Merge f4a2006e60 into 9046d80bdb

Before diving into any of the data structures, readers should be reminded of two fundamental laws in software architecture:

1.Everything is a trade-ff
2."Why is more important than the how"

So, readers face the nuances and reality of these data structures from the beginning. These two laws are coined by two thought leaders in software architecture: Mark Richards and Neal Ford. They have explained these two laws in various conference talks and books. For example, here you can read about these two laws here:

https://www.infoq.com/podcasts/software-architecture-hard-parts/


Also, here is a book for reference:
https://a.co/d/fKOodW9

Co-authored-by: Oleksii Trekhleb <3000285+trekhleb@users.noreply.github.com>

README.md

@@ -48,6 +48,8 @@ be accessed and modified efficiently. More precisely, a data structure is a coll
 values, the relationships among them, and the functions or operations that can be applied to
 the data.
 
+Remember that each data has its own trade-offs. And you need to pay attention more to why you're choosing a certain data structure than to how to implement it.
+
 `B` - Beginner, `A` - Advanced
 
 * `B` [Linked List](src/data-structures/linked-list)

src/algorithms/uncategorized/best-time-to-buy-sell-stocks/README.md

@@ -46,7 +46,7 @@ Let's say we have an array of prices `[7, 6, 4, 3, 1]` and we're on the _1st_ da
 1. _Option 1: Keep the money_ → profit would equal to the profit from buying/selling the rest of the stocks → `keepProfit = profit([6, 4, 3, 1])`.
 2. _Option 2: Buy/sell at current price_ → profit in this case would equal to the profit from buying/selling the rest of the stocks plus (or minus, depending on whether we're selling or buying) the current stock price → `buySellProfit = -7 + profit([6, 4, 3, 1])`.
 
-The overall profit would be equal to → `overalProfit = Max(keepProfit, buySellProfit)`.
+The overall profit would be equal to → `overallProfit = Max(keepProfit, buySellProfit)`.
 
 As you can see the `profit([6, 4, 3, 1])` task is being solved in the same recursive manner.
 

src/algorithms/uncategorized/n-queens/README.md

@@ -59,7 +59,7 @@ and return false.
         queen here leads to a solution.
     b) If placing queen in [row, column] leads to a solution then return 
         true.
-    c) If placing queen doesn't lead to a solution then umark this [row, 
+    c) If placing queen doesn't lead to a solution then unmark this [row, 
         column] (Backtrack) and go to step (a) to try other rows.
 3) If all rows have been tried and nothing worked, return false to trigger 
     backtracking.

src/data-structures/bloom-filter/README.md

@@ -93,7 +93,7 @@ three factors: the size of the bloom filter, the
 number of hash functions we use, and the number
 of items that have been inserted into the filter.
 
-The formula to calculate probablity of a false positive is:
+The formula to calculate probability of a false positive is:
 
 ( 1 - e <sup>-kn/m</sup> ) <sup>k</sup>
 

src/data-structures/heap/Heap.js

@@ -279,7 +279,7 @@ export default class Heap {
   /* istanbul ignore next */
   pairIsInCorrectOrder(firstElement, secondElement) {
     throw new Error(`
-      You have to implement heap pair comparision method
+      You have to implement heap pair comparison method
       for ${firstElement} and ${secondElement} values.
     `);
   }

src/data-structures/linked-list/LinkedList.js

@@ -80,8 +80,10 @@ export default class LinkedList {
           this.tail.next = newNode;
           this.tail = newNode;
         } else {
-          this.head = newNode;
-          this.tail = newNode;
+          // this.head = newNode;
+          // this.tail = newNode;
+          newNode.next = this.head;
+          newNode = this.head;
         }
       }
     }