ * ϵ
 ```
diff --git a/lectures/numpy.md b/lectures/numpy.md
index 653ca0fc..624063de 100644
--- a/lectures/numpy.md
+++ b/lectures/numpy.md
@@ -1252,7 +1252,11 @@ class DiscreteRV:
     def __init__(self, q, seed=None):
         """
         The argument q is a NumPy array, or array like, nonnegative and sums
-        to 1
+        to 1.
+
+        The argument seed sets the seed for the underlying random number
+        generator; with the default seed=None, draws are not reproducible
+        across runs.
         """
         self.q = q
         self.Q = cumsum(q)
@@ -1431,7 +1435,7 @@ print(A)
 
 **Part2**: Move on to replicate the result of the following broadcasting operation. Meanwhile, compare the speeds of broadcasting and the `for` loop you implement.
 
-For this part of the exercise you can use the `tic`/`toc` functions from the `quantecon` library to time the execution. 
+For this part of the exercise you can use the `qe.Timer()` context manager from the `quantecon` library to time the execution. 
 
 Let's make sure this library is installed.
 
diff --git a/lectures/pandas.md b/lectures/pandas.md
index 8218f44d..d61c3f74 100644
--- a/lectures/pandas.md
+++ b/lectures/pandas.md
@@ -89,7 +89,8 @@ Let's start with Series.
 We begin by creating a series of four random observations
 
 ```{code-cell} ipython3
-s = pd.Series(np.random.randn(4), name='daily returns')
+rng = np.random.default_rng()
+s = pd.Series(rng.standard_normal(4), name='daily returns')
 s
 ```
 
diff --git a/lectures/pandas_panel.md b/lectures/pandas_panel.md
index 922f42a2..383220d3 100644
--- a/lectures/pandas_panel.md
+++ b/lectures/pandas_panel.md
@@ -77,7 +77,7 @@ countries and assign it to `realwage`.
 The dataset can be accessed with the following link:
 
 ```{code-cell} ipython3
-url1 = 'https://github.com/QuantEcon/data-lectures/raw/main/lectures/realwage.csv'
+url1 = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/realwage.csv'
 ```
 
 ```{code-cell} ipython3
@@ -197,7 +197,7 @@ function.
 The dataset can be accessed with the following link:
 
 ```{code-cell} ipython3
-url2 = 'https://github.com/QuantEcon/data-lectures/raw/main/lectures/countries.csv'
+url2 = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/countries.csv'
 ```
 
 ```{code-cell} ipython3
@@ -506,7 +506,7 @@ in Europe by age and sex from [Eurostat](https://ec.europa.eu/eurostat/data/data
 The dataset can be accessed with the following link:
 
 ```{code-cell} ipython3
-url3 = 'https://github.com/QuantEcon/data-lectures/raw/main/lectures/employ.csv'
+url3 = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/employ.csv'
 ```
 
 Reading in the CSV file returns a panel dataset in long format. Use `.pivot_table()` to construct
diff --git a/lectures/polars.md b/lectures/polars.md
index 1285e065..1ab33a05 100644
--- a/lectures/polars.md
+++ b/lectures/polars.md
@@ -78,7 +78,8 @@ Let's start with Series.
 We begin by creating a series of four random observations
 
 ```{code-cell} ipython3
-s = pl.Series(name='daily returns', values=np.random.randn(4))
+rng = np.random.default_rng()
+s = pl.Series(name='daily returns', values=rng.standard_normal(4))
 s
 ```
 
@@ -114,7 +115,7 @@ For example, to associate ticker symbols with returns:
 ```{code-cell} ipython3
 df = pl.DataFrame({
     'company': ['AMZN', 'AAPL', 'MSFT', 'GOOG'],
-    'daily returns': np.random.randn(4)
+    'daily returns': rng.standard_normal(4)
 })
 df
 ```
@@ -463,13 +464,13 @@ a grouped weighted average.
 
 ```{code-cell} ipython3
 n = 5_000_000
-np.random.seed(42)
+rng = np.random.default_rng(42)
 
-groups = np.random.choice(['A', 'B', 'C', 'D'], n)
-values = np.random.randn(n)
-weights = np.random.rand(n)
-extra1 = np.random.randn(n)
-extra2 = np.random.randn(n)
+groups = rng.choice(['A', 'B', 'C', 'D'], n)
+values = rng.standard_normal(n)
+weights = rng.random(n)
+extra1 = rng.standard_normal(n)
+extra2 = rng.standard_normal(n)
 
 big_pd = pd.DataFrame({
     'group': groups, 'value': values,
@@ -685,7 +686,7 @@ Calculate percentage changes using Polars expressions:
 
 ```{code-cell} ipython3
 price_change = ticker.select([
-    ((pl.col(tick).last() / pl.col(tick).first() - 1) * 100)
+    ((pl.col(tick).drop_nulls().last() / pl.col(tick).drop_nulls().first() - 1) * 100)
     .alias(tick)
     for tick in ticker_list.keys()
 ]).transpose(
diff --git a/lectures/python_by_example.md b/lectures/python_by_example.md
index f89c87f5..b3717dfa 100644
--- a/lectures/python_by_example.md
+++ b/lectures/python_by_example.md
@@ -173,19 +173,22 @@ Then it's harder for readers to know where `sqrt` came from, should they wish to
 
 ### Random Draws
 
-Returning to our program that plots white noise, the remaining three lines
+Returning to our program that plots white noise, the remaining four lines
 after the import statements are
 
 ```{code-cell} ipython
+rng = np.random.default_rng()
 ϵ_values = rng.standard_normal(100)
 plt.plot(ϵ_values)
 plt.show()
 ```
 
-The first line generates 100 (quasi) independent standard normals and stores
+The first line creates a random number generator `rng`.
+
+The second line generates 100 (quasi) independent standard normals and stores
 them in `ϵ_values`.
 
-The next two lines genererate the plot.
+The last two lines generate the plot.
 
 We can and will look at various ways to configure and improve this plot below.