What are the common built-in data types in Python?
Python supports the below-mentioned built-in data types:
Immutable data types:
👉Number
👉String
👉Tuple
Mutable data types:
👉List
👉Dictionary
👉set
Python supports the below-mentioned built-in data types:
Immutable data types:
👉Number
👉String
👉Tuple
Mutable data types:
👉List
👉Dictionary
👉set
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9 things to do when you’re stuck in coding:
🔍 Read the error message carefully — it often tells you the issue
✍️ Rubber duck debugging — explain your code out loud
🧩 Break the problem into smaller parts
🧠 Revisit the logic — not just the syntax
❓ Google the error or issue with specific keywords
🛠️ Use console logs or print statements to trace the flow
⏸️ Take a short break — come back with a fresh mind
👥 Ask for help — forums, friends, or mentors
📖 Check the official documentation or trusted sources
#coding #tips
🔍 Read the error message carefully — it often tells you the issue
✍️ Rubber duck debugging — explain your code out loud
🧩 Break the problem into smaller parts
🧠 Revisit the logic — not just the syntax
❓ Google the error or issue with specific keywords
🛠️ Use console logs or print statements to trace the flow
⏸️ Take a short break — come back with a fresh mind
👥 Ask for help — forums, friends, or mentors
📖 Check the official documentation or trusted sources
#coding #tips
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Essential Python Libraries for Data Science
- Numpy: Fundamental for numerical operations, handling arrays, and mathematical functions.
- SciPy: Complements Numpy with additional functionalities for scientific computing, including optimization and signal processing.
- Pandas: Essential for data manipulation and analysis, offering powerful data structures like DataFrames.
- Matplotlib: A versatile plotting library for creating static, interactive, and animated visualizations.
- Keras: A high-level neural networks API, facilitating rapid prototyping and experimentation in deep learning.
- TensorFlow: An open-source machine learning framework widely used for building and training deep learning models.
- Scikit-learn: Provides simple and efficient tools for data mining, machine learning, and statistical modeling.
- Seaborn: Built on Matplotlib, Seaborn enhances data visualization with a high-level interface for drawing attractive and informative statistical graphics.
- Statsmodels: Focuses on estimating and testing statistical models, providing tools for exploring data, estimating models, and statistical testing.
- NLTK (Natural Language Toolkit): A library for working with human language data, supporting tasks like classification, tokenization, stemming, tagging, parsing, and more.
These libraries collectively empower data scientists to handle various tasks, from data preprocessing to advanced machine learning implementations.
ENJOY LEARNING 👍👍
- Numpy: Fundamental for numerical operations, handling arrays, and mathematical functions.
- SciPy: Complements Numpy with additional functionalities for scientific computing, including optimization and signal processing.
- Pandas: Essential for data manipulation and analysis, offering powerful data structures like DataFrames.
- Matplotlib: A versatile plotting library for creating static, interactive, and animated visualizations.
- Keras: A high-level neural networks API, facilitating rapid prototyping and experimentation in deep learning.
- TensorFlow: An open-source machine learning framework widely used for building and training deep learning models.
- Scikit-learn: Provides simple and efficient tools for data mining, machine learning, and statistical modeling.
- Seaborn: Built on Matplotlib, Seaborn enhances data visualization with a high-level interface for drawing attractive and informative statistical graphics.
- Statsmodels: Focuses on estimating and testing statistical models, providing tools for exploring data, estimating models, and statistical testing.
- NLTK (Natural Language Toolkit): A library for working with human language data, supporting tasks like classification, tokenization, stemming, tagging, parsing, and more.
These libraries collectively empower data scientists to handle various tasks, from data preprocessing to advanced machine learning implementations.
ENJOY LEARNING 👍👍
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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, you’re 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 you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
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.
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
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, you’re 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 you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
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.
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
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Here's the A–Z list of essential Python programming concepts
A - Arguments
B - Built-in Functions
C - Comprehensions
D - Dictionaries
E - Exceptions
F - Functions
G - Generators
H - Higher-Order Functions
I - Iterators
J - Join Method
K - Keyword Arguments
L - Lambda Functions
M - Modules
N - NoneType
O - Object-Oriented Programming
P - PEP8
Q - Queue
R - Range Function
S - Sets
T - Tuples
U - Unpacking
V - Variables
W - While Loop
X - XOR Operation
Y - Yield Keyword
Z - Zip Function
These concepts are foundational to mastering Python and writing clean, efficient, and Pythonic code.
Credits: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
A - Arguments
B - Built-in Functions
C - Comprehensions
D - Dictionaries
E - Exceptions
F - Functions
G - Generators
H - Higher-Order Functions
I - Iterators
J - Join Method
K - Keyword Arguments
L - Lambda Functions
M - Modules
N - NoneType
O - Object-Oriented Programming
P - PEP8
Q - Queue
R - Range Function
S - Sets
T - Tuples
U - Unpacking
V - Variables
W - While Loop
X - XOR Operation
Y - Yield Keyword
Z - Zip Function
These concepts are foundational to mastering Python and writing clean, efficient, and Pythonic code.
Credits: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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Machine Learning Algorithms every data scientist should know:
📌 Supervised Learning:
🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression
🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)
📌 Unsupervised Learning:
🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN
🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)
📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)
📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking
Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
📌 Supervised Learning:
🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression
🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)
📌 Unsupervised Learning:
🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN
🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)
📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)
📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking
Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
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The best doesn't come from working more.
It comes from working smarter.
The most common mistakes people make,
With practical tips to avoid each:
1) Working late every night.
• Prioritize quality time with loved ones.
Understand that long hours won't be remembered as fondly as time spent with family and friends.
2) Believing more hours mean more productivity.
• Focus on efficiency.
Complete tasks in less time to free up hours for personal activities and rest.
3) Ignoring the need for breaks.
• Take regular breaks to rejuvenate your mind.
Creativity and productivity suffer without proper rest.
4) Sacrificing personal well-being.
• Maintain a healthy work-life balance.
Ensure you don't compromise your health or relationships for work.
5) Feeling pressured to constantly produce.
• Quality over quantity.
6) Neglecting hobbies and interests.
• Engage in activities you love outside of work.
This helps to keep your mind fresh and inspired.
7) Failing to set boundaries.
• Set clear work hours and stick to them.
This helps to prevent overworking and ensures you have time for yourself.
8) Not delegating tasks.
• Delegate when possible.
Sharing the workload can enhance productivity and give you more free time.
9) Overlooking the importance of sleep.
• Prioritize sleep for better performance.
A well-rested mind is more creative and effective.
10) Underestimating the impact of overworking.
• Recognize the long-term effects.
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://news.1rj.ru/str/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍 👍
It comes from working smarter.
The most common mistakes people make,
With practical tips to avoid each:
1) Working late every night.
• Prioritize quality time with loved ones.
Understand that long hours won't be remembered as fondly as time spent with family and friends.
2) Believing more hours mean more productivity.
• Focus on efficiency.
Complete tasks in less time to free up hours for personal activities and rest.
3) Ignoring the need for breaks.
• Take regular breaks to rejuvenate your mind.
Creativity and productivity suffer without proper rest.
4) Sacrificing personal well-being.
• Maintain a healthy work-life balance.
Ensure you don't compromise your health or relationships for work.
5) Feeling pressured to constantly produce.
• Quality over quantity.
6) Neglecting hobbies and interests.
• Engage in activities you love outside of work.
This helps to keep your mind fresh and inspired.
7) Failing to set boundaries.
• Set clear work hours and stick to them.
This helps to prevent overworking and ensures you have time for yourself.
8) Not delegating tasks.
• Delegate when possible.
Sharing the workload can enhance productivity and give you more free time.
9) Overlooking the importance of sleep.
• Prioritize sleep for better performance.
A well-rested mind is more creative and effective.
10) Underestimating the impact of overworking.
• Recognize the long-term effects.
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://news.1rj.ru/str/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍 👍
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