Forwarded from Python for Data Analysts
๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ ๐๐ถ๐๐ต ๐๐ฎ๐ฟ๐๐ฎ๐ฟ๐ฑ ๐จ๐ป๐ถ๐๐ฒ๐ฟ๐๐ถ๐๐๐
๐ฏ Want to break into Data Science without spending a single rupee?๐ฐ
Harvard University is offering a goldmine of free courses that make top-tier education accessible to anyone, anywhere๐จโ๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3HxOgTW
These courses are designed by Ivy League experts and are trusted by thousands globallyโ ๏ธ
๐ฏ Want to break into Data Science without spending a single rupee?๐ฐ
Harvard University is offering a goldmine of free courses that make top-tier education accessible to anyone, anywhere๐จโ๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3HxOgTW
These courses are designed by Ivy League experts and are trusted by thousands globallyโ ๏ธ
Moving Login Button Project (Troll Form ๐)
If the user enters an incorrect password, the "Login" button moves away!
<html>
<head>
<style>
.container {
text-align: center;
margin-top: 50px;
}
.btn {
position: relative;
padding: 10px 20px;
font-size: 16px;
cursor: pointer;
}
</style>
<script>
function moveButton() {
let password = document.getElementById("password").value;
let button = document.getElementById("btn");
if (password !== "legend") {
let x = Math.random() * (window.innerWidth - 100);
let y = Math.random() * (window.innerHeight - 100);
button.style.position = "absolute";
button.style.left = x + "px";
button.style.top = y + "px";
}
}
</script>
</head>
<body>
<div class="container">
<h2>Enter Password</h2>
<input type="password" id="password" placeholder="Type something..." oninput="moveButton()">
<br><br>
<button id="btn" class="btn" onclick="alert('You got lucky!')">Login</button>
</div>
</body>
</html>
How It Works?
If the user types anything other than "legend", the login button starts running away from the cursor.
If the correct password is entered, the button stays in place.
Web Development Best Resources
ENJOY LEARNING ๐๐
#webdev
If the user enters an incorrect password, the "Login" button moves away!
<html>
<head>
<style>
.container {
text-align: center;
margin-top: 50px;
}
.btn {
position: relative;
padding: 10px 20px;
font-size: 16px;
cursor: pointer;
}
</style>
<script>
function moveButton() {
let password = document.getElementById("password").value;
let button = document.getElementById("btn");
if (password !== "legend") {
let x = Math.random() * (window.innerWidth - 100);
let y = Math.random() * (window.innerHeight - 100);
button.style.position = "absolute";
button.style.left = x + "px";
button.style.top = y + "px";
}
}
</script>
</head>
<body>
<div class="container">
<h2>Enter Password</h2>
<input type="password" id="password" placeholder="Type something..." oninput="moveButton()">
<br><br>
<button id="btn" class="btn" onclick="alert('You got lucky!')">Login</button>
</div>
</body>
</html>
How It Works?
If the user types anything other than "legend", the login button starts running away from the cursor.
If the correct password is entered, the button stays in place.
Web Development Best Resources
ENJOY LEARNING ๐๐
#webdev
โค2๐ฅ1
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ณ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐จ๐ฝ๐ด๐ฟ๐ฎ๐ฑ๐ฒ ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ฒ๐๐๐บ๐ฒ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ ๐ฎ๐ป๐ฑ ๐ฆ๐๐ฎ๐ป๐ฑ ๐ข๐๐๐
๐ Want to Make Your Resume Stand Out in 2025?โจ๏ธ
If youโre aiming to boost your chances in job interviews or want to upgrade your resume with powerful, in-demand skills โ start with these 7 free online courses๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3SJ91OV
Empower yourself and take your career to the next level! โ
๐ Want to Make Your Resume Stand Out in 2025?โจ๏ธ
If youโre aiming to boost your chances in job interviews or want to upgrade your resume with powerful, in-demand skills โ start with these 7 free online courses๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3SJ91OV
Empower yourself and take your career to the next level! โ
If you want to Excel in Data Science and become an expert, master these essential concepts:
Core Data Science Skills:
โข Python for Data Science โ Pandas, NumPy, Matplotlib, Seaborn
โข SQL for Data Extraction โ SELECT, JOIN, GROUP BY, CTEs, Window Functions
โข Data Cleaning & Preprocessing โ Handling missing data, outliers, duplicates
โข Exploratory Data Analysis (EDA) โ Visualizing data trends
Machine Learning (ML):
โข Supervised Learning โ Linear Regression, Decision Trees, Random Forest
โข Unsupervised Learning โ Clustering, PCA, Anomaly Detection
โข Model Evaluation โ Cross-validation, Confusion Matrix, ROC-AUC
โข Hyperparameter Tuning โ Grid Search, Random Search
Deep Learning (DL):
โข Neural Networks โ TensorFlow, PyTorch, Keras
โข CNNs & RNNs โ Image & sequential data processing
โข Transformers & LLMs โ GPT, BERT, Stable Diffusion
Big Data & Cloud Computing:
โข Hadoop & Spark โ Handling large datasets
โข AWS, GCP, Azure โ Cloud-based data science solutions
โข MLOps โ Deploy models using Flask, FastAPI, Docker
Statistics & Mathematics for Data Science:
โข Probability & Hypothesis Testing โ P-values, T-tests, Chi-square
โข Linear Algebra & Calculus โ Matrices, Vectors, Derivatives
โข Time Series Analysis โ ARIMA, Prophet, LSTMs
Real-World Applications:
โข Recommendation Systems โ Personalized AI suggestions
โข NLP (Natural Language Processing) โ Sentiment Analysis, Chatbots
โข AI-Powered Business Insights โ Data-driven decision-making
Like this post if you need a complete tutorial on essential data science topics! ๐โค๏ธ
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Core Data Science Skills:
โข Python for Data Science โ Pandas, NumPy, Matplotlib, Seaborn
โข SQL for Data Extraction โ SELECT, JOIN, GROUP BY, CTEs, Window Functions
โข Data Cleaning & Preprocessing โ Handling missing data, outliers, duplicates
โข Exploratory Data Analysis (EDA) โ Visualizing data trends
Machine Learning (ML):
โข Supervised Learning โ Linear Regression, Decision Trees, Random Forest
โข Unsupervised Learning โ Clustering, PCA, Anomaly Detection
โข Model Evaluation โ Cross-validation, Confusion Matrix, ROC-AUC
โข Hyperparameter Tuning โ Grid Search, Random Search
Deep Learning (DL):
โข Neural Networks โ TensorFlow, PyTorch, Keras
โข CNNs & RNNs โ Image & sequential data processing
โข Transformers & LLMs โ GPT, BERT, Stable Diffusion
Big Data & Cloud Computing:
โข Hadoop & Spark โ Handling large datasets
โข AWS, GCP, Azure โ Cloud-based data science solutions
โข MLOps โ Deploy models using Flask, FastAPI, Docker
Statistics & Mathematics for Data Science:
โข Probability & Hypothesis Testing โ P-values, T-tests, Chi-square
โข Linear Algebra & Calculus โ Matrices, Vectors, Derivatives
โข Time Series Analysis โ ARIMA, Prophet, LSTMs
Real-World Applications:
โข Recommendation Systems โ Personalized AI suggestions
โข NLP (Natural Language Processing) โ Sentiment Analysis, Chatbots
โข AI-Powered Business Insights โ Data-driven decision-making
Like this post if you need a complete tutorial on essential data science topics! ๐โค๏ธ
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
โค3
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ฐ ๐๐ถ๐ด๐ต-๐๐บ๐ฝ๐ฎ๐ฐ๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐๐ผ ๐๐ฎ๐๐ป๐ฐ๐ต ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
These globally recognized certifications from platforms like Google, IBM, Microsoft, and DataCamp are beginner-friendly, industry-aligned, and designed to make you job-ready in just a few weeks
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4kC18XE
These courses help you gain hands-on experience โ exactly what top MNCs look for!โ ๏ธ
These globally recognized certifications from platforms like Google, IBM, Microsoft, and DataCamp are beginner-friendly, industry-aligned, and designed to make you job-ready in just a few weeks
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4kC18XE
These courses help you gain hands-on experience โ exactly what top MNCs look for!โ ๏ธ
Forwarded from Python for Data Analysts
๐ญ๐ฌ๐ฌ๐ฌ+ ๐๐ฟ๐ฒ๐ฒ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฒ๐ฑ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฏ๐ ๐๐ป๐ณ๐ผ๐๐๐ โ ๐๐ฒ๐ฎ๐ฟ๐ป, ๐๐ฟ๐ผ๐, ๐ฆ๐๐ฐ๐ฐ๐ฒ๐ฒ๐ฑ!๐
๐ Looking to upgrade your skills without spending a rupee?๐ฐ
Hereโs your golden opportunity to unlock 1,000+ certified online courses across technology, business, communication, leadership, soft skills, and much more โ all absolutely FREE on Infosys Springboard!๐ฅ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/43UcmQ7
Save this blog, sign up, and start your upskilling journey today!โ ๏ธ
๐ Looking to upgrade your skills without spending a rupee?๐ฐ
Hereโs your golden opportunity to unlock 1,000+ certified online courses across technology, business, communication, leadership, soft skills, and much more โ all absolutely FREE on Infosys Springboard!๐ฅ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/43UcmQ7
Save this blog, sign up, and start your upskilling journey today!โ ๏ธ
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐๐ฟ๐ฒ๐ฒ ๐ฃ๐๐๐ต๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ: ๐ง๐ต๐ฒ ๐๐ฒ๐๐ ๐ฆ๐๐ฎ๐ฟ๐๐ถ๐ป๐ด ๐ฃ๐ผ๐ถ๐ป๐ ๐ณ๐ผ๐ฟ ๐ง๐ฒ๐ฐ๐ต & ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ๐๐
๐ Want to break into tech or data analytics but donโt know how to start?๐โจ๏ธ
Python is the #1 most in-demand programming language, and Scalerโs free Python for Beginners course is a game-changer for absolute beginners๐โ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/45TroYX
No coding background needed!โ ๏ธ
๐ Want to break into tech or data analytics but donโt know how to start?๐โจ๏ธ
Python is the #1 most in-demand programming language, and Scalerโs free Python for Beginners course is a game-changer for absolute beginners๐โ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/45TroYX
No coding background needed!โ ๏ธ
โค1
Python Learning Plan in 2025
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
Here you can find essential Python Interview Resources๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this ๐โฅ๏ธ
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
Here you can find essential Python Interview Resources๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this ๐โฅ๏ธ
โค2
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
๐ญ๐ฌ๐ฌ% ๐๐ฟ๐ฒ๐ฒ ๐ง๐ฒ๐ฐ๐ต ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
From data science and AI to web development and cloud computing, checkout Top 5 Websites for Free Tech Certification Courses in 2025
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4e76jMX
Enroll For FREE & Get Certified!โ ๏ธ
From data science and AI to web development and cloud computing, checkout Top 5 Websites for Free Tech Certification Courses in 2025
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4e76jMX
Enroll For FREE & Get Certified!โ ๏ธ
โค1
ReverseEngineeringCodewithIDAPro.pdf
4.2 MB
Reverse Engineering
PHP_7_Programming_Cookbook.pdf
13.5 MB
PHP 7 Programming (Packthub)
Py_DS_Algo.pdf
1.2 MB
Py_DS_Algo.pdf
โค3
๐ฑ ๐๐ผ๐ฑ๐ถ๐ป๐ด ๐๐ต๐ฎ๐น๐น๐ฒ๐ป๐ด๐ฒ๐ ๐ง๐ต๐ฎ๐ ๐๐ฐ๐๐๐ฎ๐น๐น๐ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐๐ ๐ป
You donโt need to be a LeetCode grandmaster.
But data science interviews still test your problem-solving mindsetโand these 5 types of challenges are the ones that actually matter.
Hereโs what to focus on (with examples) ๐
๐น 1. String Manipulation (Common in Data Cleaning)
โ Parse messy columns (e.g., split โName_Age_Cityโ)
โ Regex to extract phone numbers, emails, URLs
โ Remove stopwords or HTML tags in text data
Example: Clean up a scraped dataset from LinkedIn bias
๐น 2. GroupBy and Aggregation with Pandas
โ Group sales data by product/region
โ Calculate avg, sum, count using .groupby()
โ Handle missing values smartly
Example: โWhatโs the top-selling product in each region?โ
๐น 3. SQL Join + Window Functions
โ INNER JOIN, LEFT JOIN to merge tables
โ ROW_NUMBER(), RANK(), LEAD(), LAG() for trends
โ Use CTEs to break complex queries
Example: โGet 2nd highest salary in each departmentโ
๐น 4. Data Structures: Lists, Dicts, Sets in Python
โ Use dictionaries to map, filter, and count
โ Remove duplicates with sets
โ List comprehensions for clean solutions
Example: โCount frequency of hashtags in tweetsโ
๐น 5. Basic Algorithms (Not DP or Graphs)
โ Sliding window for moving averages
โ Two pointers for duplicate detection
โ Binary search in sorted arrays
Example: โDetect if a pair of values sum to 100โ
๐ฏ Tip: Practice challenges that feel like real-world data work, not textbook CS exams.
Use platforms like:
StrataScratch
Hackerrank (SQL + Python)
Kaggle Code
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
You donโt need to be a LeetCode grandmaster.
But data science interviews still test your problem-solving mindsetโand these 5 types of challenges are the ones that actually matter.
Hereโs what to focus on (with examples) ๐
๐น 1. String Manipulation (Common in Data Cleaning)
โ Parse messy columns (e.g., split โName_Age_Cityโ)
โ Regex to extract phone numbers, emails, URLs
โ Remove stopwords or HTML tags in text data
Example: Clean up a scraped dataset from LinkedIn bias
๐น 2. GroupBy and Aggregation with Pandas
โ Group sales data by product/region
โ Calculate avg, sum, count using .groupby()
โ Handle missing values smartly
Example: โWhatโs the top-selling product in each region?โ
๐น 3. SQL Join + Window Functions
โ INNER JOIN, LEFT JOIN to merge tables
โ ROW_NUMBER(), RANK(), LEAD(), LAG() for trends
โ Use CTEs to break complex queries
Example: โGet 2nd highest salary in each departmentโ
๐น 4. Data Structures: Lists, Dicts, Sets in Python
โ Use dictionaries to map, filter, and count
โ Remove duplicates with sets
โ List comprehensions for clean solutions
Example: โCount frequency of hashtags in tweetsโ
๐น 5. Basic Algorithms (Not DP or Graphs)
โ Sliding window for moving averages
โ Two pointers for duplicate detection
โ Binary search in sorted arrays
Example: โDetect if a pair of values sum to 100โ
๐ฏ Tip: Practice challenges that feel like real-world data work, not textbook CS exams.
Use platforms like:
StrataScratch
Hackerrank (SQL + Python)
Kaggle Code
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
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๐Here's a breakdown of SQL interview questions covering various topics:
๐บBasic SQL Concepts:
-Differentiate between SQL and NoSQL databases.
-List common data types in SQL.
๐บQuerying:
-Retrieve all records from a table named "Customers."
-Contrast SELECT and SELECT DISTINCT.
-Explain the purpose of the WHERE clause.
๐บJoins:
-Describe types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
-Retrieve data from two tables using INNER JOIN.
๐บAggregate Functions:
-Define aggregate functions and name a few.
-Calculate average, sum, and count of a column in SQL.
๐บGrouping and Filtering:
-Explain the GROUP BY clause and its use.
-Filter SQL query results using the HAVING clause.
๐บSubqueries:
-Define a subquery and provide an example.
๐บIndexes and Optimization:
-Discuss the importance of indexes in a database.
&Optimize a slow-running SQL query.
๐บNormalization and Data Integrity:
-Define database normalization and its significance.
-Enforce data integrity in a SQL database.
๐บTransactions:
-Define a SQL transaction and its purpose.
-Explain ACID properties in database transactions.
๐บViews and Stored Procedures:
-Define a database view and its use.
-Distinguish a stored procedure from a regular SQL query.
๐บAdvanced SQL:
-Write a recursive SQL query and explain its use.
-Explain window functions in SQL.
โ ๐These questions offer a comprehensive assessment of SQL knowledge, ranging from basics to advanced concepts.
โค๏ธLike if you'd like answers in the next post! ๐
๐Be the first one to know the latest Job openings ๐
https://t.me/jobs_SQL
๐บBasic SQL Concepts:
-Differentiate between SQL and NoSQL databases.
-List common data types in SQL.
๐บQuerying:
-Retrieve all records from a table named "Customers."
-Contrast SELECT and SELECT DISTINCT.
-Explain the purpose of the WHERE clause.
๐บJoins:
-Describe types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
-Retrieve data from two tables using INNER JOIN.
๐บAggregate Functions:
-Define aggregate functions and name a few.
-Calculate average, sum, and count of a column in SQL.
๐บGrouping and Filtering:
-Explain the GROUP BY clause and its use.
-Filter SQL query results using the HAVING clause.
๐บSubqueries:
-Define a subquery and provide an example.
๐บIndexes and Optimization:
-Discuss the importance of indexes in a database.
&Optimize a slow-running SQL query.
๐บNormalization and Data Integrity:
-Define database normalization and its significance.
-Enforce data integrity in a SQL database.
๐บTransactions:
-Define a SQL transaction and its purpose.
-Explain ACID properties in database transactions.
๐บViews and Stored Procedures:
-Define a database view and its use.
-Distinguish a stored procedure from a regular SQL query.
๐บAdvanced SQL:
-Write a recursive SQL query and explain its use.
-Explain window functions in SQL.
โ ๐These questions offer a comprehensive assessment of SQL knowledge, ranging from basics to advanced concepts.
โค๏ธLike if you'd like answers in the next post! ๐
๐Be the first one to know the latest Job openings ๐
https://t.me/jobs_SQL
โค2
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