Coding and Aptitude Round before interview
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds donโt go your way, you might have had a brain fade, it was not your day etc. Donโt worry! Shake if off for there is always a next time and this is not the end of the world.
Follow This WhatsApp Channel for More Interview Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds donโt go your way, you might have had a brain fade, it was not your day etc. Donโt worry! Shake if off for there is always a next time and this is not the end of the world.
Follow This WhatsApp Channel for More Interview Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Forwarded from JavaScript Resources | Libraries & Frameweorks| React Js|Node Js|Vue Js|Express|Angular|jQuery
JavaScript Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to JavaScript
| | |-- Setting Up Development Environment (IDE: VSCode, Sublime Text, etc.)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables (var, let, const) and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Switch Case
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Do-While Loop
| | |-- For...in and For...of Loops
| |
| |-- Exception Handling
| | |-- Try-Catch Block
| | |-- Finally Block
| | |-- Throwing Errors
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Declarations
| | |-- Function Expressions
| | |-- Arrow Functions
| |
| |-- Parameters and Arguments
| | |-- Default Parameters
| | |-- Rest and Spread Operators
| |
| |-- Scope
| | |-- Global and Local Scope
| | |-- Hoisting
| | |-- Closures
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Objects and Properties
| | |-- Methods
| |
| |-- Prototypes and Inheritance
| | |-- Prototype Chain
| | |-- Inheritance with Prototypes
| |
| |-- Classes
| | |-- Class Syntax
| | |-- Constructors
| | |-- Inheritance (extends and super)
| |
| |-- Encapsulation
| | |-- Private and Public Members (using # for private)
|
|-- Advanced JavaScript
| |-- Asynchronous JavaScript
| | |-- Callbacks
| | |-- Promises
| | |-- Async/Await
| |
| |-- Event Loop
| | |-- Understanding the Event Loop
| | |-- Microtasks and Macrotasks
|
|-- Data Structures
| |-- Arrays
| | |-- Array Methods (map, filter, reduce, etc.)
| | |-- Array Manipulation
| |
| |-- Objects
| | |-- Creating and Manipulating Objects
| | |-- Object Methods (keys, values, entries)
| |
| |-- Sets and Maps
| | |-- Working with Sets
| | |-- Working with Maps
|
|-- Browser APIs
| |-- Document Object Model (DOM)
| | |-- Selecting Elements
| | |-- Manipulating Elements
| | |-- Event Handling
| |
| |-- Fetch API
| | |-- Making HTTP Requests
| | |-- Handling Responses
| |
| |-- Web Storage
| | |-- LocalStorage and SessionStorage
|
|-- Libraries and Frameworks
| |-- jQuery
| | |-- Basics of jQuery
| | |-- DOM Manipulation with jQuery
| |
| |-- React
| | |-- Components and JSX
| | |-- State and Props
| | |-- Lifecycle Methods
| |
| |-- Angular
| | |-- Components and Templates
| | |-- Services and Dependency Injection
| | |-- Routing
| |
| |-- Vue
| | |-- Vue Instance
| | |-- Templates and Directives
| | |-- Vue Router
|
|-- Build Tools and Module Bundlers
| |-- NPM and Yarn
| | |-- Package Management
| | |-- Scripts and Dependencies
| |
| |-- Webpack
| | |-- Module Bundling
| | |-- Loaders and Plugins
| |
| |-- Babel
| | |-- Transpiling JavaScript
| | |-- Using Presets and Plugins
|
|-- Testing in JavaScript
| |-- Unit Testing
| | |-- Jest (Setup, Writing Tests, Mocking)
| | |-- Mocha and Chai
| |
| |-- End-to-End Testing
| | |-- Cypress
| | |-- Selenium WebDriver
|
|-- Deployment and DevOps
| |-- Continuous Integration/Continuous Deployment (CI/CD)
| | |-- GitHub Actions
| | |-- Travis CI
| |
| |-- Containers and Microservices
| | |-- Docker (Dockerfile, Image Creation, Container Management)
| | |-- Kubernetes (Pods, Services, Deployments, Managing JavaScript Applications on Kubernetes)
Free JavaScript Courses ๐๐
https://udacity.com/course/intro-to-javascript--ud803
https://udemy.com/course/javascript-essentials-mini-course
https://udacity.com/course/object-oriented-javascript--ud711
https://t.me/javascriptresourcestp
https://www.udemy.com/course/code-your-first-game
Join for more free courses
https://t.me/javascriptresourcestp
ENJOY LEARNING ๐๐
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to JavaScript
| | |-- Setting Up Development Environment (IDE: VSCode, Sublime Text, etc.)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables (var, let, const) and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Switch Case
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Do-While Loop
| | |-- For...in and For...of Loops
| |
| |-- Exception Handling
| | |-- Try-Catch Block
| | |-- Finally Block
| | |-- Throwing Errors
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Declarations
| | |-- Function Expressions
| | |-- Arrow Functions
| |
| |-- Parameters and Arguments
| | |-- Default Parameters
| | |-- Rest and Spread Operators
| |
| |-- Scope
| | |-- Global and Local Scope
| | |-- Hoisting
| | |-- Closures
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Objects and Properties
| | |-- Methods
| |
| |-- Prototypes and Inheritance
| | |-- Prototype Chain
| | |-- Inheritance with Prototypes
| |
| |-- Classes
| | |-- Class Syntax
| | |-- Constructors
| | |-- Inheritance (extends and super)
| |
| |-- Encapsulation
| | |-- Private and Public Members (using # for private)
|
|-- Advanced JavaScript
| |-- Asynchronous JavaScript
| | |-- Callbacks
| | |-- Promises
| | |-- Async/Await
| |
| |-- Event Loop
| | |-- Understanding the Event Loop
| | |-- Microtasks and Macrotasks
|
|-- Data Structures
| |-- Arrays
| | |-- Array Methods (map, filter, reduce, etc.)
| | |-- Array Manipulation
| |
| |-- Objects
| | |-- Creating and Manipulating Objects
| | |-- Object Methods (keys, values, entries)
| |
| |-- Sets and Maps
| | |-- Working with Sets
| | |-- Working with Maps
|
|-- Browser APIs
| |-- Document Object Model (DOM)
| | |-- Selecting Elements
| | |-- Manipulating Elements
| | |-- Event Handling
| |
| |-- Fetch API
| | |-- Making HTTP Requests
| | |-- Handling Responses
| |
| |-- Web Storage
| | |-- LocalStorage and SessionStorage
|
|-- Libraries and Frameworks
| |-- jQuery
| | |-- Basics of jQuery
| | |-- DOM Manipulation with jQuery
| |
| |-- React
| | |-- Components and JSX
| | |-- State and Props
| | |-- Lifecycle Methods
| |
| |-- Angular
| | |-- Components and Templates
| | |-- Services and Dependency Injection
| | |-- Routing
| |
| |-- Vue
| | |-- Vue Instance
| | |-- Templates and Directives
| | |-- Vue Router
|
|-- Build Tools and Module Bundlers
| |-- NPM and Yarn
| | |-- Package Management
| | |-- Scripts and Dependencies
| |
| |-- Webpack
| | |-- Module Bundling
| | |-- Loaders and Plugins
| |
| |-- Babel
| | |-- Transpiling JavaScript
| | |-- Using Presets and Plugins
|
|-- Testing in JavaScript
| |-- Unit Testing
| | |-- Jest (Setup, Writing Tests, Mocking)
| | |-- Mocha and Chai
| |
| |-- End-to-End Testing
| | |-- Cypress
| | |-- Selenium WebDriver
|
|-- Deployment and DevOps
| |-- Continuous Integration/Continuous Deployment (CI/CD)
| | |-- GitHub Actions
| | |-- Travis CI
| |
| |-- Containers and Microservices
| | |-- Docker (Dockerfile, Image Creation, Container Management)
| | |-- Kubernetes (Pods, Services, Deployments, Managing JavaScript Applications on Kubernetes)
Free JavaScript Courses ๐๐
https://udacity.com/course/intro-to-javascript--ud803
https://udemy.com/course/javascript-essentials-mini-course
https://udacity.com/course/object-oriented-javascript--ud711
https://t.me/javascriptresourcestp
https://www.udemy.com/course/code-your-first-game
Join for more free courses
https://t.me/javascriptresourcestp
ENJOY LEARNING ๐๐
Forwarded from Machine Learning Resources TP
Complete Machine Learning Roadmap
1. Introduction to Machine Learning
- Definition
- Purpose
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
2. Mathematics for Machine Learning
- Linear Algebra
- Calculus
- Statistics and Probability
3. Programming Languages for ML
- Python and Libraries (NumPy, Pandas, Matplotlib)
- R
4. Data Preprocessing
- Handling Missing Data
- Feature Scaling
- Data Transformation
5. Exploratory Data Analysis (EDA)
- Data Visualization
- Descriptive Statistics
6. Supervised Learning
- Regression
- Classification
- Model Evaluation
7. Unsupervised Learning
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)
8. Model Selection and Evaluation
- Cross-Validation
- Hyperparameter Tuning
- Evaluation Metrics (Precision, Recall, F1 Score)
9. Ensemble Learning
- Random Forest
- Gradient Boosting
10. Neural Networks and Deep Learning
- Introduction to Neural Networks
- Building and Training Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
11. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Named Entity Recognition (NER)
12. Reinforcement Learning
- Basics
- Markov Decision Processes
- Q-Learning
13. Machine Learning Frameworks
- TensorFlow
- PyTorch
- Scikit-Learn
14. Deployment of ML Models
- Flask for Web Deployment
- Docker and Kubernetes
15. Ethical and Responsible AI
- Bias and Fairness
- Ethical Considerations
16. Machine Learning in Production
- Model Monitoring
- Continuous Integration/Continuous Deployment (CI/CD)
17. Real-world Projects and Case Studies
18. Machine Learning Resources
- Online Courses
- Books
- Blogs and Journals
๐ Learning Resources for Machine Learning:
- [Python for Machine Learning](https://t.me/pythonresourcestp/48)
- [Fast.ai: Practical Deep Learning for Coders](https://course.fast.ai/)
- [Intro to Machine Learning](https://learn.microsoft.com/en-us/training/paths/intro-to-ml-with-python/)
๐ Books: https://t.me/mlresourcestp/45
๐ Join for more free resources
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
ENJOY LEARNING! ๐๐
1. Introduction to Machine Learning
- Definition
- Purpose
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
2. Mathematics for Machine Learning
- Linear Algebra
- Calculus
- Statistics and Probability
3. Programming Languages for ML
- Python and Libraries (NumPy, Pandas, Matplotlib)
- R
4. Data Preprocessing
- Handling Missing Data
- Feature Scaling
- Data Transformation
5. Exploratory Data Analysis (EDA)
- Data Visualization
- Descriptive Statistics
6. Supervised Learning
- Regression
- Classification
- Model Evaluation
7. Unsupervised Learning
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)
8. Model Selection and Evaluation
- Cross-Validation
- Hyperparameter Tuning
- Evaluation Metrics (Precision, Recall, F1 Score)
9. Ensemble Learning
- Random Forest
- Gradient Boosting
10. Neural Networks and Deep Learning
- Introduction to Neural Networks
- Building and Training Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
11. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Named Entity Recognition (NER)
12. Reinforcement Learning
- Basics
- Markov Decision Processes
- Q-Learning
13. Machine Learning Frameworks
- TensorFlow
- PyTorch
- Scikit-Learn
14. Deployment of ML Models
- Flask for Web Deployment
- Docker and Kubernetes
15. Ethical and Responsible AI
- Bias and Fairness
- Ethical Considerations
16. Machine Learning in Production
- Model Monitoring
- Continuous Integration/Continuous Deployment (CI/CD)
17. Real-world Projects and Case Studies
18. Machine Learning Resources
- Online Courses
- Books
- Blogs and Journals
๐ Learning Resources for Machine Learning:
- [Python for Machine Learning](https://t.me/pythonresourcestp/48)
- [Fast.ai: Practical Deep Learning for Coders](https://course.fast.ai/)
- [Intro to Machine Learning](https://learn.microsoft.com/en-us/training/paths/intro-to-ml-with-python/)
๐ Books: https://t.me/mlresourcestp/45
๐ Join for more free resources
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
ENJOY LEARNING! ๐๐
Data Structures Interview Preparation
Free Programming Resources ๐๐
โ Data science and Data Analytics Free Courses by Google
https://developers.google.com/edu/python/introduction
https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field
https://cloud.google.com/data-science?hl=en
https://developers.google.com/machine-learning/crash-course
https://t.me/datascienceresourcestp/52
๐ Free Data Analytics Courses by Microsoft
1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/
3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
๐ค Free AI Courses by Microsoft
1. Fundamentals of AI by Microsoft
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
2. Introduction to AI with python by Harvard.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
๐ Useful Resources for the Programmers
Data Analyst Roadmap
https://t.me/dataanalysisresourcestp/48
Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019
Interactive React Native Resources
https://fullstackopen.com/en/part10
Python for Data Science and ML
https://t.me/pythonresourcestp/39
Ethical Hacking Course
https://t.me/zerotrusthackers/18
Unity Documentation
https://docs.unity3d.com/Manual/index.html
Advanced Javascript concepts
https://t.me/javascriptresourcestp/446
Oops in Java
https://nptel.ac.in/courses/106105224
Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction
Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em
Data Structures Interview Preparation
https://t.me/techpsyche/501
๐ป Free Programming Courses by Microsoft
โฏ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/
โฏ TypeScript
http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
โฏ C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
Join for more free resources.
https://t.me/techpsyche
ENJOY LEARNING ๐๐
๐ Join for more free resources
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
โ Data science and Data Analytics Free Courses by Google
https://developers.google.com/edu/python/introduction
https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field
https://cloud.google.com/data-science?hl=en
https://developers.google.com/machine-learning/crash-course
https://t.me/datascienceresourcestp/52
๐ Free Data Analytics Courses by Microsoft
1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/
3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
๐ค Free AI Courses by Microsoft
1. Fundamentals of AI by Microsoft
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
2. Introduction to AI with python by Harvard.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
๐ Useful Resources for the Programmers
Data Analyst Roadmap
https://t.me/dataanalysisresourcestp/48
Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019
Interactive React Native Resources
https://fullstackopen.com/en/part10
Python for Data Science and ML
https://t.me/pythonresourcestp/39
Ethical Hacking Course
https://t.me/zerotrusthackers/18
Unity Documentation
https://docs.unity3d.com/Manual/index.html
Advanced Javascript concepts
https://t.me/javascriptresourcestp/446
Oops in Java
https://nptel.ac.in/courses/106105224
Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction
Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em
Data Structures Interview Preparation
https://t.me/techpsyche/501
๐ป Free Programming Courses by Microsoft
โฏ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/
โฏ TypeScript
http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
โฏ C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
Join for more free resources.
https://t.me/techpsyche
ENJOY LEARNING ๐๐
๐ Join for more free resources
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Remote UI/UX Designer Needed at SPIRALYZE
Company: SPIRALYZE
Job Type: Fulltime
Required skills:
* Think in UI design: 3+ years of UI/UX design experience.
* Excel in UI with bonus UX experience.
* Expert in Figma: Create style libraries and handover files.
* Familiarity with HTML, CSS, and responsive frameworks.
* Stay updated on UI/UX trends.
* Overlap with EST (New York time) for 4+ hours.
* Excellent communication skills.
Apply Here:
https://kenyatrends.co.ke/remote-ui-ux-designer-needed-at-spiralyze/
Company: SPIRALYZE
Job Type: Fulltime
Required skills:
* Think in UI design: 3+ years of UI/UX design experience.
* Excel in UI with bonus UX experience.
* Expert in Figma: Create style libraries and handover files.
* Familiarity with HTML, CSS, and responsive frameworks.
* Stay updated on UI/UX trends.
* Overlap with EST (New York time) for 4+ hours.
* Excellent communication skills.
Apply Here:
https://kenyatrends.co.ke/remote-ui-ux-designer-needed-at-spiralyze/
Remote Software Engineer (Frontend, Backend, or Full stack) Job at Subscript
You'll work on:
* Backend: database design and implementation, complex data management problems, API design and implementation, writing tests
* Frontend: building features in our react app, HTML/CSS + styled components, building amazing user experiences, writing tests
* Frontend and backend: building and iterating on our product, collaborating with designers and founders, and making a product that users will love.
Apply Here:
https://kenyatrends.co.ke/remote-software-engineer-frontend-backend-or-full-stack-job-at-subscript/
You'll work on:
* Backend: database design and implementation, complex data management problems, API design and implementation, writing tests
* Frontend: building features in our react app, HTML/CSS + styled components, building amazing user experiences, writing tests
* Frontend and backend: building and iterating on our product, collaborating with designers and founders, and making a product that users will love.
Apply Here:
https://kenyatrends.co.ke/remote-software-engineer-frontend-backend-or-full-stack-job-at-subscript/
COURSE๐ฐ Master Course in CompTIA A+ (Core 1 & Core 2 - 101 Level) ๐ฐ
Size: 298 MB
๐ฅDownload Link:
https://mega.nz/file/6G5niaDJ#HgQQa5eGqe9JWWH2rzzEbwTTr-EewOPziUR475WpOBs
๐ Or Buy on Udemy:
https://udemy.com/course/master-course-in-comptia-a-core-1-core-2-101-level/
๐ขNo Copyright Infringement Intended, All The Credits & Rights Reserved to the Respected Actual Owner. We don't own this content.
Telegram Channel:
https://t.me/zerotrusthackers
WhatsApp Channel:
https://whatsapp.com/channel/0029VaxVv551iUxRku094918
100+ YouTube channels you should subscribe now as per your need
โฏ HTMLCSS โ Kevin Powell
โฏ C โ Jacob Sorber
โฏ C++ โ TheCherno
โฏ Java โ Telusko
โฏ C# โ kudvenkat
โฏ Python โ Corey Schafer
โฏ JavaScript โ developedbyed
โฏ SQL โ Joey Blue
โฏ Golang โ Jon Calhoun
โฏ Swift โ CodeWithChris
โฏ Kotlin โ PhilippLackner
โฏ PHP โ ProgramWithGio
โฏ Ruby โ DriftingRuby
โฏ Rust โ NoBoilerplate
โฏ Lua โ Steve's teacher
โฏ Scala โ DevInsideYou
โฏ Julia โ TheJuliaLanguage
โฏ MATLAB โ Joseph Delgadillo
โฏ R โ marinstatlectures
โฏ C++ โ javidx9
โฏ C++ โ LearningLad
โฏ C++ โ Trevor Payne
โฏ JavaScript โ Akshay Saini
โฏ TypeScript โ basarat
โฏ TypeScript โ TypeScriptTV
โฏ C# โ Microsoft Developer [Bob Tabor]
โฏ C# โ dotnet [ScottKendra]
โฏ SQL โ The Magic of SQL
-- Frameworks --
โฏ Node.js โ Traversy Media
โฏ React โ Codevolution
โฏ React โ Dave Gray
โฏ React โ Jack Herrington
โฏ Next.js โ Lama Dev
โฏ Vue โ Vue Mastery
โฏ Svelte โ Joy of Code
โฏ Angular โ Angular University
โฏ Django โ CodingEntrepreneurs
โฏ Laravel โ LaravelDaily
โฏ Blazor โ James Montemagno
โฏ Spring โ SpringSourceDev
โฏ SpringBoot โ amigoscode
โฏ Ruby on Rails โ GorailsTV
-- Mobile App --
โฏ React Native โ Codevolution
โฏ React Native โ Hitesh Choudhary
โฏ Flutter โ The Flutter Way
โฏ Flutter โ Tadas Petra
-- DSA --
โฏ take U forward
โฏ mycodeschool
โฏ Abdul Bari
โฏ Kunal Kushwaha
โฏ Jenny's Lectures CS IT
โฏ CodeWithHarry
-- Full Stack --
โฏ Traversy Media
โฏ NetNinja
โฏ Dave Gray
โฏ Projects
โ WebDevSimplified
โ JavaScript King
โฏ UI Design
โ developedbyed
โ DesignCourse
-- DevOps --
โฏ GIT โ The Modern Coder
โฏ Linux โ Learn Linux TV
โฏ DevOps โ DevOpsToolkit
โฏ CICD โ TechWorld with Nana
โฏ Docker โ Bret Fisher
โฏ Kubernetes โ Kubesimplify
โฏ Microservices โ freeCodeCamp
โฏ Selenium โ edureka!
โฏ Playwright โ Jaydeep Karale
-- Cloud Computing --
โฏ AWS โ amazonwebservices
โฏ Azure โ Adam Marczak
โฏ GCP โ edureka!
โฏ Serverless โ Serverless
โฏ Jenkins โ DevOps Journey
โฏ Puppet โ simplilearn
โฏ Chef โ simplilearn
โฏ Ansible โ Learn Linux TV
-- Data Science --
โฏ Mathematics
โ 3Blue1Brown
โ ProfRobBob
โ Ghrist Math
โ Numberphile
โฏ Machine Learning
โ sentdex
โ DeepLearningAI
โ StatQuest
โฏ Excel
โ ExcelIsFun
โ Kevin Stratvert
โ Chandoo
โฏ Tableau โ Tableau Tim
โฏ PowerBI
โ Guy in a Cube
โ Chandoo
โฏ Data Science
โ Krish Naik
โ Leila Gharani
โ Socratica
โฏ Data Analyst
โ AlexTheAnalyst
โ Luke Barousse
โฏ Projects โ Ken Jee
-- Code Editors --
โฏ Vim โ ThePrimeagen
โฏ VS Code โ Visual Studio Code
โฏ Jupyter Notebook โ Corey Schafer
-- Special Mentions --
โฏ Programming in 100 Sec โ Fireship
โฏ Interviews โ NeetCode
-- Free Education --
โ freecodecamp
โ Simplilearn
โ edureka!
-- Most Valuable --
โ TechWithTim
โ programmingwithmosh
โ Traversy Media
โ BroCodez
โ thenewboston
โ Telusko
โ Derek Banas
โ CodeWithHarry
โ MySirG .com
โ TechWorld with Nana
โ KodeKloud
Telegram Channel: https://t.me/techpsyche
WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
โฏ HTMLCSS โ Kevin Powell
โฏ C โ Jacob Sorber
โฏ C++ โ TheCherno
โฏ Java โ Telusko
โฏ C# โ kudvenkat
โฏ Python โ Corey Schafer
โฏ JavaScript โ developedbyed
โฏ SQL โ Joey Blue
โฏ Golang โ Jon Calhoun
โฏ Swift โ CodeWithChris
โฏ Kotlin โ PhilippLackner
โฏ PHP โ ProgramWithGio
โฏ Ruby โ DriftingRuby
โฏ Rust โ NoBoilerplate
โฏ Lua โ Steve's teacher
โฏ Scala โ DevInsideYou
โฏ Julia โ TheJuliaLanguage
โฏ MATLAB โ Joseph Delgadillo
โฏ R โ marinstatlectures
โฏ C++ โ javidx9
โฏ C++ โ LearningLad
โฏ C++ โ Trevor Payne
โฏ JavaScript โ Akshay Saini
โฏ TypeScript โ basarat
โฏ TypeScript โ TypeScriptTV
โฏ C# โ Microsoft Developer [Bob Tabor]
โฏ C# โ dotnet [ScottKendra]
โฏ SQL โ The Magic of SQL
-- Frameworks --
โฏ Node.js โ Traversy Media
โฏ React โ Codevolution
โฏ React โ Dave Gray
โฏ React โ Jack Herrington
โฏ Next.js โ Lama Dev
โฏ Vue โ Vue Mastery
โฏ Svelte โ Joy of Code
โฏ Angular โ Angular University
โฏ Django โ CodingEntrepreneurs
โฏ Laravel โ LaravelDaily
โฏ Blazor โ James Montemagno
โฏ Spring โ SpringSourceDev
โฏ SpringBoot โ amigoscode
โฏ Ruby on Rails โ GorailsTV
-- Mobile App --
โฏ React Native โ Codevolution
โฏ React Native โ Hitesh Choudhary
โฏ Flutter โ The Flutter Way
โฏ Flutter โ Tadas Petra
-- DSA --
โฏ take U forward
โฏ mycodeschool
โฏ Abdul Bari
โฏ Kunal Kushwaha
โฏ Jenny's Lectures CS IT
โฏ CodeWithHarry
-- Full Stack --
โฏ Traversy Media
โฏ NetNinja
โฏ Dave Gray
โฏ Projects
โ WebDevSimplified
โ JavaScript King
โฏ UI Design
โ developedbyed
โ DesignCourse
-- DevOps --
โฏ GIT โ The Modern Coder
โฏ Linux โ Learn Linux TV
โฏ DevOps โ DevOpsToolkit
โฏ CICD โ TechWorld with Nana
โฏ Docker โ Bret Fisher
โฏ Kubernetes โ Kubesimplify
โฏ Microservices โ freeCodeCamp
โฏ Selenium โ edureka!
โฏ Playwright โ Jaydeep Karale
-- Cloud Computing --
โฏ AWS โ amazonwebservices
โฏ Azure โ Adam Marczak
โฏ GCP โ edureka!
โฏ Serverless โ Serverless
โฏ Jenkins โ DevOps Journey
โฏ Puppet โ simplilearn
โฏ Chef โ simplilearn
โฏ Ansible โ Learn Linux TV
-- Data Science --
โฏ Mathematics
โ 3Blue1Brown
โ ProfRobBob
โ Ghrist Math
โ Numberphile
โฏ Machine Learning
โ sentdex
โ DeepLearningAI
โ StatQuest
โฏ Excel
โ ExcelIsFun
โ Kevin Stratvert
โ Chandoo
โฏ Tableau โ Tableau Tim
โฏ PowerBI
โ Guy in a Cube
โ Chandoo
โฏ Data Science
โ Krish Naik
โ Leila Gharani
โ Socratica
โฏ Data Analyst
โ AlexTheAnalyst
โ Luke Barousse
โฏ Projects โ Ken Jee
-- Code Editors --
โฏ Vim โ ThePrimeagen
โฏ VS Code โ Visual Studio Code
โฏ Jupyter Notebook โ Corey Schafer
-- Special Mentions --
โฏ Programming in 100 Sec โ Fireship
โฏ Interviews โ NeetCode
-- Free Education --
โ freecodecamp
โ Simplilearn
โ edureka!
-- Most Valuable --
โ TechWithTim
โ programmingwithmosh
โ Traversy Media
โ BroCodez
โ thenewboston
โ Telusko
โ Derek Banas
โ CodeWithHarry
โ MySirG .com
โ TechWorld with Nana
โ KodeKloud
Telegram Channel: https://t.me/techpsyche
WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
๐1
Understanding Essential Charts:
1๏ธโฃ Line Charts: These are your go-to for tracking trends over time.
2๏ธโฃ Bar Charts: Perfect for comparing different categories, like sales in different regions or the popularity of different products.
3๏ธโฃ Pie Charts: These are all about showing proportions.
4๏ธโฃ Scatter Plots: If you're trying to find relationships between variables, scatter plots are your friend.
5๏ธโฃ Histograms: how data is distributed, histograms are your tool of choice.
Data Visualization Tools: https://t.me/dataanalysisresourcestp/70
๐ ๐ฆ๐ฎ๐ฌ๐ญ-๐๐จ F๐ซ๐๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ ๐๐จ๐ฎ๐ซ๐ฌ๐๐ฌ: https://tinyurl.com/m239d2s8
Follow this WhatsApp Channel for More Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
1๏ธโฃ Line Charts: These are your go-to for tracking trends over time.
2๏ธโฃ Bar Charts: Perfect for comparing different categories, like sales in different regions or the popularity of different products.
3๏ธโฃ Pie Charts: These are all about showing proportions.
4๏ธโฃ Scatter Plots: If you're trying to find relationships between variables, scatter plots are your friend.
5๏ธโฃ Histograms: how data is distributed, histograms are your tool of choice.
Data Visualization Tools: https://t.me/dataanalysisresourcestp/70
๐ ๐ฆ๐ฎ๐ฌ๐ญ-๐๐จ F๐ซ๐๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ ๐๐จ๐ฎ๐ซ๐ฌ๐๐ฌ: https://tinyurl.com/m239d2s8
Follow this WhatsApp Channel for More Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
โค1
2025 Google Conference Scholarships
Perks:
* Roundtrip airfare
* Conference registration
* $100 stipend
* Hotel accommodation
Criteria:
* Be a full-time student enrolled with a recognized university in Africa OR Asia Pacific(APAC) who is in need of conference travel funds.
* If you're Non-African, also check the provided link for appropriate option.
Apply here:
https://kenyatrends.co.ke/2025-google-conference-scholarships/
Perks:
* Roundtrip airfare
* Conference registration
* $100 stipend
* Hotel accommodation
Criteria:
* Be a full-time student enrolled with a recognized university in Africa OR Asia Pacific(APAC) who is in need of conference travel funds.
* If you're Non-African, also check the provided link for appropriate option.
Apply here:
https://kenyatrends.co.ke/2025-google-conference-scholarships/