Data Science

Reusable Lesson Template (Data Science Interview Prep)

Arun Murali

Tags: data-science; template; study-guide

[Topic Name Here]

🟪 1-Minute Summary

A 2-3 sentence explanation that captures the absolute essence. If you only read this section, you’d know enough to recognize when the topic is mentioned in an interview.

Example: “Linear Regression models the relationship between a dependent variable and one or more independent variables using a straight line. The goal is to find the best-fit line that minimizes prediction errors. It’s used when you need to predict continuous numerical values.”

🟦 Core Notes (Must-Know)

What is [Topic]?

Definition and purpose in 1-2 paragraphs

Key Concepts

  1. Concept 1: Explanation
  2. Concept 2: Explanation
  3. Concept 3: Explanation

Formulas / Key Equations

If applicable, include the most important mathematical representations

Formula 1: y = mx + b
Formula 2: MSE = (1/n) * Σ(actual - predicted)²

When to Use It

  • ✅ Use when [condition 1]
  • ✅ Use when [condition 2]
  • ❌ DON’T use when [condition 3]

Pros & Cons

Advantages:

  • ✓ Pro 1
  • ✓ Pro 2

Disadvantages:

  • ✗ Con 1
  • ✗ Con 2

🟨 Interview Triggers (What Interviewers Actually Test)

Common Interview Questions

  1. “When would you use [this technique] vs [alternative]?”

    • Answer framework: Explain the difference based on [data type/use case/assumptions]

  2. “What are the assumptions of [this method]?”

    • Answer framework: List 3-5 key assumptions

  3. “How do you evaluate [this method]?”

    • Answer framework: Name specific metrics and when to use each

Red Flags Interviewers Watch For

  • 🚩 Red flag 1: Confusing this with [similar concept]
  • 🚩 Red flag 2: Not knowing when it fails
  • 🚩 Red flag 3: Unable to explain trade-offs

How to Impress

  • 💡 Tip 1: Mention real-world application or business impact
  • 💡 Tip 2: Discuss limitations proactively
  • 💡 Tip 3: Connect to related concepts (shows depth)

🟥 Common Mistakes (Traps to Avoid)

Mistake 1: [Conceptual Mistake]

  • What people do: [Wrong approach]
  • Why it’s wrong: [Explanation]
  • Correct approach: [Right way]

Mistake 2: [Implementation Mistake]

  • What people do: [Wrong code/logic]
  • Why it’s wrong: [Explanation]
  • Correct approach: [Right way]

Mistake 3: [Interpretation Mistake]

  • What people do: [Misunderstanding]
  • Why it’s wrong: [Explanation]
  • Correct approach: [Right way]

🟩 Mini Example (Quick Application)

Scenario

A realistic but simple problem statement

“You have a dataset with [describe data]. You want to [goal]. Should you use [this technique]?”

Solution

# Import libraries
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# Sample data
X = [[1], [2], [3], [4], [5]]
y = [2, 4, 6, 8, 10]

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict
predictions = model.predict(X_test)
print(predictions)

Interpretation

Explain what the results mean and what to look for

  • The model coefficient is [value], meaning…
  • The predictions show [pattern], which indicates…
  • If you saw [different result], it would mean…

🔗 Related Topics

📚 Further Reading

  • [Link to documentation]
  • [Link to research paper if applicable]
  • [Link to tutorial]

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