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- Type: Competition/Award
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Benefits:
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๐จโ๐ปWebsites to Practice Python
Python Basics ๐:
1. http://codingbat.com/python
2. https://www.hackerrank.com/
3. https://www.hackerearth.com/practice/
Practice Problems set :
4. https://projecteuler.net/archives
5. http://www.codeabbey.com/index/task_list
6. http://www.pythonchallenge.com/
Python Basics ๐:
1. http://codingbat.com/python
2. https://www.hackerrank.com/
3. https://www.hackerearth.com/practice/
Practice Problems set :
4. https://projecteuler.net/archives
5. http://www.codeabbey.com/index/task_list
6. http://www.pythonchallenge.com/
๐๐ข๐ฉ๐ฌ ๐๐จ๐ซ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐๐จ๐๐ข๐ง๐ ๐ข๐ง ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ:
๐ ๐จ๐ฆ๐ต ๐ด๐ฐ ๐ฎ๐ข๐ฏ๐บ ๐ฒ๐ถ๐ฆ๐ด๐ต๐ช๐ฐ๐ฏ๐ด ๐ง๐ณ๐ฐ๐ฎ ๐ฅ๐ข๐ต๐ข ๐ข๐ฏ๐ข๐ญ๐บ๐ต๐ช๐ค๐ด ๐ข๐ด๐ฑ๐ช๐ณ๐ข๐ฏ๐ต๐ด ๐ข๐ฏ๐ฅ ๐ฑ๐ณ๐ฐ๐ง๐ฆ๐ด๐ด๐ช๐ฐ๐ฏ๐ข๐ญ๐ด ๐ฐ๐ฏ ๐ฉ๐ฐ๐ธ ๐ต๐ฐ ๐จ๐ข๐ช๐ฏ ๐ค๐ฐ๐ฎ๐ฎ๐ข๐ฏ๐ฅ ๐ฐ๐ง ๐๐บ๐ต๐ฉ๐ฐ๐ฏ.
๐๐๐๐๐ซ๐ง ๐๐จ๐ซ๐ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ: Master Python libraries for data analytics, like
-pandas for dataframes,
-NumPy for numerical operations,
-Matplotlib/Seaborn for plotting,
-scikit-learn for machine learning.
๐๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐ ๐๐จ๐ง๐๐๐ฉ๐ญ๐ฌ: Important concepts like list comprehensions, lambda functions, object-oriented programming, and error handling to write efficient code.
๐๐๐ฌ๐ ๐๐ซ๐จ๐๐ฅ๐๐ฆ-๐๐จ๐ฅ๐ฏ๐ข๐ง๐ ๐๐๐ญ๐ก๐จ๐๐ฌ: Apply data wrangling techniques, efficient loops, and vectorized operations in NumPy/pandas for optimized performance.
๐๐๐จ ๐๐จ๐๐ค ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Work on end-to-end Python analytics projectsโdata loading, cleaning, analysis, and visualization.
๐๐๐๐๐ซ๐ง ๐๐ซ๐จ๐ฆ ๐๐๐ฌ๐ญ ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Review your previous Python projects to see where your code can be more efficient.
Make sure to scroll through the above messages ๐ you will definitely find more interesting things ๐ค
Websites to Practice Python
https://t.me/pythonresourcestp/76
10 Ways to Speed Up Your Python Code
https://t.me/pythonresourcestp/73
Libraries for Data Science in Python
https://t.me/pythonresourcestp/66
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
WhatsApp Channel
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
๐ ๐จ๐ฆ๐ต ๐ด๐ฐ ๐ฎ๐ข๐ฏ๐บ ๐ฒ๐ถ๐ฆ๐ด๐ต๐ช๐ฐ๐ฏ๐ด ๐ง๐ณ๐ฐ๐ฎ ๐ฅ๐ข๐ต๐ข ๐ข๐ฏ๐ข๐ญ๐บ๐ต๐ช๐ค๐ด ๐ข๐ด๐ฑ๐ช๐ณ๐ข๐ฏ๐ต๐ด ๐ข๐ฏ๐ฅ ๐ฑ๐ณ๐ฐ๐ง๐ฆ๐ด๐ด๐ช๐ฐ๐ฏ๐ข๐ญ๐ด ๐ฐ๐ฏ ๐ฉ๐ฐ๐ธ ๐ต๐ฐ ๐จ๐ข๐ช๐ฏ ๐ค๐ฐ๐ฎ๐ฎ๐ข๐ฏ๐ฅ ๐ฐ๐ง ๐๐บ๐ต๐ฉ๐ฐ๐ฏ.
๐๐๐๐๐ซ๐ง ๐๐จ๐ซ๐ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ: Master Python libraries for data analytics, like
-pandas for dataframes,
-NumPy for numerical operations,
-Matplotlib/Seaborn for plotting,
-scikit-learn for machine learning.
๐๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐ ๐๐จ๐ง๐๐๐ฉ๐ญ๐ฌ: Important concepts like list comprehensions, lambda functions, object-oriented programming, and error handling to write efficient code.
๐๐๐ฌ๐ ๐๐ซ๐จ๐๐ฅ๐๐ฆ-๐๐จ๐ฅ๐ฏ๐ข๐ง๐ ๐๐๐ญ๐ก๐จ๐๐ฌ: Apply data wrangling techniques, efficient loops, and vectorized operations in NumPy/pandas for optimized performance.
๐๐๐จ ๐๐จ๐๐ค ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Work on end-to-end Python analytics projectsโdata loading, cleaning, analysis, and visualization.
๐๐๐๐๐ซ๐ง ๐๐ซ๐จ๐ฆ ๐๐๐ฌ๐ญ ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Review your previous Python projects to see where your code can be more efficient.
Make sure to scroll through the above messages ๐ you will definitely find more interesting things ๐ค
Websites to Practice Python
https://t.me/pythonresourcestp/76
10 Ways to Speed Up Your Python Code
https://t.me/pythonresourcestp/73
Libraries for Data Science in Python
https://t.me/pythonresourcestp/66
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
WhatsApp Channel
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
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* SaaS Support
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Python Code to remove Image Background
โโโโโโโโโโโโโโโโโโโโโ-
from rembg import remove
from PIL import Image
image_path = 'Image Name' ## ---> Change to Image name
output_image = 'ImageNew' ## ---> Change to new name your image
input = Image.open(image_path)
output = remove(input)
output.save(output_image)
#How to Speed Up Python Code๐
#https://t.me/pythonresourcestp/73
โโโโโโโโโโโโโโโโโโโโโ-
from rembg import remove
from PIL import Image
image_path = 'Image Name' ## ---> Change to Image name
output_image = 'ImageNew' ## ---> Change to new name your image
input = Image.open(image_path)
output = remove(input)
output.save(output_image)
#How to Speed Up Python Code๐
#https://t.me/pythonresourcestp/73
๐2
Essential Python topics for data analysts ๐๐
Python Topics:
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Python for Machine Learning: https://t.me/pythonresourcestp/48
Best Programming Resources: https://topmate.io/learning_resources/1362011
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
WhatsApp Channel
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Python Topics:
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Python for Machine Learning: https://t.me/pythonresourcestp/48
Best Programming Resources: https://topmate.io/learning_resources/1362011
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
WhatsApp Channel
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Python from scratch by University of Waterloo
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
Link: https://open.cs.uwaterloo.ca/python-from-scratch/
Websites to Practice Python
https://t.me/pythonresourcestp/76
10 Ways to Speed Up Your Python Code
https://t.me/pythonresourcestp/73
Libraries for Data Science in Python
https://t.me/pythonresourcestp/66
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
WhatsApp Channel
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
Link: https://open.cs.uwaterloo.ca/python-from-scratch/
Websites to Practice Python
https://t.me/pythonresourcestp/76
10 Ways to Speed Up Your Python Code
https://t.me/pythonresourcestp/73
Libraries for Data Science in Python
https://t.me/pythonresourcestp/66
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
WhatsApp Channel
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
๐1
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Python for Data Analysis Free Resources:
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Practice: https://www.kaggle.com/learn/python
Free Course: https://www.freecodecamp.org/learn/data-analysis-with-python/
Practice: https://www.kaggle.com/learn/python
Complete Roadmap to Learn Python Programming in 2025
Beginner Level
1. Basics of Python
- Understanding syntax and basic concepts
- Variables and data types
- Basic operators and expressions
- Input and output functions
- Conditional statements (if, elif, else)
- Loops (for, while)
2. Data Structures
- Lists
- Tuples
- Sets
- Dictionaries
3. Functions and Modules
- Defining and calling functions
- Arguments and return values
- Lambda functions
- Built-in modules and importing external modules
Intermediate Level
4. File Handling
- Reading from and writing to files
- Working with CSV, JSON, and other file formats
5. Object-Oriented Programming (OOP)
- Classes and objects
- Methods and constructors
- Inheritance and polymorphism
- Encapsulation and abstraction
6. Error Handling and Exceptions
- Try, except, finally blocks
- Raising exceptions
- Custom exceptions
7. Libraries and Frameworks
- Understanding and using popular libraries (NumPy, Pandas, Matplotlib)
- Introduction to web frameworks (Flask, Django)
Advanced Level
8. Advanced Concepts
- Decorators
- Generators
- Context managers
9. Working with Databases
- SQL and NoSQL databases
- ORM (Object-Relational Mapping) with SQLAlchemy or Django ORM
10. Web Development
- Full-stack development with Django or Flask
- RESTful APIs and backend services
11. Data Science and Machine Learning
- Data analysis with Pandas
- Data visualization with Matplotlib and Seaborn
- Machine learning with Scikit-Learn and TensorFlow
Tools and Best Practices
12. Version Control
- Using Git and GitHub for version control
- Collaboration and branching strategies
13. Testing and Debugging
- Unit testing with Unittest or PyTest
- Debugging techniques and tools
14. Development Environment
- Setting up IDEs (PyCharm, VS Code)
- Virtual environments and dependency management
15. Code Quality
- Writing clean and efficient code
- Adhering to PEP 8 standards
- Code reviews and refactoring
Best Resource to learn Python
Python Interview Questions with Answers (https://t.me/pythonresourcestp/23)
Freecodecamp Python ML Course with FREE Certificate (http://developers.google.com/machine-learning/crash-course)
Python for Data Analysis (https://t.me/pythonresourcestp/97)
Python course for beginners by Microsoft (https://learn.microsoft.com/en-us/training/paths/beginner-python/)
Scientific Computing with Python (https://www.freecodecamp.org/learn/scientific-computing-with-python/)
Python course by Google (https://developers.google.com/edu/python)
Python Free Resources (https://t.me/pythonresourcestp)
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https://t.me/techpsyche
ENJOY LEARNING ๐๐
More Resources Here
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Like this post if you need more ๐โค๏ธ
Hope it helps :)
Beginner Level
1. Basics of Python
- Understanding syntax and basic concepts
- Variables and data types
- Basic operators and expressions
- Input and output functions
- Conditional statements (if, elif, else)
- Loops (for, while)
2. Data Structures
- Lists
- Tuples
- Sets
- Dictionaries
3. Functions and Modules
- Defining and calling functions
- Arguments and return values
- Lambda functions
- Built-in modules and importing external modules
Intermediate Level
4. File Handling
- Reading from and writing to files
- Working with CSV, JSON, and other file formats
5. Object-Oriented Programming (OOP)
- Classes and objects
- Methods and constructors
- Inheritance and polymorphism
- Encapsulation and abstraction
6. Error Handling and Exceptions
- Try, except, finally blocks
- Raising exceptions
- Custom exceptions
7. Libraries and Frameworks
- Understanding and using popular libraries (NumPy, Pandas, Matplotlib)
- Introduction to web frameworks (Flask, Django)
Advanced Level
8. Advanced Concepts
- Decorators
- Generators
- Context managers
9. Working with Databases
- SQL and NoSQL databases
- ORM (Object-Relational Mapping) with SQLAlchemy or Django ORM
10. Web Development
- Full-stack development with Django or Flask
- RESTful APIs and backend services
11. Data Science and Machine Learning
- Data analysis with Pandas
- Data visualization with Matplotlib and Seaborn
- Machine learning with Scikit-Learn and TensorFlow
Tools and Best Practices
12. Version Control
- Using Git and GitHub for version control
- Collaboration and branching strategies
13. Testing and Debugging
- Unit testing with Unittest or PyTest
- Debugging techniques and tools
14. Development Environment
- Setting up IDEs (PyCharm, VS Code)
- Virtual environments and dependency management
15. Code Quality
- Writing clean and efficient code
- Adhering to PEP 8 standards
- Code reviews and refactoring
Best Resource to learn Python
Python Interview Questions with Answers (https://t.me/pythonresourcestp/23)
Freecodecamp Python ML Course with FREE Certificate (http://developers.google.com/machine-learning/crash-course)
Python for Data Analysis (https://t.me/pythonresourcestp/97)
Python course for beginners by Microsoft (https://learn.microsoft.com/en-us/training/paths/beginner-python/)
Scientific Computing with Python (https://www.freecodecamp.org/learn/scientific-computing-with-python/)
Python course by Google (https://developers.google.com/edu/python)
Python Free Resources (https://t.me/pythonresourcestp)
Join this Channel for More Resourses
https://t.me/techpsyche
ENJOY LEARNING ๐๐
More Resources Here
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Like this post if you need more ๐โค๏ธ
Hope it helps :)
๐1
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๐ฏ ๐๐ฅ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฏ๐ ๐๐ผ๐ผ๐ด๐น๐ฒ, ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ & ๐๐ถ๐ป๐ธ๐ฒ๐ฑ๐๐ป ๐๐ป
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Donโt miss this opportunity to elevate your expertise. ๐
Upskill with these amazing free courses from top platforms! ๐
1๏ธโฃ Generative AI by Google: Dive into AI fundamentals and applications.
2๏ธโฃ Training for DevOps Engineers: Master DevOps tools and practices with Microsoft.
3๏ธโฃ Career Essentials in Data Analysis: Build data analysis skills with Microsoft & LinkedIn.
๐๐ข๐ง๐ค ๐:-
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Donโt miss this opportunity to elevate your expertise. ๐