Learn DSA Visually

Links to Sites to help you learn Data Structures and Algorithms Visually:

Data Structure Visualisations :
https://www.cs.usfca.edu/~galles/visualization/Algorithms.html

Visualgo:
https://visualgo.net/en

Visualizing Algorithms by Mike Bostock :
https://bost.ocks.org/mike/algorithms/

DSA Interview Questions: https://t.me/techpsyche/545

All the best 👍👍

More Resources Here
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R

DSA INTERVIEW QUESTIONS AND ANSWERS

1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.

2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.

3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.

4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.

5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).

6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.

7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.

8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.

Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).

Time complexity: best case O(n2); worst O(n2)

Space complexity: worst O(1)

9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them

10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.

11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect

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Don’t miss these top SQL questions recently asked by leading companies!

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Learn This Concept to be proficient in PySpark.

𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝗦𝗽𝗮𝗿𝗸:
- PySpark Architecture
- SparkContext and SparkSession
- RDDs (Resilient Distributed Datasets)
- DataFrames
- Transformations and Actions
- Lazy Evaluation

𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀:
- Creating DataFrames
- Reading Data from CSV, JSON, Parquet
- DataFrame Operations
- Filtering, Selecting, and Aggregating Data
- Joins and Merging DataFrames
- Working with Null Values

𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗖𝗼𝗹𝘂𝗺𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Defining and Using UDFs (User Defined Functions)
- Column Operations (Select, Rename, Drop)
- Handling Complex Data Types (Array, Map)
- Working with Dates and Timestamps

𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗵𝘂𝗳𝗳𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Understanding Partitions
- Repartitioning and Coalescing
- Managing Shuffle Operations
- Optimizing Partition Sizes for Performance

𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮:
- When to Cache or Persist
- Memory vs Disk Caching
- Checking Storage Levels

𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗪𝗶𝘁𝗵 𝗦𝗤𝗟:
- Spark SQL Introduction
- Creating Temp Views
- Running SQL Queries
- Optimizing SQL Queries with Catalyst Optimizer
- Working with Hive Tables in PySpark

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸:
- Data Cleaning and Preparation
- Handling Missing Values
- Data Normalization and Transformation
- Working with Categorical Data

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸:
- Broadcasting Variables
- Accumulators
- PySpark Window Functions
- PySpark with Machine Learning (MLlib)
- Working with Streaming Data (Spark Streaming)

𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗧𝘂𝗻𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸:
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- Tungsten Execution Engine
- Memory Management and Garbage Collection
- Tuning Parallelism
- Using Spark UI for Performance Monitoring

Data Engineering Interview Preparation Resources
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5 SQL Queries Every Data Engineer Must Master (with Examples)

SQL has been the backbone of #DataEngineering for years. Whether you’re building pipelines, optimizing databases, or troubleshooting, mastering these concepts is crucial:

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Perform advanced analytics like rankings, running totals, and comparisons while preserving row-level detail. Learn functions like ROW_NUMBER, RANK, NTILE, LAG, LEAD, and windowed SUM.

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Combine data from multiple tables using INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, and CROSS JOIN.

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Simplify complex queries with WITH statements, or use subqueries in SELECT, FROM, and WHERE clauses to enhance readability and performance.

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Prepare your data with functions like DISTINCT, LOWER, UPPER, TRIM, REGEXP_REPLACE, and COALESCE to ensure high-quality outputs.

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Complete JavaScript Road Map🔥

A-Z JavaScript👇

1.Variables
↳ var
↳ let
↳ const

2. Data Types
↳ number
↳ string
↳ boolean
↳ null
↳ undefined
↳ symbol

3.Declaring variables
↳ var
↳ let
↳ const

4.Expressions
Primary expressions
↳ this
↳ Literals
↳ []
↳ {}
↳ function
↳ class
↳ function*
↳ async function
↳ async function*
↳ /ab+c/i
↳ string
↳ ( )

Left-hand-side expressions
↳ Property accessors
↳ ?.
↳ new
↳ new .target
↳ import.meta
↳ super
↳ import()

5.operators
↳ Arithmetic Operators: +, -, *, /, %
↳ Comparison Operators: ==, ===, !=, !==, <, >, <=, >=
↳ Logical Operators: &&, ||, !

6.Control Structures
↳ if
↳ else if
↳ else
↳ switch
↳ case
↳ default

7.Iterations/Loop
↳ do...while
↳ for
↳ for...in
↳ for...of
↳ for await...of
↳ while

8.Functions
↳ Arrow Functions
↳ Default parameters
↳ Rest parameters
↳ arguments
↳ Method definitions
↳ getter
↳ setter

9.Objects and Arrays
↳ Object Literal: { key: value }
↳ Array Literal: [element1, element2, ...]
↳ Object Methods and Properties
↳ Array Methods: push(), pop(), shift(), unshift(),
splice(), slice(), forEach(), map(), filter()

10.Classes and Prototypes
↳ Class Declaration
↳ Constructor Functions
↳ Prototypal Inheritance
↳ extends keyword
↳ super keyword
↳ Private class features
↳ Public class fields
↳ static
↳ Static initialization blocks

11.Error Handling
↳ try,
↳ catch,
↳ finally (exception handling)

ADVANCED CONCEPTS
--------------------------

12.Closures
↳ Lexical Scope
↳ Function Scope
↳ Closure Use Cases

13.Asynchronous JavaScript
↳ Callback Functions
↳ Promises
↳ async/await Syntax
↳ Fetch API
↳ XMLHttpRequest

14.Modules
↳ import and export Statements (ES6 Modules)
↳ CommonJS Modules (require, module.exports)

15.Event Handling
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↳ Event Object
↳ Bubbling and Capturing

16.DOM Manipulation
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↳ Geolocation API
↳ Canvas API

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↳ XMLHttpRequest
↳ Fetch API
↳ WebSockets

20.Functional Programming
↳ Higher-Order Functions
↳ map(), reduce(), filter()
↳ Pure Functions and Immutability

21.Promises and Asynchronous Patterns
↳ Promise Chaining
↳ Error Handling with Promises
↳ Async/Await

22.ES6+ Features
↳ Template Literals
↳ Destructuring Assignment
↳ Rest and Spread Operators
↳ Arrow Functions
↳ Classes and Inheritance
↳ Default Parameters
↳ let, const Block Scoping

23.Browser Object Model (BOM)
↳ window Object
↳ history Object
↳ location Object
↳ navigator Object

24.Node.js Specific Concepts
↳ require()
↳ Node.js Modules (module.exports)
↳ File System Module (fs)
↳ npm (Node Package Manager)

25.Testing Frameworks
↳ Jasmine
↳ Mocha
↳ Jest

------------------- END-------------------

Some Good Resources To Learn JavaScript
1.Documentation

Mozilla MDN Web Docs
developer.mozilla.org/en-US/docs/Web…
DevDocs
devdocs.io/javascript/

Websites to practice javaScript: https://t.me/javascriptresourcestp/451

GitHub repos for JS Devs: https://t.me/javascriptresourcestp/442

Useful Channel: youtube.com/c/FreeCodeCamp

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Top 5 Regression Algorithms in ML

Famous Programming Languages and Their Frameworks

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Frameworks:
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* Play Framework

4.Ruby:
Frameworks:
* Ruby on Rails (Rails)
* Sinatra
* Hanami

5.PHP:
Frameworks:
* Laravel
* Symfony
* CodeIgniter
* Yii
* Zend Framework

6.C#:
Frameworks:
* .NET Framework
* ASP. NET
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7.Go (Golang):
Frameworks:
* Gin
* Echo
* Revel

8.Rust:
Frameworks:
* Rocket
* Actix
* Warp

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Frameworks (iOS/macOS):
* SwiftUI
* UIKit
* Cocoa Touch

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Frameworks (Android):
* Android Jetpack
* Ktor

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Frameworks (Front-End):
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* Vue.js (with TypeScript)
* React (with TypeScript)

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Frameworks:
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* Akka

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Frameworks:
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14.Lua:
Frameworks:
* OpenResty (for web development)

15.Dart:
Frameworks:
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Frameworks (for data science and statistics):
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Frameworks (for scientific and engineering applications):
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Frameworks:
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Frameworks:
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Frameworks:
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Libraries for Data Science in Python

Backend Burger

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- Share your dashboard on LinkedIn with a concise post providing context. Include a link to your Novypro-hosted dashboard to foster engagement and professional connections.

Power BI Syllabus: https://t.me/dataanalysisresourcestp/66

Chart Selection: https://t.me/dataanalysisresourcestp/81

3 Must Do Data Analytics Courses: https://tinyurl.com/m239d2s8

Hope this helps you

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