Most asked Data Analyst Questions
Welcome to the Most asked Data Analyst Questions. Today we will cover some basic topics on SQL, Python, Machine learning and Case Study. Let’s get started with the Most asked Data Analyst Questions.
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1) Extract Domain Name from Email ID
To extract the domain name from an email ID (e.g., [email protected]), you need to remove the part before @ and keep the domain.
email = "[email protected]"
domain = email.split("@")[1]
print(domain) # Output: domain.comExplanation:
- The split(“@”) function separates the email into two parts:
- “xyz” (before @)
- “domain.com” (after @)
- The [1] index extracts the domain.
2) Why is My App’s Conversion Rate Only 4%?
If only 4% of users complete checkout in your app, multiple factors might be affecting conversions.
Possible Reasons for Low Conversion:
1️⃣ User Experience Issues
- Too many steps (5 pages before checkout may cause drop-offs).
- Slow loading speed frustrates users.
- Complicated UI makes navigation difficult.
2️⃣ Trust & Payment Concerns
- Lack of COD options (especially in India).
- Limited payment methods (UPI, wallets missing).
- High delivery charges create last-minute cart abandonment.
3️⃣ Product-Specific Issues (Raw Meat in India)
- Hygiene concerns about meat freshness.
- Lack of cold storage assurance for safe delivery.
- Preference for local butcher shops over online purchases.
4️⃣ Marketing & Pricing Problems
- High competition from local sellers.
- Discounts & offers missing compared to competitors.
- Mismatched pricing expectations (users expect lower prices).
3) What is Log Likelihood?
Log Likelihood measures how well a model fits the data. It tells us the probability of seeing the given data under a specific model.
Layman Explanation:
- Imagine you are guessing the weather for the week.
- You have two weather forecasts:
- Model A: Predicts sunny days 80% of the time.
- Model B: Predicts sunny days 50% of the time.
- If the actual week is mostly sunny, Model A will have a higher likelihood of being correct.
Mathematically:
- The likelihood is the probability of data given a model.
- Log likelihood takes the natural logarithm (log()) to make large numbers manageable.
4) Difference Between Variance and R-Squared Error
Variance and R² (R-Squared Error) are used in regression analysis but serve different purposes.
1️⃣ Variance:
- Measures spread of data points from the mean.
- Higher variance means data is more scattered.
- Example:
- Student scores: 50, 55, 60, 90, 95 → High variance
- Student scores: 70, 72, 73, 74, 75 → Low variance
2️⃣ R-Squared (R²):
- Measures how well the model explains variance in the data.
- Ranges from 0 to 1:
- R² = 1 → Perfect fit (model explains all variance).
- R² = 0 → Model does not explain variance at all.
- Example:
- A regression model predicting house prices has R² = 0.85, meaning 85% of price variation is explained by the model.
5) What is Z-Score and How is It Used?
A Z-Score tells us how far a data point is from the mean in terms of standard deviations.
Formula:
Z = (X – μ) / σ
Where:
- X = Data point
- μ = Mean of dataset
- σ = Standard deviation
Example:
Imagine the average height of students in a class is 170 cm with a standard deviation of 5 cm.
- A student with height 180 cm has: Z = (180 – 170) / 5 = 10 / 5 = 2 → This student is 2 standard deviations above the mean!
Uses of Z-Score:
✅ Detect outliers (Z > 3 means data is unusual).
✅ Compare different datasets with different scales.
✅ Normalize data for machine learning.
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