𝐀𝐈 & 𝐌𝐋 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐅𝐫𝐨𝐦 6 𝐓𝐨𝐩 𝐈𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐢𝐨𝐧𝐬!😍

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8 FREE AI Courses by Google 🎓🚀 Learn, Grow, and Succeed

1. Introduction to Generative AI
→ An introductory course to explain what generative AI is.
→ You'll learn how AI is used and how it's different from machine learning.

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2. Image Generation
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→ After completing this course, you will be awarded a badge.

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Best Resources to learn ML & AI 👇
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Free and Essential GenAI Courses
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Excel Functions

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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:

🔹 1️⃣ Aggregation and Grouping
Efficiently summarize and analyze data with key functions like SUM, COUNT, AVG, MIN, MAX, and GROUP BY.

🔹 2️⃣ Window Functions
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.

🔹 3️⃣ Join Operations
Combine data from multiple tables using INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, and CROSS JOIN.

🔹 4️⃣ Subqueries and CTEs
Simplify complex queries with WITH statements, or use subqueries in SELECT, FROM, and WHERE clauses to enhance readability and performance.

🔹 5️⃣ Data Cleaning and Transformation
Prepare your data with functions like DISTINCT, LOWER, UPPER, TRIM, REGEXP_REPLACE, and COALESCE to ensure high-quality outputs.

Data Engineering Interview Preparation Resources: https://t.me/datascienceresourcestp/61

Learn SQL: https://t.me/sqlresourcestp

All the best 👍👍

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💫🌈🧑‍💻👩‍💻⚛️Free Full Stack Certifications Courses to try in 2025

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http://cs50.harvard.edu/web/

🔸Full Stack Development Roadmap
https://t.me/webdevresourcestp/35

🔸Java
https://data-flair.training/courses/free-java-course/

https://t.me/javaresourcestp/26

http://learn.microsoft.com/shows/java-for-beginners/

🔸 JavaScript Roadmap
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🔸Neo4j
https://graphacademy.neo4j.com/courses/neo4j-certification/

🔸React
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🔸Angular
https://hackerrank.com/skills-verification/angular_intermediate

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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
↳ Event Listeners
↳ Event Object
↳ Bubbling and Capturing

16.DOM Manipulation
↳ Selecting DOM Elements
↳ Modifying Element Properties
↳ Creating and Appending Elements

17.Regular Expressions
↳ Pattern Matching
↳ RegExp Methods: test(), exec(), match(), replace()

18.Browser APIs
↳ localStorage and sessionStorage
↳ navigator Object
↳ Geolocation API
↳ Canvas API

19.Web APIs
↳ setTimeout(), setInterval()
↳ 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

Hope it helps 😊🌱

More Resources on this WhatsApp Channel
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R

Top 5 Regression Algorithms in ML

Famous Programming Languages and Their Frameworks

1.Python:
Frameworks:
* Django
* Flask
* Pyramid
* Tornado

2.JavaScript:
Frameworks (Front-End):
* React
* Angular
* Vue.js
* Ember.js
_and thousands more...._

Frameworks (Back-End):
* Node.js (Runtime)
* Express.js
* Nest.js
* Meteor

3..Java:
Frameworks:
* Spring Framework
* Hibernate
* Apache Struts
* 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
* ASP. NET Core

7.Go (Golang):
Frameworks:
* Gin
* Echo
* Revel

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

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

10.Kotlin:
Frameworks (Android):
* Android Jetpack
* Ktor

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

12.Scala:
Frameworks:
* Play Framework
* Akka

13.Perl:
Frameworks:
* Dancer
* Catalyst

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

15.Dart:
Frameworks:
* Flutter (for mobile app development)

16.R:
Frameworks (for data science and statistics):
* Shiny
* ggplot2

17.Julia:
Frameworks (for scientific computing):
* Pluto.jl
* Genie.jl

18.MATLAB:
Frameworks (for scientific and engineering applications):
* Simulink

19.COBOL:
Frameworks:
* COBOL-IT

20.Erlang:
Frameworks:
* Phoenix (for web applications)

21.Groovy:
Frameworks:
* Grails (for web applications)

Best Programming Resources: https://topmate.io/learning_resources/1362011

ENJOY LEARNING 👍👍

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Libraries for Data Science in Python

Backend Burger

MUST ADD these 5 POWER Bl projects to your resume to get hired

Here are 5 mini projects that not only help you to gain experience but also it will help you to build your resume stronger

📌Customer Churn Analysis
🔗 https://www.kaggle.com/code/fabiendaniel/customer-segmentation/input

📌Credit Card Fraud
🔗 https://github.com/sahidul-shaikh/credit-card-fraud-

📌Movie Sales Analysis
🔗https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data

📌Airline Sector
🔗https://www.kaggle.com/datasets/yuanyuwendymu/airline-

📌Financial Data Analysis
🔗https://www.kaggle.com/datasets/qks1%7Cver/financial-data-

✅ Free Courses with Certificate:
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Simple guide

1. Data Utilization:
- Initiate the process by using the provided datasets for a comprehensive analysis.

2. Domain Research:
- Conduct thorough research within the domain to identify crucial metrics and KPIs for analysis.

3. Dashboard Blueprint:
- Outline the structure and aesthetics of your dashboard, drawing inspiration from existing online dashboards for enhanced design and functionality.

4. Data Handling:
- Import data meticulously, ensuring accuracy. Proceed with cleaning, modeling, and the creation of essential measures and calculations.

5. Question Formulation:
- Brainstorm a list of insightful questions your dashboard aims to answer, covering trends, comparisons, aggregations, and correlations within the data.

6. Platform Integration:
- Utilize Novypro.com as the hosting platform for your dashboard, ensuring seamless integration and accessibility.

7. LinkedIn Visibility:
- 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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🔍 Mastering Spark: 20 Interview Questions Demystified!

1️⃣ MapReduce vs. Spark: Learn how Spark achieves 100x faster performance compared to MapReduce.
2️⃣ RDD vs. DataFrame: Unravel the key differences between RDD and DataFrame, and discover what makes DataFrame unique.
3️⃣ DataFrame vs. Datasets: Delve into the distinctions between DataFrame and Datasets in Spark.
4️⃣ RDD Operations: Explore the various RDD operations that power Spark.
5️⃣ Narrow vs. Wide Transformations: Understand the differences between narrow and wide transformations in Spark.
6️⃣ Shared Variables: Discover the shared variables that facilitate distributed computing in Spark.
7️⃣ Persist vs. Cache: Differentiate between the persist and cache functionalities in Spark.
8️⃣ Spark Checkpointing: Learn about Spark checkpointing and how it differs from persisting to disk.
9️⃣ SparkSession vs. SparkContext: Understand the roles of SparkSession and SparkContext in Spark applications.
1️⃣0️⃣ Spark-submit Parameters: Explore the parameters to specify in the spark-submit command.
1️⃣1️⃣ Cluster Managers in Spark: Familiarize yourself with the different types of cluster managers available in Spark.
1️⃣2️⃣ Deploy Modes: Learn about the deploy modes in Spark and their significance.
1️⃣3️⃣ Executor vs. Executor Core: Distinguish between executor and executor core in the Spark ecosystem.
1️⃣4️⃣ Shuffling Concept: Gain insights into the shuffling concept in Spark and its importance.
1️⃣5️⃣ Number of Stages in Spark Job: Understand how to decide the number of stages created in a Spark job.
1️⃣6️⃣ Spark Job Execution Internals: Get a peek into how Spark internally executes a program.
1️⃣7️⃣ Direct Output Storage: Explore the possibility of directly storing output without sending it back to the driver.
1️⃣8️⃣ Coalesce and Repartition: Learn about the applications of coalesce and repartition in Spark.
1️⃣9️⃣ Physical and Logical Plan Optimization: Uncover the optimization techniques employed in Spark's physical and logical plans.
2️⃣0️⃣ Treereduce and Treeaggregate: Discover why treereduce and treeaggregate are preferred over reduceByKey and aggregateByKey in certain scenarios.

PySpark Concepts: https://t.me/datascienceresourcestp/63

Data Engineer Interview Questions: https://t.me/datascienceresourcestp/61

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

Who is Data Scientist?

He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.

A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:

Determines correct datasets and variables.

Identifies the most challenging data-analytics problems.

Collects large sets of data- structured and unstructured, from different sources.

Cleans and validates data ensuring accuracy, completeness, and uniformity.

Builds and applies models and algorithms to mine stores of big data.

Analyzes data to recognize patterns and trends.

Interprets data to find solutions.

Communicates findings to stakeholders using tools like visualization.

Data Science Habits: https://t.me/datascienceresourcestp/56

Top Data Science Tools: https://t.me/datascienceresourcestp/70

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

Remote Senior Data Engineer (Python) Job at Soda Data

- Fully Remote
- Compensation: Up to 110, 000 euros/year + equity

Requirements
- Experience building data/ML products or cloud-based software
- Python Data Stack and SQL skills

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𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐓𝐨 𝐁𝐞𝐜𝐨𝐦𝐞 𝐒𝐤𝐢𝐥𝐥𝐞𝐝 𝗜𝗻 𝟐𝟎𝟐𝟓

Free lifetime access – Learn anytime, anywhere

Get Completion Certificate

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New developers: Whenever you work on something interesting, write it down in a document which you keep updating. This will be very helpful when you need to create a resume or have to talk about your achievements in an interview. (Or for college essays.)

I can guarantee you that if you don't do this, you will forget half the interesting things you've done; and for a majority of us, our brains are experts in convincing us that we haven't really done anything interesting.

𝗖𝗜𝗦𝗖𝗢 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀

- Data Analytics
- Data Science 
- Python
- Javascript
- Cybersecurity
 
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Learn for free:

HTML: html.com
CSS: web.dev/learn/css
JavaScript: t.me/javascriptresourcestp
React: react-tutorial.app
API: rapidapi.com/learn
Python: t.me/pythonresourcestp
SQL: t.me/sqlresourcestp
Git: git-scm.com/book

✅ List and Set in Java Collections Framework :

🔅 LIST :

🔸 List in Java provides the facility to maintain the ordered collection.

🔹 It contains the index-based methods to insert, update, delete and search the elements.

🔸 It can have the duplicate elements also.

🔹 We can also store the null elements in the list.

🔅 SET :

🔸 Set interface in Java is present in jave.util package.

🔹It extends the Collection interface.

🔸 It represents the unordered set of elements which doesn't allow us to store the duplicate items.

🔹 We can store at most one null value in Set.

🔸 Set is implemented by HashSet, LinkedHashSet and TreeSet.

Java BackEnd Development: https://t.me/javaresourcestp/22

Java Developer Interview: https://t.me/javaresourcestp/29

All the best 👍👍

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