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Data Structures and Algorithms (DSA Tutorial)

Data Structures and Algorithms (DSA Tutorial)

In the Current Times Demand For Data Science Has Rose Up And There Are Many Developers Willing to Learn Data Structures & Algorithms And This is Post is Going to Be the Introduction to Data Structures & Algorithms, What are the Topics to Cover In Order to Become a Good Data Scientist. If You Want the Complete Tutorial Series Or a DSA Course Then Let Me Know In the Comments Below. 

What are Data Structures and Algorithms?

Data Structures and Algorithms are the Basic Concepts That are Used to Organize & Manipulate Data.  

In The Field Of Data Science, Without Data Structures and Algorithms You can Proceed Further In Your Data Science Journey. 

  • Data Structures: Data Structures are a Way In Which a Piece Of Data Is Organized And Stored In a Computer Memory. We Use Data Structures to Hold & Manage Our Data.

Some of the Common Data Structures Include the Following: 

  •  Arrays
  •  Linked Lists
  • Stacks
  • Queues
  • Trees
  • Graphs 

What are Arrays?

In Simple Words, Arrays are the Data Structures That Stores a Collection of Items In Contiguous Memory Locations. They are Efficiently Used For Accessing Elements By Index, But They are Not Efficient In Inserting or Deleting Items. 

What are Linked Lists?

Linked Lists are Dynamic Data Structures That Consists Of a Chain Of Nodes, In Which Each Node Contains a Value & a Reference to the next node. Linked Lists Allows Insertion & Deletion, But Accessing the Element By Index Is Quiet Slower.

What are Stacks & Queues?

Stacks & Queues Both are Linear Data Structures, But They Have Different Access Patterns. 

  • Stacks: Stack is a Last-In & First-Out (LIFO) Data Structure, Where the Last Item is To Be Added And the First Item Is To Be Removed. 
  • Queues: Queues is a First-In & Fiest Out (FIFO) Data Structure, Where the First Item Is Added To the Queue & Last Item is Removed.

What are Trees?

Trees are a non-Linear Data Structure That Consists of Nodes & Edges. And Each Node Represents a Value And Edges Connect Nodes to Their Children. The Topmost Node is Called Root. And Nodes Without Children are Called Leaves. Trees are Used For Searching, Sorting, And Also For Data Compression. 

What are Graphs?

Graphs are Again Another Non-Linear Data Structure That Consists of Nodes And Edges Same as Trees. But Unlike Trees, Graphs can are Used for a Variety Of Tasks Such as Network Analysis, Social Media Analysis, And a Recommendation System.

What are Algorithms?

Algorithms are a Set of Instructions Used to Solve a Problem or Perform any Task. They Are Used to Manipulate Data In Such a Way That It Gives a Desired Outcome. 

Some Of The Common Algorithms Include:

Examples of Some Sorting Algorithms

  • Bubble Sort
  • Insertion Sort 
  • Quicksort

Examples of Searching Algorithms 

  • Linear Search
  • Binary Search

Sorting Algorithms

Sorting Algorithms are Used to Arrange a Collection of Items In a Specific Order. For Example, Ascending or Descending. Bubble Sort, Quicksort, And All Other Different Algorithms That Can Be Used to Sort a Collection of Items. 

  • Bubble Sort: Bubble Sort Compares Adjacent Elements And Swaps Them If They are Ub The Wrong Order. While Insertion Sort Takes an Element And Inserts It In the Correct Place, In the Already Started Portion Of the List. 
  • Quick Sort: Quick Sort is a Divide-and-Conquer Alogirthm That Works By Selection a Pivot Point and Partitioning the Data Around That Point.

Searching Algorithms

Searching Algorithms are Used to Find a Specific Element Within the Given Collection of Items. Linear Search Checks Each Element One At a Time Whereas Binary Search Takes Advantage Of This And That Collection is Sorted And Narrows Down the Search By Repeatedly Dividing the Collection In Half. 

Conclusion 

At Last, Data Structures & Algorithms Play a Vital Role In Data Science As Well As It is Used In Other Fields Like Software Development. Data Structures & Algorithms are the Fundamental Unit To Learn In Order to Get Started With Data Science. So This Was It For This Post Do Share This Post With Your Data Science Friends And Keep Coding Keep Exploring!

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