Python Data Science Jobs & Interviews
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Your go-to hub for Python and Data Science—featuring questions, answers, quizzes, and interview tips to sharpen your skills and boost your career in the data-driven world.

Admin: @Hussein_Sheikho
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Question 26 (Intermediate - Edge Detection):
In Python's OpenCV, which of these edge detection techniques preserves edge directionality while reducing noise?

A) cv2.Laplacian()
B) cv2.Canny()
C) cv2.Sobel() with dx=1, dy=1
D) cv2.blur() + thresholding

#Python #OpenCV #EdgeDetection #ComputerVision

By: https://t.iss.one/DataScienceQ
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Question 27 (Intermediate - List Operations):
What is the time complexity of the list.insert(0, item) operation in Python, and why?

A) O(1) - Constant time (like appending)
B) O(n) - Linear time (shifts all elements)
C) O(log n) - Logarithmic time (binary search)
D) O(n²) - Quadratic time (worst-case)

#Python #DataStructures #TimeComplexity #Lists

By: https://t.iss.one/DataScienceQ
Question 30 (Intermediate - PyTorch):
What is the purpose of torch.no_grad() context manager in PyTorch?

A) Disables model training
B) Speeds up computations by disabling gradient tracking
C) Forces GPU memory cleanup
D) Enables distributed training

#Python #PyTorch #DeepLearning #NeuralNetworks

By: https://t.iss.one/DataScienceQ
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Question 31 (Intermediate - Django ORM):
When using Django ORM's select_related() and prefetch_related() for query optimization, which statement is correct?

A) select_related uses JOINs (1 SQL query) while prefetch_related uses 2+ queries
B) Both methods generate exactly one SQL query
C) prefetch_related works only with ForeignKey relationships
D) select_related is better for many-to-many relationships

#Python #Django #ORM #Database

By: https://t.iss.one/DataScienceQ
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Question 32 (Advanced - NLP & RNNs):
What is the key limitation of vanilla RNNs for NLP tasks that led to the development of LSTMs and GRUs?

A) Vanishing gradients in long sequences
B) High GPU memory usage
C) Inability to handle embeddings
D) Single-direction processing only

#Python #NLP #RNN #DeepLearning

By: https://t.iss.one/DataScienceQ
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🚀 Comprehensive Guide: How to Prepare for an Image Processing Job Interview – 500 Most Common Interview Questions

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#ImageProcessing #ComputerVision #OpenCV #Python #InterviewPrep #DigitalImageProcessing #MachineLearning #AI #SignalProcessing #ComputerGraphics

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Python Cheat Sheet

📖 Compact Python cheat sheet covering setup, syntax, data types, variables, strings, control flow, functions, classes, errors, and I/O.

🏷️ #Python
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Advanced Python Test

1. What is the output of the following code?
def func(x, l=[]):
for i in range(x):
l.append(i * i)
return l

print(func(2))
print(func(3, []))
print(func(3))

A) [0, 1] [0, 1, 4] [0, 1, 4]
B) [0, 1] [0, 1, 4] [0, 1, 4, 0, 1, 4]
C) [0, 1] [0, 1, 4] [0, 1, 4, 0, 1, 4, 0, 1, 4]
D) [0, 1] [0, 1, 4] [0, 1, 4, 0, 1, 4, 0, 1, 4, 0, 1, 4]

2. Which statement about metaclasses in Python is TRUE?
A) A metaclass is used to create class instances
B) The __call__ method of a metaclass controls instance creation
C) All classes must explicitly specify a metaclass
D) Metaclasses cannot inherit from other metaclasses

3. What does this decorator do?
from functools import wraps

def debug(func):
@wraps(func)
def wrapper(*args, **kwargs):
print(f"Calling {func.__name__}")
return func(*args, **kwargs)
return wrapper

A) Measures function execution time
B) Logs function calls with arguments
C) Prints the function name when called
D) Prevents function execution in debug mode

4. What is the purpose of context managers?
A) To manage class inheritance hierarchies
B) To handle resource allocation and cleanup
C) To create thread-safe operations
D) To optimize memory usage in loops

#Python #AdvancedPython #CodingTest #ProgrammingQuiz #PythonDeveloper #CodeChallenge


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Here are links to the most important free Python courses with a brief description of their value.


1. Coursera: Python for Everybody
Link: https://www.coursera.org/specializations/python
Importance: A perfect starting point for absolute beginners. Covers Python fundamentals and basic data structures, leading to web scraping and database access.

2. freeCodeCamp: Scientific Computing with Python
Link: https://www.freecodecamp.org/learn/scientific-computing-with-python/
Importance: Project-based certification. You build applications like a budget app or a time calculator, reinforcing learning through practical, portfolio-worthy projects.

3. Harvard's CS50P: CS50's Introduction to Programming with Python
Link: https://cs50.harvard.edu/python/2022/
Importance: A rigorous university-level course. Teaches core concepts and problem-solving skills with exceptional depth and clarity, preparing you for complex programming challenges.

4. Real Python Tutorials
Link: https://realpython.com/
Importance: An extensive resource for all levels. Offers in-depth articles, tutorials, and code examples on nearly every Python topic, from basics to advanced specialized libraries.

5. W3Schools Python Tutorial
Link: https://www.w3schools.com/python/
Importance: Excellent for quick reference and interactive learning. Allows you to read a concept and test code directly in the browser, ideal for fast learning and checking syntax.

6. Google's Python Class
Link: https://developers.google.com/edu/python
Importance: A concise, fast-paced course for those with some programming experience. Includes lecture videos and well-designed exercises to quickly get up to speed.

#Python #LearnPython #PythonProgramming #Coding #FreeCourses #PythonForBeginners #Developer #Programming


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