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What are python namespaces?

๐Ÿ‘‰A Python namespace ensures that object names in a program are unique and can be used without any conflict. Python implements these namespaces as dictionaries with โ€˜name as keyโ€™ mapped to its respective โ€˜object as valueโ€™.

Letโ€™s explore some examples of namespaces:

๐Ÿ‘‰Local Namespace consists of local names inside a function. It is temporarily created for a function call and gets cleared once the function returns.

๐Ÿ‘‰Global Namespace consists of names from various imported modules/packages that are being used in the ongoing project. It is created once the package is imported into the script and survives till the execution of the script.

๐Ÿ‘‰Built-in Namespace consists of built-in functions of core Python and dedicated built-in names for various types of exceptions.
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What are the common built-in data types in Python?

Python supports the below-mentioned built-in data types:

Immutable data types:

๐Ÿ‘‰Number
๐Ÿ‘‰String
๐Ÿ‘‰Tuple

Mutable data types:

๐Ÿ‘‰List
๐Ÿ‘‰Dictionary
๐Ÿ‘‰set
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Python For Data Science Cheat Sheet
Python Basics


๐Ÿ“Œ cheatsheet
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9 things to do when youโ€™re stuck in coding:

๐Ÿ” Read the error message carefully โ€” it often tells you the issue

โœ๏ธ Rubber duck debugging โ€” explain your code out loud

๐Ÿงฉ Break the problem into smaller parts

๐Ÿง  Revisit the logic โ€” not just the syntax

โ“ Google the error or issue with specific keywords

๐Ÿ› ๏ธ Use console logs or print statements to trace the flow

โธ๏ธ Take a short break โ€” come back with a fresh mind

๐Ÿ‘ฅ Ask for help โ€” forums, friends, or mentors

๐Ÿ“– 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.

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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, youโ€™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 youโ€™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

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Here's the Aโ€“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
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Machine Learning Algorithms every data scientist should know:

๐Ÿ“Œ Supervised Learning:

๐Ÿ”น Regression
โˆŸ Linear Regression
โˆŸ Ridge & Lasso Regression
โˆŸ Polynomial Regression

๐Ÿ”น Classification
โˆŸ Logistic Regression
โˆŸ K-Nearest Neighbors (KNN)
โˆŸ Decision Tree
โˆŸ Random Forest
โˆŸ Support Vector Machine (SVM)
โˆŸ Naive Bayes
โˆŸ Gradient Boosting (XGBoost, LightGBM, CatBoost)


๐Ÿ“Œ Unsupervised Learning:

๐Ÿ”น Clustering
โˆŸ K-Means
โˆŸ Hierarchical Clustering
โˆŸ DBSCAN

๐Ÿ”น Dimensionality Reduction
โˆŸ PCA (Principal Component Analysis)
โˆŸ t-SNE
โˆŸ LDA (Linear Discriminant Analysis)


๐Ÿ“Œ Reinforcement Learning (Basics):
โˆŸ Q-Learning
โˆŸ Deep Q Network (DQN)


๐Ÿ“Œ Ensemble Techniques:
โˆŸ Bagging (Random Forest)
โˆŸ Boosting (XGBoost, AdaBoost, Gradient Boosting)
โˆŸ Stacking

Donโ€™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 โค๏ธ for more free resources
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30 Days Python Roadmap for Data Analysts ๐Ÿ‘†
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