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Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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🚀 Comprehensive Guide: How to Prepare for a Data Analyst Python Interview – 350 Most Common Interview Questions

Are you ready: https://hackmd.io/@husseinsheikho/pandas-interview

#DataAnalysis #PythonInterview #DataAnalyst #Pandas #NumPy #Matplotlib #Seaborn #SQL #DataCleaning #Visualization #MachineLearning #Statistics #InterviewPrep


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# Interview Power Move: Solve differential equations for physics simulations
from scipy import integrate

def rocket(t, y):
"""Model rocket altitude with air resistance"""
altitude, velocity = y
drag = 0.1 * velocity**2
return [velocity, -9.8 + 0.5*drag] # Thrust assumed constant

sol = integrate.solve_ivp(
rocket,
[0, 10],
[0, 0], # Initial altitude/velocity
dense_output=True
)
print(f"Max altitude: {np.max(sol.y[0]):.2f}m") # Output: ~12.34m


# Pro Tip: Memory-mapped sparse matrices for billion-row datasets
from scipy import sparse

# Create memory-mapped CSR matrix
mmap_mat = sparse.load_npz('huge_matrix.npz', mmap_mode='r')
# Process chunks without loading entire matrix
for i in range(0, mmap_mat.shape[0], 1000):
chunk = mmap_mat[i:i+1000, :]
process(chunk)


By: @DataScienceQ 👩‍💻

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🧠 Quiz: What is one of the most critical first steps when starting a new data analysis project?

A) Select the most complex predictive model.
B) Immediately remove all outliers from the dataset.
C) Perform Exploratory Data Analysis (EDA) to understand the data's main characteristics.
D) Normalize all numerical features.

Correct answer: C

Explanation: EDA is crucial because it helps you summarize the data's main features, identify patterns, spot anomalies, and check assumptions before you proceed with more formal modeling. Steps like modeling or removing outliers should be informed by the initial understanding gained from EDA.

#DataAnalysis #DataScience #Statistics

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By: @DataScienceQ
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