diff --git a/DIRECTORY.md b/DIRECTORY.md
index 4d662ea47..699774b2c 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -75,6 +75,7 @@
   * [MinimumCostPath](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/MinimumCostPath.js)
   * [NumberOfSubsetEqualToGivenSum](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/NumberOfSubsetEqualToGivenSum.js)
   * [SieveOfEratosthenes](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/SieveOfEratosthenes.js)
+  * [Shuf](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/Shuf.js)
   * [SudokuSolver](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/SudokuSolver.js)
   * [TrappingRainWater](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/TrappingRainWater.js)
   * [ZeroOneKnapsack](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/ZeroOneKnapsack.js)
diff --git a/Dynamic-Programming/Shuf.js b/Dynamic-Programming/Shuf.js
new file mode 100644
index 000000000..a6767c9a6
--- /dev/null
+++ b/Dynamic-Programming/Shuf.js
@@ -0,0 +1,96 @@
+/*
+Given a data set of an unknown size,
+Get a random sample in a random order
+It's used in data analytics, often as a way to get a small random sample from a data lake or warehouse, or from a large CSV file
+*/
+function shuf (datasetSource, sampleSize) {
+  const output = fillBaseSample(datasetSource, sampleSize)
+
+  return randomizeOutputFromDataset(datasetSource, output)
+}
+
+/**
+ * Fills the output if possible, with the minimum number of values
+ * @param {Iterable.<T>} datasetSource The iterable source of data
+ * @param {number} sampleSize The size of the sample to extract from the dataset
+ * @returns {Array.<T>} The random sample, as an array
+ * @template T
+ */
+function fillBaseSample (datasetSource, sampleSize) {
+  let filledIndexes = []
+  let output = new Array(sampleSize)
+
+  // Spread data out filling the array
+  while (true) {
+    const iterator = datasetSource.next()
+    if (iterator.done) break
+
+    let insertTo = Math.floor(Math.random() * output.length)
+    while (filledIndexes.includes(insertTo)) {
+      insertTo++
+      if (insertTo === output.length) {
+        insertTo = 0
+      }
+    }
+    output[insertTo] = {
+      value: iterator.value
+    }
+
+    filledIndexes = [...filledIndexes, insertTo]
+
+    if (filledIndexes.length === sampleSize) {
+      break
+    }
+  }
+
+  if (filledIndexes.length < output.length) {
+    // Not a large enough dataset to fill the sample - trim empty values
+    output = output.filter((_, i) => filledIndexes.includes(i))
+  }
+
+  return output.map((o) => o.value)
+}
+
+/**
+ * Replaces values in the output randomly with new ones from the dataset
+ * @param {Iterable.<T>} datasetSource The iterable source of data
+ * @param {Array.<T>} output The output so far, filled with data
+ * @returns {Array.<T>} The random sample, as an array
+ * @template T
+ */
+function randomizeOutputFromDataset (datasetSource, output) {
+  const newOutput = [...output]
+  let readSoFar = output.length
+
+  while (true) {
+    const iterator = datasetSource.next()
+    if (iterator.done) break
+    readSoFar++
+
+    const insertTo = Math.floor(Math.random() * readSoFar)
+    if (insertTo < newOutput.length) {
+      newOutput[insertTo] = iterator.value
+    }
+  }
+
+  return newOutput
+}
+
+const main = () => {
+  /**
+   * Generates a random range of data, with values between 0 and 2^31 - 1
+   * @param {number} length The number of data items to generate
+   * @returns {Iterable<number>} Random iterable data
+  */
+  function * generateRandomData (length) {
+    const maxValue = Math.pow(2, 31) - 1
+    for (let i = 0; i < length; i++) {
+      yield Math.floor(Math.random() * maxValue)
+    }
+  }
+
+  const source = generateRandomData(1000)
+  const result = shuf(source, 10)
+  console.log(result)
+}
+main()