Merge branch 'main' into sankey

content/c-sharp/concepts/data-types/data-types.md

@@ -24,15 +24,15 @@ Value types are data types that are built-in to C#. The available types and thei
 | --------- | ----------------------- | ------------ |
 | `bool`    | Boolean                 | 1 byte       |
 | `byte`    | Byte                    | 1 byte       |
-| `sbyte`   | Short Byte              | 1 byte       |
+| `sbyte`   | Signed Byte             | 1 byte       |
 | `char`    | Character               | 2 bytes      |
 | `decimal` | Decimal                 | 16 bytes     |
 | `double`  | Double                  | 8 bytes      |
 | `float`   | Float                   | 4 bytes      |
 | `int`     | Integer                 | 4 bytes      |
 | `uint`    | Unsigned Integer        | 4 bytes      |
 | `nint`    | Native Integer          | 4 or 8 bytes |
-| `unint`   | Unsigned Native Integer | 4 or 8 bytes |
+| `nuint`   | Unsigned Native Integer | 4 or 8 bytes |
 | `long`    | Long                    | 8 bytes      |
 | `ulong`   | Unsigned Long           | 8 bytes      |
 | `short`   | Short                   | 2 bytes      |
@@ -51,7 +51,7 @@ float heightOfGiraffe = 908.32f;
 int seaLevel = -24;
 uint year = 2023u;
 nint pagesInBook = 412;
-unint milesToNewYork = 2597;
+nuint milesToNewYork = 2597;
 long circumferenceOfEarth = 25000l;
 ulong depthOfOcean = 28000ul;
 short tableHeight = 4;

content/data-science/concepts/data-distributions/terms/exponential-distribution/exponential-distribution.md

@@ -0,0 +1,48 @@
+---
+Title: 'Exponential Distribution'
+Description: 'The exponential distribution is a probability distribution often used to model the time between events in a Poisson process.'
+Subjects:
+  - 'Data Science'
+  - 'Statistics'
+Tags:
+  - 'Data Distributions'
+  - 'Exponential'
+CatalogContent:
+  - 'learn-data-science'
+  - 'paths/data-science'
+---
+
+The **exponential distribution** models the time between independent events that occur at a fixed average rate. It is frequently used in reliability analysis, queuing theory, and survival analysis. The distribution is defined by a single parameter, the rate λ which determines how quickly events occur.
+
+The exponential distribution formula is given by:
+
+$$f(x|λ) = λ e^{-λ x}$$
+
+- `λ`: The rate parameter that represents the number of events per unit time.
+- `x`: A random variable that represents the time between events.
+
+## Example
+
+The example below demonstrates how to generate random samples from an exponential distribution using NumPy and visualize the results with a histogram using Matplotlib:
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Set the rate parameter (lambda)
+rate = 1.5  # Events per unit time
+
+# Generate 1,000 random samples from the exponential distribution
+data = np.random.exponential(scale=1/rate, size=1000)
+
+# Plot the histogram of the generated data
+plt.hist(data, bins=30, density=True, alpha=0.6, color='teal', edgecolor='black')
+plt.title(f"Exponential Distribution (rate = {rate})")
+plt.xlabel("Time Between Events")
+plt.ylabel("Density")
+plt.show()
+```
+
+The above code produces the following output:
+
+![The output for the above example](https://raw.githubusercontent.com/Codecademy/docs/main/media/exponential-distribution.png)

documentation/tags.md

@@ -393,6 +393,7 @@ Visibility
 VR
 Vue
 Web3
+Exponential
 WebRTC
 Weight & Bias
 While