Python Data Science Interview Questions Guide

Data science interviews test your Python skills. You need to know libraries and concepts. This guide covers key questions.

We will look at data manipulation and statistics. We will also cover machine learning. Practical examples are included.

Core Python and Data Structures

Questions start with Python basics. You must explain data structures. Know lists, dictionaries, and sets.

You might be asked about list comprehensions. They provide a concise way to create lists. They are faster than loops.

# Example: List Comprehension
numbers = [1, 2, 3, 4, 5]
squared = [x**2 for x in numbers]
print(squared)
    

[1, 4, 9, 16, 25]
    

Know how to use lambda functions. They are small anonymous functions. They are used with map() and filter().

Data Manipulation with Pandas

Pandas is essential for data work. You must handle DataFrames well. Common tasks include filtering and grouping.

You should master data analysis with pandas. It is a key skill for any data scientist. Our Master Data Analysis with Pandas Python Guide can help.

Be ready to clean messy data. This involves handling missing values. Use fillna() or dropna().

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', None], 'Score': [85, None, 90]}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

# Handle missing values
df_filled = df.fillna({'Name': 'Unknown', 'Score': df['Score'].mean()})
print("\nDataFrame after filling missing values:")
print(df_filled)
    

Original DataFrame:
    Name  Score
0  Alice   85.0
1    Bob    NaN
2   None   90.0

DataFrame after filling missing values:
      Name  Score
0    Alice   85.0
1      Bob   87.5
2  Unknown   90.0
    

Merging datasets is another common task. Use merge() or concat(). Know the different join types.

Statistics and Probability Questions

You will face statistics questions. Understand mean, median, and mode. Know variance and standard deviation.

Explain the central limit theorem. It is fundamental. It states sample means approximate a normal distribution.

You might code statistical functions. Use NumPy or SciPy. Calculate correlation or p-values.

import numpy as np
from scipy import stats

# Sample data
sample_data = [23, 45, 67, 23, 89, 34, 56]

# Calculate basic statistics
mean_val = np.mean(sample_data)
median_val = np.median(sample_data)
std_val = np.std(sample_data)

print(f"Mean: {mean_val:.2f}")
print(f"Median: {median_val}")
print(f"Standard Deviation: {std_val:.2f}")
    

Mean: 48.14
Median: 45.0
Standard Deviation: 22.72
    

Exploratory Data Analysis (EDA)

EDA is a critical first step. It involves summarizing data. You visualize patterns and spot anomalies.

Use libraries like Matplotlib and Seaborn. Create histograms and scatter plots. Check our Exploratory Data Analysis Python Guide & Techniques.

Describe the data with describe(). Look for skewness and outliers. This informs your modeling choices.

Machine Learning Concepts

Know the difference between supervised and unsupervised learning. Supervised uses labeled data. Unsupervised finds hidden patterns.

Explain bias-variance tradeoff. High bias causes underfitting. High variance causes overfitting.

You may implement an algorithm. Linear regression or k-means are common. Use scikit-learn.

from sklearn.linear_model import LinearRegression
import numpy as np

# Sample data: Years of experience vs Salary
X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)  # Features
y = np.array([30000, 35000, 50000, 40000, 65000])  # Target

# Create and train model
model = LinearRegression()
model.fit(X, y)

# Make a prediction
prediction = model.predict([[6]])
print(f"Predicted salary for 6 years experience: ${prediction[0]:.2f}")
    

Predicted salary for 6 years experience: $68200.00
    

SQL and Database Integration

Data often comes from databases. You need SQL skills. Write queries to fetch and filter data.

Use Python to connect to databases. Libraries like SQLAlchemy help. You can also use pandas.read_sql.

For Excel files, you might use xlrd. Learn to Integrate Python xlrd with pandas for Data Analysis.

Problem-Solving and Coding Challenges

You will solve problems on a whiteboard. Practice algorithm questions. Use platforms like LeetCode.

Focus on efficiency. Explain your thought process. Write clean, commented code.

A common task is finding duplicates. Use a set for an efficient solution. This shows you understand complexity.

def find_duplicates(input_list):
    """Find duplicate items in a list."""
    seen = set()
    duplicates = set()
    for item in input_list:
        if item in seen:
            duplicates.add(item)
        else:
            seen.add(item)
    return list(duplicates)

# Test the function
test_list = [1, 2, 3, 2, 4, 5, 3, 6]
result = find_duplicates(test_list)
print(f"Duplicates in the list: {result}")
    

Duplicates in the list: [2, 3]
    

Conclusion

Preparing for data science interviews takes work. You need strong Python skills. Know pandas, statistics, and ML.

Practice explaining your code. Understand the theory behind it. Use the resources linked in this guide.

Consistent practice is the key to success. Good luck with your interview preparation.