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How to get job as python fresher?
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, youโre recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once youโll learn several Python web frameworks and other trending technologies.
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, youโre recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once youโll learn several Python web frameworks and other trending technologies.
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
โค2๐1
๐๐ฒ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฟ๐ฒ๐ฎ๐บ ๐๐ผ๐ฏ ๐๐ป ๐๐บ๐ฎ๐๐ผ๐ป, ๐๐ผ๐ผ๐ด๐น๐ฒ, ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐, ๐ก๐ฉ๐๐๐๐, ๐ฎ๐ป๐ฑ ๐ ๐ฒ๐๐ฎ (๐๐ฎ๐ฐ๐ฒ๐ฏ๐ผ๐ผ๐ธ) ๐๐ถ๐๐ต ๐๐ต๐ฒ๐๐ฒ ๐ฐ๐ผ๐บ๐ฝ๐ฟ๐ฒ๐ต๐ฒ๐ป๐๐ถ๐๐ฒ ๐ฟ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ ๐๐ป
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Kenya Trends - Jobs | Opportunities | Free Resources
Sharing free learning resources, jobs & opportunities.
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BACK-END DEVELOPER REMOTE JOBS ๐๐
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Here are some Great features of Python coding:
Syntax Features:
1. Indentation-based syntax
2. Dynamic typing
3. No semicolons
4. Simple syntax for loops (e.g., for item in list:)
5. Context managers (with statement)
Language Features:
1. First-class functions
2. Lambda functions
3. Closures
4. Generators
5. Coroutines
Data Structures:
1. Lists (dynamic arrays)
2. Tuples (immutable lists)
3. Dictionaries (hash tables)
4. Sets (unordered collections)
5. NumPy arrays (for numerical computing)
Object-Oriented Programming (OOP) Features:
1. Classes
2. Objects
3. Inheritance
4. Polymorphism
5. Encapsulation
Functional Programming Features:
1. Map
2. Filter
3. Reduce
4. Lambda functions
5. Recursion
Exception Handling:
1. Try-except blocks
2. Raise statement
3. Custom exceptions
Modules and Packages:
1. Import statement
2. Modules (e.g., math, random)
3. Packages (e.g., numpy, pandas)
4. Virtual environments (e.g., venv)
Other Features:
1. Docstrings (documentation strings)
2. Type hints (optional static typing)
3. F-strings (formatted string literals)
4. Context managers (e.g., with open())
5. asyncio library (for asynchronous programming)
Silent Features:
1. Automatic memory management (garbage collection)
2. Dynamic loading of modules
3. Unicode support
4. Cross-platform compatibility
5. Extensive libraries and frameworks (e.g., NumPy, pandas, Flask)
These silent features make Python a powerful, flexible, and easy-to-use language.
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Free University Python Courses
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Syntax Features:
1. Indentation-based syntax
2. Dynamic typing
3. No semicolons
4. Simple syntax for loops (e.g., for item in list:)
5. Context managers (with statement)
Language Features:
1. First-class functions
2. Lambda functions
3. Closures
4. Generators
5. Coroutines
Data Structures:
1. Lists (dynamic arrays)
2. Tuples (immutable lists)
3. Dictionaries (hash tables)
4. Sets (unordered collections)
5. NumPy arrays (for numerical computing)
Object-Oriented Programming (OOP) Features:
1. Classes
2. Objects
3. Inheritance
4. Polymorphism
5. Encapsulation
Functional Programming Features:
1. Map
2. Filter
3. Reduce
4. Lambda functions
5. Recursion
Exception Handling:
1. Try-except blocks
2. Raise statement
3. Custom exceptions
Modules and Packages:
1. Import statement
2. Modules (e.g., math, random)
3. Packages (e.g., numpy, pandas)
4. Virtual environments (e.g., venv)
Other Features:
1. Docstrings (documentation strings)
2. Type hints (optional static typing)
3. F-strings (formatted string literals)
4. Context managers (e.g., with open())
5. asyncio library (for asynchronous programming)
Silent Features:
1. Automatic memory management (garbage collection)
2. Dynamic loading of modules
3. Unicode support
4. Cross-platform compatibility
5. Extensive libraries and frameworks (e.g., NumPy, pandas, Flask)
These silent features make Python a powerful, flexible, and easy-to-use language.
Like for more ..โค๏ธ
Free University Python Courses
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Python Resources TP
Master Python programming in 15 days with Free Resources ๐๐
Days 1-3: Introduction to Python
- Day 1: Start by installing Python on your computer.
- Day 2: Learn the basic syntax and data types in Python (variables, numbers, strings).
- Day 3: Explore Python'sโฆ
Days 1-3: Introduction to Python
- Day 1: Start by installing Python on your computer.
- Day 2: Learn the basic syntax and data types in Python (variables, numbers, strings).
- Day 3: Explore Python'sโฆ
Official Python Docs
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Tools:
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http://www.pythonforbeginners.com/basics/python-pip-usage
Practice:
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https://www.hackerrank.com
https://wiki.python.org/moin/PythonDecorators
Python GUI FAQ
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Python Projects
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WhatsApp Channel
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https://docs.python.org/3/
Tools:
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http://www.pythonforbeginners.com/basics/python-pip-usage
Practice:
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https://www.hackerrank.com
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Python Code to remove Image Background
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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
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Essential Python topics for data analysts ๐
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
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Best way to prepare for Python interviews ๐๐
1. Fundamentals: Strengthen your understanding of Python basics, including data types, control structures, functions, and object-oriented programming concepts.
2. Data Structures and Algorithms: Familiarize yourself with common data structures (lists, dictionaries, sets, etc.) and algorithms. Practice solving coding problems on platforms like LeetCode or HackerRank.
3. Problem Solving: Develop problem-solving skills by working on real-world scenarios. Understand how to approach and solve problems efficiently using Python.
4. Libraries and Frameworks: Be well-versed in popular Python libraries and frameworks relevant to the job, such as NumPy, Pandas, Flask, or Django. Demonstrate your ability to apply these tools in practical situations.
5. Web Development (if applicable): If the position involves web development, understand web frameworks like Flask or Django. Be ready to discuss your experience in building web applications using Python.
6. Database Knowledge: Have a solid understanding of working with databases in Python. Know how to interact with databases using SQLAlchemy or Django ORM.
7. Testing and Debugging: Showcase your proficiency in writing unit tests and debugging code. Understand testing frameworks like pytest and debugging tools available in Python.
8. Version Control: Familiarize yourself with version control systems, particularly Git, and demonstrate your ability to collaborate on projects using Git.
9. Projects: Showcase relevant projects in your portfolio. Discuss the challenges you faced, solutions you implemented, and the impact of your work.
10. Soft Skills: Highlight your communication and collaboration skills. Be ready to explain your thought process and decision-making during technical discussions.
Best Resource to learn Python
Python Interview Questions with Answers (https://t.me/pythonresourcestp/23)
Machine Learning Crash Course (http://developers.google.com/machine-learning/crash-course)
Python for Data Science Free Course Here: https://tinyurl.com/42nau8jx
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)
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1. Fundamentals: Strengthen your understanding of Python basics, including data types, control structures, functions, and object-oriented programming concepts.
2. Data Structures and Algorithms: Familiarize yourself with common data structures (lists, dictionaries, sets, etc.) and algorithms. Practice solving coding problems on platforms like LeetCode or HackerRank.
3. Problem Solving: Develop problem-solving skills by working on real-world scenarios. Understand how to approach and solve problems efficiently using Python.
4. Libraries and Frameworks: Be well-versed in popular Python libraries and frameworks relevant to the job, such as NumPy, Pandas, Flask, or Django. Demonstrate your ability to apply these tools in practical situations.
5. Web Development (if applicable): If the position involves web development, understand web frameworks like Flask or Django. Be ready to discuss your experience in building web applications using Python.
6. Database Knowledge: Have a solid understanding of working with databases in Python. Know how to interact with databases using SQLAlchemy or Django ORM.
7. Testing and Debugging: Showcase your proficiency in writing unit tests and debugging code. Understand testing frameworks like pytest and debugging tools available in Python.
8. Version Control: Familiarize yourself with version control systems, particularly Git, and demonstrate your ability to collaborate on projects using Git.
9. Projects: Showcase relevant projects in your portfolio. Discuss the challenges you faced, solutions you implemented, and the impact of your work.
10. Soft Skills: Highlight your communication and collaboration skills. Be ready to explain your thought process and decision-making during technical discussions.
Best Resource to learn Python
Python Interview Questions with Answers (https://t.me/pythonresourcestp/23)
Machine Learning Crash Course (http://developers.google.com/machine-learning/crash-course)
Python for Data Science Free Course Here: https://tinyurl.com/42nau8jx
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)
ENJOY LEARNING ๐๐
More Resources Here
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
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