๐ ๐ผ๐๐ ๐๐๐ธ๐ฒ๐ฑ ๐ฆ๐ค๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐ ๐ฎ๐ ๐ ๐๐๐ก๐ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐๐ฅ๐ฅ
1. How do you retrieve all columns from a table?
SELECT * FROM table_name;
2. What SQL statement is used to filter records?
SELECT * FROM table_name
WHERE condition;
The WHERE clause is used to filter records based on a specified condition.
3. How can you join multiple tables? Describe different types of JOINs.
SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;
Types of JOINs:
1. INNER JOIN: Returns records with matching values in both tables
SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;
2. LEFT JOIN (or LEFT OUTER JOIN): Returns all records from the left table and matched records from the right table. Unmatched records will have NULL values.
SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;
3. RIGHT JOIN (or RIGHT OUTER JOIN): Returns all records from the right table and matched records from the left table. Unmatched records will have NULL values.
SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;
4. FULL JOIN (or FULL OUTER JOIN): Returns records when there is a match in either left or right table. Unmatched records will have NULL values.
SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;
4. What is the difference between WHERE and HAVING clauses?
WHERE: Filters records before any groupings are made.
SELECT * FROM table_name
WHERE condition;
HAVING: Filters records after groupings are made.
SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;
5. How do you count the number of records in a table?
SELECT COUNT(*) FROM table_name;
This query counts all the records in the specified table.
6. How do you calculate average, sum, minimum, and maximum values in a column?
Average: SELECT AVG(column_name) FROM table_name;
Sum: SELECT SUM(column_name) FROM table_name;
Minimum: SELECT MIN(column_name) FROM table_name;
Maximum: SELECT MAX(column_name) FROM table_name;
7. What is a subquery, and how do you use it?
Subquery: A query nested inside another query
SELECT * FROM table_name
WHERE column_name = (SELECT column_name FROM another_table WHERE condition);
Till then keep learning and keep exploring ๐
1. How do you retrieve all columns from a table?
SELECT * FROM table_name;
2. What SQL statement is used to filter records?
SELECT * FROM table_name
WHERE condition;
The WHERE clause is used to filter records based on a specified condition.
3. How can you join multiple tables? Describe different types of JOINs.
SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;
Types of JOINs:
1. INNER JOIN: Returns records with matching values in both tables
SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;
2. LEFT JOIN (or LEFT OUTER JOIN): Returns all records from the left table and matched records from the right table. Unmatched records will have NULL values.
SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;
3. RIGHT JOIN (or RIGHT OUTER JOIN): Returns all records from the right table and matched records from the left table. Unmatched records will have NULL values.
SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;
4. FULL JOIN (or FULL OUTER JOIN): Returns records when there is a match in either left or right table. Unmatched records will have NULL values.
SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;
4. What is the difference between WHERE and HAVING clauses?
WHERE: Filters records before any groupings are made.
SELECT * FROM table_name
WHERE condition;
HAVING: Filters records after groupings are made.
SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;
5. How do you count the number of records in a table?
SELECT COUNT(*) FROM table_name;
This query counts all the records in the specified table.
6. How do you calculate average, sum, minimum, and maximum values in a column?
Average: SELECT AVG(column_name) FROM table_name;
Sum: SELECT SUM(column_name) FROM table_name;
Minimum: SELECT MIN(column_name) FROM table_name;
Maximum: SELECT MAX(column_name) FROM table_name;
7. What is a subquery, and how do you use it?
Subquery: A query nested inside another query
SELECT * FROM table_name
WHERE column_name = (SELECT column_name FROM another_table WHERE condition);
Till then keep learning and keep exploring ๐
โค7
Python for Data Analysis: Must-Know Libraries ๐๐
Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.
๐ฅ Essential Python Libraries for Data Analysis:
โ Pandas โ The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.
๐ Example: Loading a CSV file and displaying the first 5 rows:
โ NumPy โ Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.
๐ Example: Creating an array and performing basic operations:
โ Matplotlib & Seaborn โ These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.
๐ Example: Creating a basic bar chart:
โ Scikit-Learn โ A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.
โ OpenPyXL โ Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.
๐ก Challenge for You!
Try writing a Python script that:
1๏ธโฃ Reads a CSV file
2๏ธโฃ Cleans missing data
3๏ธโฃ Creates a simple visualization
React with โฅ๏ธ if you want me to post the script for above challenge! โฌ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.
๐ฅ Essential Python Libraries for Data Analysis:
โ Pandas โ The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.
๐ Example: Loading a CSV file and displaying the first 5 rows:
import pandas as pd df = pd.read_csv('data.csv') print(df.head()) โ NumPy โ Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.
๐ Example: Creating an array and performing basic operations:
import numpy as np arr = np.array([10, 20, 30]) print(arr.mean()) # Calculates the average
โ Matplotlib & Seaborn โ These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.
๐ Example: Creating a basic bar chart:
import matplotlib.pyplot as plt plt.bar(['A', 'B', 'C'], [5, 7, 3]) plt.show()
โ Scikit-Learn โ A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.
โ OpenPyXL โ Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.
๐ก Challenge for You!
Try writing a Python script that:
1๏ธโฃ Reads a CSV file
2๏ธโฃ Cleans missing data
3๏ธโฃ Creates a simple visualization
React with โฅ๏ธ if you want me to post the script for above challenge! โฌ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค5๐ฅ4
Machine Learning โ Essential Concepts ๐
1๏ธโฃ Types of Machine Learning
Supervised Learning โ Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning โ Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning โ Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2๏ธโฃ Key Algorithms
Regression โ Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification โ Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naรฏve Bayes).
Clustering โ Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction โ Reduces the number of features (PCA, t-SNE, LDA).
3๏ธโฃ Model Training & Evaluation
Train-Test Split โ Dividing data into training and testing sets.
Cross-Validation โ Splitting data multiple times for better accuracy.
Metrics โ Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4๏ธโฃ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5๏ธโฃ Overfitting & Underfitting
Overfitting โ Model learns noise, performs well on training but poorly on test data.
Underfitting โ Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6๏ธโฃ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7๏ธโฃ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8๏ธโฃ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
1๏ธโฃ Types of Machine Learning
Supervised Learning โ Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning โ Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning โ Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2๏ธโฃ Key Algorithms
Regression โ Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification โ Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naรฏve Bayes).
Clustering โ Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction โ Reduces the number of features (PCA, t-SNE, LDA).
3๏ธโฃ Model Training & Evaluation
Train-Test Split โ Dividing data into training and testing sets.
Cross-Validation โ Splitting data multiple times for better accuracy.
Metrics โ Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4๏ธโฃ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5๏ธโฃ Overfitting & Underfitting
Overfitting โ Model learns noise, performs well on training but poorly on test data.
Underfitting โ Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6๏ธโฃ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7๏ธโฃ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8๏ธโฃ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
โค5
Best YouTube Playlists for Data Science
โถ๏ธ Python
๐ Playlist Link
โถ๏ธ SQL
๐ Playlist Link
โถ๏ธ Data Analysis
๐ Playlist Link
โถ๏ธ Data Analyst
๐ Playlist Link
โถ๏ธ Linear Algebra
๐ Playlist Link
โถ๏ธ Calculus
๐ Playlist Link
โถ๏ธ Statistics
๐ Playlist Link
โถ๏ธ Machine Learning
๐ Playlist Link
โถ๏ธ Deep Learning
๐ Playlist Link
โถ๏ธ Excel Power Query
๐ Playlist Link
โถ๏ธ Ruby
๐ Playlist Link
โถ๏ธ Microsoft Excel
๐ Playlist Link
โถ๏ธ Python
๐ Playlist Link
โถ๏ธ SQL
๐ Playlist Link
โถ๏ธ Data Analysis
๐ Playlist Link
โถ๏ธ Data Analyst
๐ Playlist Link
โถ๏ธ Linear Algebra
๐ Playlist Link
โถ๏ธ Calculus
๐ Playlist Link
โถ๏ธ Statistics
๐ Playlist Link
โถ๏ธ Machine Learning
๐ Playlist Link
โถ๏ธ Deep Learning
๐ Playlist Link
โถ๏ธ Excel Power Query
๐ Playlist Link
โถ๏ธ Ruby
๐ Playlist Link
โถ๏ธ Microsoft Excel
๐ Playlist Link
โค12๐ฅ2
10 Ways to Speed Up Your Python Code
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Pythonโs built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python script, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you canโt make use of dictionaries or sets.
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Pythonโs built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python script, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you canโt make use of dictionaries or sets.
โค9
Frontend Development Interview Questions
Beginner Level
1. What are semantic HTML tags?
2. Difference between id and class in HTML?
3. What is the Box Model in CSS?
4. Difference between margin and padding?
5. What is a responsive web design?
6. What is the use of the <meta viewport> tag?
7. Difference between inline, block, and inline-block elements?
8. What is the difference between == and === in JavaScript?
9. What are arrow functions in JavaScript?
10. What is DOM and how is it used?
Intermediate Level
1. What are pseudo-classes and pseudo-elements in CSS?
2. How do media queries work in responsive design?
3. Difference between relative, absolute, fixed, and sticky positioning?
4. What is the event loop in JavaScript?
5. Explain closures in JavaScript with an example.
6. What are Promises and how do you handle errors with .catch()?
7. What is a higher-order function?
8. What is the difference between localStorage and sessionStorage?
9. How does this keyword work in different contexts?
10. What is JSX in React?
Advanced Level
1. How does the virtual DOM work in React?
2. What are controlled vs uncontrolled components in React?
3. What is useMemo and when should you use it?
4. How do you optimize a large React app for performance?
5. What are React lifecycle methods (class-based) and their hook equivalents?
6. How does Redux work and when should you use it?
7. What is code splitting and why is it useful?
8. How do you secure a frontend app from XSS attacks?
9. Explain the concept of Server-Side Rendering (SSR) vs Client-Side Rendering (CSR).
10. What are Web Components and how do they work?
React โค๏ธ for the detailed answers
Join for free resources: ๐ https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
Beginner Level
1. What are semantic HTML tags?
2. Difference between id and class in HTML?
3. What is the Box Model in CSS?
4. Difference between margin and padding?
5. What is a responsive web design?
6. What is the use of the <meta viewport> tag?
7. Difference between inline, block, and inline-block elements?
8. What is the difference between == and === in JavaScript?
9. What are arrow functions in JavaScript?
10. What is DOM and how is it used?
Intermediate Level
1. What are pseudo-classes and pseudo-elements in CSS?
2. How do media queries work in responsive design?
3. Difference between relative, absolute, fixed, and sticky positioning?
4. What is the event loop in JavaScript?
5. Explain closures in JavaScript with an example.
6. What are Promises and how do you handle errors with .catch()?
7. What is a higher-order function?
8. What is the difference between localStorage and sessionStorage?
9. How does this keyword work in different contexts?
10. What is JSX in React?
Advanced Level
1. How does the virtual DOM work in React?
2. What are controlled vs uncontrolled components in React?
3. What is useMemo and when should you use it?
4. How do you optimize a large React app for performance?
5. What are React lifecycle methods (class-based) and their hook equivalents?
6. How does Redux work and when should you use it?
7. What is code splitting and why is it useful?
8. How do you secure a frontend app from XSS attacks?
9. Explain the concept of Server-Side Rendering (SSR) vs Client-Side Rendering (CSR).
10. What are Web Components and how do they work?
React โค๏ธ for the detailed answers
Join for free resources: ๐ https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
โค4
Roadmap to Becoming a Python Developer ๐
1. Basics ๐ฑ
- Learn programming fundamentals and Python syntax.
2. Core Python ๐ง
- Master data structures, functions, and OOP.
3. Advanced Python ๐
- Explore modules, file handling, and exceptions.
4. Web Development ๐
- Use Django or Flask; build REST APIs.
5. Data Science ๐
- Learn NumPy, pandas, and Matplotlib.
6. Projects & Practice๐ก
- Build projects, contribute to open-source, join communities.
Like for more โค๏ธ
ENJOY LEARNING ๐๐
1. Basics ๐ฑ
- Learn programming fundamentals and Python syntax.
2. Core Python ๐ง
- Master data structures, functions, and OOP.
3. Advanced Python ๐
- Explore modules, file handling, and exceptions.
4. Web Development ๐
- Use Django or Flask; build REST APIs.
5. Data Science ๐
- Learn NumPy, pandas, and Matplotlib.
6. Projects & Practice๐ก
- Build projects, contribute to open-source, join communities.
Like for more โค๏ธ
ENJOY LEARNING ๐๐
โค5
Guys, Big Announcement!
Weโve officially hit 2 MILLION followers โ and itโs time to take our Python journey to the next level!
Iโm super excited to launch the 30-Day Python Coding Challenge โ perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python โ bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Hereโs what youโll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile script)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic โ Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs โ Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract titles from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer script)
- Final Project (Choose, build, and polish your app!)
React with โค๏ธ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
Weโve officially hit 2 MILLION followers โ and itโs time to take our Python journey to the next level!
Iโm super excited to launch the 30-Day Python Coding Challenge โ perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python โ bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Hereโs what youโll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile script)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic โ Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs โ Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract titles from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer script)
- Final Project (Choose, build, and polish your app!)
React with โค๏ธ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
โค12
Website Development Roadmap โ 2025
๐น Stage 1: HTML โ Learn the basics of web page structure.
๐น Stage 2: CSS โ Style and enhance web pages (Flexbox, Grid, Animations).
๐น Stage 3: JavaScript (ES6+) โ Add interactivity and dynamic features.
๐น Stage 4: Git & GitHub โ Manage code versions and collaborate.
๐น Stage 5: Responsive Design โ Make websites mobile-friendly (Media Queries, Bootstrap, Tailwind CSS).
๐น Stage 6: UI/UX Basics โ Understand user experience and design principles.
๐น Stage 7: JavaScript Frameworks โ Learn React.js, Vue.js, or Angular for interactive UIs.
๐น Stage 8: Backend Development โ Use Node.js, PHP, Python, or Ruby to
build server-side logic.
๐น Stage 9: Databases โ Work with MySQL, PostgreSQL, or MongoDB for data storage.
๐น Stage 10: RESTful APIs & GraphQL โ Create APIs for data communication.
๐น Stage 11: Authentication & Security โ Implement JWT, OAuth, and HTTPS best practices.
๐น Stage 12: Full Stack Project โ Build a fully functional website with both frontend and backend.
๐น Stage 13: Testing & Debugging โ Use Jest, Cypress, or other testing tools.
๐น Stage 14: Deployment โ Host websites using Netlify, Vercel, or cloud services.
๐น Stage 15: Performance Optimization โ Improve website speed (Lazy Loading, CDN, Caching).
๐ Web Development Resources
ENJOY LEARNING ๐๐
๐น Stage 1: HTML โ Learn the basics of web page structure.
๐น Stage 2: CSS โ Style and enhance web pages (Flexbox, Grid, Animations).
๐น Stage 3: JavaScript (ES6+) โ Add interactivity and dynamic features.
๐น Stage 4: Git & GitHub โ Manage code versions and collaborate.
๐น Stage 5: Responsive Design โ Make websites mobile-friendly (Media Queries, Bootstrap, Tailwind CSS).
๐น Stage 6: UI/UX Basics โ Understand user experience and design principles.
๐น Stage 7: JavaScript Frameworks โ Learn React.js, Vue.js, or Angular for interactive UIs.
๐น Stage 8: Backend Development โ Use Node.js, PHP, Python, or Ruby to
build server-side logic.
๐น Stage 9: Databases โ Work with MySQL, PostgreSQL, or MongoDB for data storage.
๐น Stage 10: RESTful APIs & GraphQL โ Create APIs for data communication.
๐น Stage 11: Authentication & Security โ Implement JWT, OAuth, and HTTPS best practices.
๐น Stage 12: Full Stack Project โ Build a fully functional website with both frontend and backend.
๐น Stage 13: Testing & Debugging โ Use Jest, Cypress, or other testing tools.
๐น Stage 14: Deployment โ Host websites using Netlify, Vercel, or cloud services.
๐น Stage 15: Performance Optimization โ Improve website speed (Lazy Loading, CDN, Caching).
๐ Web Development Resources
ENJOY LEARNING ๐๐
โค4
Python Roadmap for 2025: Complete Guide
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
๐ Python Interview ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐
https://t.iss.one/dsabooks
๐ ๐ฃ๐ฟ๐ฒ๐บ๐ถ๐๐บ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ : https://topmate.io/coding/914624
๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.iss.one/getjobss
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
๐ Python Interview ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐
https://t.iss.one/dsabooks
๐ ๐ฃ๐ฟ๐ฒ๐บ๐ถ๐๐บ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ : https://topmate.io/coding/914624
๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.iss.one/getjobss
โค4
๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ ๐๐ ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐ โ ๐ช๐ต๐ถ๐ฐ๐ต ๐ฃ๐ฎ๐๐ต ๐ถ๐ ๐ฅ๐ถ๐ด๐ต๐ ๐ณ๐ผ๐ฟ ๐ฌ๐ผ๐? ๐ค
In todayโs data-driven world, career clarity can make all the difference. Whether youโre starting out in analytics, pivoting into data science, or aligning business with data as an analyst โ understanding the core responsibilities, skills, and tools of each role is crucial.
๐ Hereโs a quick breakdown from a visual I often refer to when mentoring professionals:
๐น ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐
๓ ฏโข๓ Focus: Analyzing historical data to inform decisions.
๓ ฏโข๓ Skills: SQL, basic stats, data visualization, reporting.
๓ ฏโข๓ Tools: Excel, Tableau, Power BI, SQL.
๐น ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐
๓ ฏโข๓ Focus: Predictive modeling, ML, complex data analysis.
๓ ฏโข๓ Skills: Programming, ML, deep learning, stats.
๓ ฏโข๓ Tools: Python, R, TensorFlow, Scikit-Learn, Spark.
๐น ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐
๓ ฏโข๓ Focus: Bridging business needs with data insights.
๓ ฏโข๓ Skills: Communication, stakeholder management, process modeling.
๓ ฏโข๓ Tools: Microsoft Office, BI tools, business process frameworks.
๐ ๐ ๐ ๐๐ฑ๐๐ถ๐ฐ๐ฒ:
Start with what interests you the most and aligns with your current strengths. Are you business-savvy? Start as a Business Analyst. Love solving puzzles with data?
Explore Data Analyst. Want to build models and uncover deep insights? Head into Data Science.
๐ ๐ง๐ฎ๐ธ๐ฒ ๐๐ถ๐บ๐ฒ ๐๐ผ ๐๐ฒ๐น๐ณ-๐ฎ๐๐๐ฒ๐๐ ๐ฎ๐ป๐ฑ ๐ฐ๐ต๐ผ๐ผ๐๐ฒ ๐ฎ ๐ฝ๐ฎ๐๐ต ๐๐ต๐ฎ๐ ๐ฒ๐ป๐ฒ๐ฟ๐ด๐ถ๐๐ฒ๐ ๐๐ผ๐, not just one thatโs trending.
In todayโs data-driven world, career clarity can make all the difference. Whether youโre starting out in analytics, pivoting into data science, or aligning business with data as an analyst โ understanding the core responsibilities, skills, and tools of each role is crucial.
๐ Hereโs a quick breakdown from a visual I often refer to when mentoring professionals:
๐น ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐
๓ ฏโข๓ Focus: Analyzing historical data to inform decisions.
๓ ฏโข๓ Skills: SQL, basic stats, data visualization, reporting.
๓ ฏโข๓ Tools: Excel, Tableau, Power BI, SQL.
๐น ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐
๓ ฏโข๓ Focus: Predictive modeling, ML, complex data analysis.
๓ ฏโข๓ Skills: Programming, ML, deep learning, stats.
๓ ฏโข๓ Tools: Python, R, TensorFlow, Scikit-Learn, Spark.
๐น ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐
๓ ฏโข๓ Focus: Bridging business needs with data insights.
๓ ฏโข๓ Skills: Communication, stakeholder management, process modeling.
๓ ฏโข๓ Tools: Microsoft Office, BI tools, business process frameworks.
๐ ๐ ๐ ๐๐ฑ๐๐ถ๐ฐ๐ฒ:
Start with what interests you the most and aligns with your current strengths. Are you business-savvy? Start as a Business Analyst. Love solving puzzles with data?
Explore Data Analyst. Want to build models and uncover deep insights? Head into Data Science.
๐ ๐ง๐ฎ๐ธ๐ฒ ๐๐ถ๐บ๐ฒ ๐๐ผ ๐๐ฒ๐น๐ณ-๐ฎ๐๐๐ฒ๐๐ ๐ฎ๐ป๐ฑ ๐ฐ๐ต๐ผ๐ผ๐๐ฒ ๐ฎ ๐ฝ๐ฎ๐๐ต ๐๐ต๐ฎ๐ ๐ฒ๐ป๐ฒ๐ฟ๐ด๐ถ๐๐ฒ๐ ๐๐ผ๐, not just one thatโs trending.
โค5
Guys, We Did It!
We just crossed 1 Lakh followers on WhatsApp โ and Iโm dropping something massive for you all!
Iโm launching a Data Science Learning Series โ where I will cover essential Data Science & Machine Learning concepts from basic to advanced level covering real-world projects with step-by-step explanations, hands-on examples, and quizzes to test your skills after every major topic.
Hereโs what weโll cover in the coming days:
Week 1: Data Science Foundations
- What is Data Science?
- Where is DS used in real life?
- Data Analyst vs Data Scientist vs ML Engineer
- Tools used in DS (with icons & examples)
- DS Life Cycle (Step-by-step)
- Mini Quiz: Week 1 Topics
Week 2: Python for Data Science (Basics Only)
- Variables, Data Types, Lists, Dicts (with real-world data)
- Loops & Conditional Statements
- Functions (only basics)
- Importing CSV, Viewing Data
- Intro to Pandas DataFrame
- Mini Quiz: Python Topics
Week 3: Data Cleaning & Preparation
- Handling Missing Data
- Duplicates, Outliers (conceptual + pandas code)
- Data Type Conversions
- Renaming Columns, Reindexing
- Combining Datasets
- Mini Quiz: Choose the right method (dropna vs fillna, etc.)
Week 4: Data Exploration & Visualization
- Descriptive Stats (mean, median, std)
- GroupBy, Value_counts
- Visualizing with Pandas (plot, bar, hist)
- Matplotlib & Seaborn (basic use only)
- Correlation & Heatmaps
- Mini Quiz: Match chart type with goal
Week 5: Feature Engineering + Intro to ML
What is Feature Engineering?
Encoding (Label, One-Hot), Scaling
Train-Test Split, ML Pipeline
Supervised vs Unsupervised
Linear Regression: Concept Only
Mini Quiz: Regression or Classification?
Week 6: Model Building & Evaluation
- Train a Linear Regression Model
- Logistic Regression (basic example)
- Model Evaluation (Accuracy, Precision, Recall)
- Confusion Matrix (explanation)
- Overfitting & Underfitting (concepts)
- Mini Quiz: Model Evaluation Scenarios
Week 7: Real-World Projects
- Project 1: Predict House Prices
- Project 2: Classify Emails as Spam
- Project 3: Explore Titanic Dataset
- How to structure your project
- What to upload on GitHub
- Mini Quiz: Whatโs missing in this project?
Week 8: Career Boost Week
- Resume Tips for DS Roles
- Portfolio Tips (GitHub/Notion/PDF)
- Best Platforms to Apply (Internship + Job)
- 15 Most Common DS Interview Qs
- Mock Interview Questions for Practice
- Final Recap Quiz
React with โค๏ธ if you're ready for this new journey
Join our WhatsApp channel now: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998
We just crossed 1 Lakh followers on WhatsApp โ and Iโm dropping something massive for you all!
Iโm launching a Data Science Learning Series โ where I will cover essential Data Science & Machine Learning concepts from basic to advanced level covering real-world projects with step-by-step explanations, hands-on examples, and quizzes to test your skills after every major topic.
Hereโs what weโll cover in the coming days:
Week 1: Data Science Foundations
- What is Data Science?
- Where is DS used in real life?
- Data Analyst vs Data Scientist vs ML Engineer
- Tools used in DS (with icons & examples)
- DS Life Cycle (Step-by-step)
- Mini Quiz: Week 1 Topics
Week 2: Python for Data Science (Basics Only)
- Variables, Data Types, Lists, Dicts (with real-world data)
- Loops & Conditional Statements
- Functions (only basics)
- Importing CSV, Viewing Data
- Intro to Pandas DataFrame
- Mini Quiz: Python Topics
Week 3: Data Cleaning & Preparation
- Handling Missing Data
- Duplicates, Outliers (conceptual + pandas code)
- Data Type Conversions
- Renaming Columns, Reindexing
- Combining Datasets
- Mini Quiz: Choose the right method (dropna vs fillna, etc.)
Week 4: Data Exploration & Visualization
- Descriptive Stats (mean, median, std)
- GroupBy, Value_counts
- Visualizing with Pandas (plot, bar, hist)
- Matplotlib & Seaborn (basic use only)
- Correlation & Heatmaps
- Mini Quiz: Match chart type with goal
Week 5: Feature Engineering + Intro to ML
What is Feature Engineering?
Encoding (Label, One-Hot), Scaling
Train-Test Split, ML Pipeline
Supervised vs Unsupervised
Linear Regression: Concept Only
Mini Quiz: Regression or Classification?
Week 6: Model Building & Evaluation
- Train a Linear Regression Model
- Logistic Regression (basic example)
- Model Evaluation (Accuracy, Precision, Recall)
- Confusion Matrix (explanation)
- Overfitting & Underfitting (concepts)
- Mini Quiz: Model Evaluation Scenarios
Week 7: Real-World Projects
- Project 1: Predict House Prices
- Project 2: Classify Emails as Spam
- Project 3: Explore Titanic Dataset
- How to structure your project
- What to upload on GitHub
- Mini Quiz: Whatโs missing in this project?
Week 8: Career Boost Week
- Resume Tips for DS Roles
- Portfolio Tips (GitHub/Notion/PDF)
- Best Platforms to Apply (Internship + Job)
- 15 Most Common DS Interview Qs
- Mock Interview Questions for Practice
- Final Recap Quiz
React with โค๏ธ if you're ready for this new journey
Join our WhatsApp channel now: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998
โค8๐1
Call for papers on AI to AI Journey* conference journal has started!
Prize for the best scientific paper - 1 million roubles!
Selected papers will be published in the scientific journal Doklady Mathematics.
๐ The journal:
โข Indexed in the largest bibliographic databases of scientific citations
โข Accessible to an international audience and published in the worldโs digital libraries
Submit your article by August 20 and get the opportunity not only to publish your research the scientific journal, but also to present it at the AI Journey conference.
Prize for the best article - 1 million roubles!
More detailed information can be found in the Selection Rules -> AI Journey
*AI Journey - a major online conference in the field of AI technologies
Prize for the best scientific paper - 1 million roubles!
Selected papers will be published in the scientific journal Doklady Mathematics.
๐ The journal:
โข Indexed in the largest bibliographic databases of scientific citations
โข Accessible to an international audience and published in the worldโs digital libraries
Submit your article by August 20 and get the opportunity not only to publish your research the scientific journal, but also to present it at the AI Journey conference.
Prize for the best article - 1 million roubles!
More detailed information can be found in the Selection Rules -> AI Journey
*AI Journey - a major online conference in the field of AI technologies
โค5
Most Important Mathematical Equations in Data Science!
1๏ธโฃ Gradient Descent: Optimization algorithm minimizing the cost function.
2๏ธโฃ Normal Distribution: Distribution characterized by mean ฮผ\muฮผ and variance ฯ2\sigma^2ฯ2.
3๏ธโฃ Sigmoid Function: Activation function mapping real values to 0-1 range.
4๏ธโฃ Linear Regression: Predictive model of linear input-output relationships.
5๏ธโฃ Cosine Similarity: Metric for vector similarity based on angle cosine.
6๏ธโฃ Naive Bayes: Classifier using Bayesโ Theorem and feature independence.
7๏ธโฃ K-Means: Clustering minimizing distances to cluster centroids.
8๏ธโฃ Log Loss: Performance measure for probability output models.
9๏ธโฃ Mean Squared Error (MSE): Average of squared prediction errors.
๐ MSE (Bias-Variance Decomposition): Explains MSE through bias and variance.
1๏ธโฃ1๏ธโฃ MSE + L2 Regularization: Adds penalty to prevent overfitting.
1๏ธโฃ2๏ธโฃ Entropy: Uncertainty measure used in decision trees.
1๏ธโฃ3๏ธโฃ Softmax: Converts logits to probabilities for classification.
1๏ธโฃ4๏ธโฃ Ordinary Least Squares (OLS): Estimates regression parameters by minimizing residuals.
1๏ธโฃ5๏ธโฃ Correlation: Measures linear relationships between variables.
1๏ธโฃ6๏ธโฃ Z-score: Standardizes value based on standard deviations from mean.
1๏ธโฃ7๏ธโฃ Maximum Likelihood Estimation (MLE): Estimates parameters maximizing data likelihood.
1๏ธโฃ8๏ธโฃ Eigenvectors and Eigenvalues: Characterize linear transformations in matrices.
1๏ธโฃ9๏ธโฃ R-squared (Rยฒ): Proportion of variance explained by regression.
2๏ธโฃ0๏ธโฃ F1 Score: Harmonic mean of precision and recall.
2๏ธโฃ1๏ธโฃ Expected Value: Weighted average of all possible values.
Like if you need similar content ๐๐
1๏ธโฃ Gradient Descent: Optimization algorithm minimizing the cost function.
2๏ธโฃ Normal Distribution: Distribution characterized by mean ฮผ\muฮผ and variance ฯ2\sigma^2ฯ2.
3๏ธโฃ Sigmoid Function: Activation function mapping real values to 0-1 range.
4๏ธโฃ Linear Regression: Predictive model of linear input-output relationships.
5๏ธโฃ Cosine Similarity: Metric for vector similarity based on angle cosine.
6๏ธโฃ Naive Bayes: Classifier using Bayesโ Theorem and feature independence.
7๏ธโฃ K-Means: Clustering minimizing distances to cluster centroids.
8๏ธโฃ Log Loss: Performance measure for probability output models.
9๏ธโฃ Mean Squared Error (MSE): Average of squared prediction errors.
๐ MSE (Bias-Variance Decomposition): Explains MSE through bias and variance.
1๏ธโฃ1๏ธโฃ MSE + L2 Regularization: Adds penalty to prevent overfitting.
1๏ธโฃ2๏ธโฃ Entropy: Uncertainty measure used in decision trees.
1๏ธโฃ3๏ธโฃ Softmax: Converts logits to probabilities for classification.
1๏ธโฃ4๏ธโฃ Ordinary Least Squares (OLS): Estimates regression parameters by minimizing residuals.
1๏ธโฃ5๏ธโฃ Correlation: Measures linear relationships between variables.
1๏ธโฃ6๏ธโฃ Z-score: Standardizes value based on standard deviations from mean.
1๏ธโฃ7๏ธโฃ Maximum Likelihood Estimation (MLE): Estimates parameters maximizing data likelihood.
1๏ธโฃ8๏ธโฃ Eigenvectors and Eigenvalues: Characterize linear transformations in matrices.
1๏ธโฃ9๏ธโฃ R-squared (Rยฒ): Proportion of variance explained by regression.
2๏ธโฃ0๏ธโฃ F1 Score: Harmonic mean of precision and recall.
2๏ธโฃ1๏ธโฃ Expected Value: Weighted average of all possible values.
Like if you need similar content ๐๐
โค4๐2
Python Interview Questions:
Ready to test your Python skills? Letโs get started! ๐ป
1. How to check if a string is a palindrome?
2. How to find the factorial of a number using recursion?
3. How to merge two dictionaries in Python?
4. How to find the intersection of two lists?
5. How to generate a list of even numbers from 1 to 100?
6. How to find the longest word in a sentence?
7. How to count the frequency of elements in a list?
8. How to remove duplicates from a list while maintaining the order?
9. How to reverse a linked list in Python?
10. How to implement a simple binary search algorithm?
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1. How to check if a string is a palindrome?
def is_palindrome(s):
return s == s[::-1]
print(is_palindrome("madam")) # True
print(is_palindrome("hello")) # False
2. How to find the factorial of a number using recursion?
def factorial(n):
if n == 0 or n == 1:
return 1
return n * factorial(n - 1)
print(factorial(5)) # 120
3. How to merge two dictionaries in Python?
dict1 = {'a': 1, 'b': 2}
dict2 = {'c': 3, 'd': 4}
# Method 1 (Python 3.5+)
merged_dict = {**dict1, **dict2}
# Method 2 (Python 3.9+)
merged_dict = dict1 | dict2
print(merged_dict)4. How to find the intersection of two lists?
list1 = [1, 2, 3, 4]
list2 = [3, 4, 5, 6]
intersection = list(set(list1) & set(list2))
print(intersection) # [3, 4]
5. How to generate a list of even numbers from 1 to 100?
even_numbers = [i for i in range(1, 101) if i % 2 == 0]
print(even_numbers)
6. How to find the longest word in a sentence?
def longest_word(sentence):
words = sentence.split()
return max(words, key=len)
print(longest_word("Python is a powerful language")) # "powerful"
7. How to count the frequency of elements in a list?
from collections import Counter
my_list = [1, 2, 2, 3, 3, 3, 4]
frequency = Counter(my_list)
print(frequency) # Counter({3: 3, 2: 2, 1: 1, 4: 1})
8. How to remove duplicates from a list while maintaining the order?
def remove_duplicates(lst):
return list(dict.fromkeys(lst))
my_list = [1, 2, 2, 3, 4, 4, 5]
print(remove_duplicates(my_list)) # [1, 2, 3, 4, 5]
9. How to reverse a linked list in Python?
class Node:
def __init__(self, data):
self.data = data
self.next = None
def reverse_linked_list(head):
prev = None
current = head
while current:
next_node = current.next
current.next = prev
prev = current
current = next_node
return prev
# Create linked list: 1 -> 2 -> 3
head = Node(1)
head.next = Node(2)
head.next.next = Node(3)
# Reverse and print the list
reversed_head = reverse_linked_list(head)
while reversed_head:
print(reversed_head.data, end=" -> ")
reversed_head = reversed_head.next
10. How to implement a simple binary search algorithm?
def binary_search(arr, target):
low, high = 0, len(arr) - 1
while low <= high:
mid = (low + high) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
low = mid + 1
else:
high = mid - 1
return -1
print(binary_search([1, 2, 3, 4, 5, 6, 7], 4)) # 3
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Data Science Learning Plan
Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)
Step 2: Python for Data Science (Basics and Libraries)
Step 3: Data Manipulation and Analysis (Pandas, NumPy)
Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)
Step 5: Databases and SQL for Data Retrieval
Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)
Step 7: Data Cleaning and Preprocessing
Step 8: Feature Engineering and Selection
Step 9: Model Evaluation and Tuning
Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)
Step 11: Working with Big Data (Hadoop, Spark)
Step 12: Building Data Science Projects and Portfolio
Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)
Step 2: Python for Data Science (Basics and Libraries)
Step 3: Data Manipulation and Analysis (Pandas, NumPy)
Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)
Step 5: Databases and SQL for Data Retrieval
Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)
Step 7: Data Cleaning and Preprocessing
Step 8: Feature Engineering and Selection
Step 9: Model Evaluation and Tuning
Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)
Step 11: Working with Big Data (Hadoop, Spark)
Step 12: Building Data Science Projects and Portfolio
โค4
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