Find Interview Questions for Harita

We are given:

• Today is Tuesday

• We are to find the day of the week after 62 days

🧠 Step 1: Understand the pattern of days

Days of the week repeat every 7 days.

So to find the day after 62 days, we divide 62 by 7 and take the remainder:

$$62÷7=8\text{ weeks and }6\text{ days}$$

So, 62 days = 6 days beyond a complete number of weeks

🧮 Step 2: Move 6 days ahead from Tuesday:

• Tuesday + 1 = Wednesday

• +2 = Thursday

• +3 = Friday

• +4 = Saturday

• +5 = Sunday

• +6 = Monday

✅ Final Answer:
After 62 days, it will be Monday.

• Length of the train = 100 meters

• Length of the platform = 100 meters

• Total distance to be covered = 100 m (train) + 100 m (platform) = 200 meters

• Time taken = Not given directly (you wrote “in seconds”), so we assume you want to find time if speed is given — or vice versa.

But you asked: “The speed of the train is?” — so we must be missing the time.

Let’s assume you meant to ask:

❓ If a 100-meter-long train passes a 100-meter-long platform in 10 seconds, what is the speed of the train?

✅ Step 1: Total distance = 100 + 100 = 200 meters
✅ Step 2: Time = 10 seconds
✅ Step 3: Speed = Distance ÷ Time = 200 ÷ 10 = 20 m/s
✅ Step 4: Convert to km/h → 20 × 3.6 = 72 km/h

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✅ Final Answer (if time = 10 seconds): Speed of the train = 72 km/h

If you have a different time value, please provide it and I’ll recalculate accordingly.

Incomplete or missing data is a common challenge in data analysis. Whether it’s skipped survey responses, blank spreadsheet cells, or unavailable values, missing data can affect the accuracy and reliability of your results.

The key is to handle missing data thoughtfully so you can still draw valid conclusions without misleading your interpretation.

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🔍 Common Ways to Handle Missing Data:

1. Identify the Missing Data
 Start by locating where and how much data is missing.
 Check: Is it random or following a pattern? Are entire sections missing or just a few values?

2. Remove Incomplete Entries (if appropriate)
 If only a small number of rows are missing data, and they don’t heavily impact the dataset, you can safely remove them.

3. Use Imputation (Estimate Missing Values)
 If the dataset is large and important, you can fill in missing values using methods like:
– Mean or median substitution (for numerical data)
– Mode (for categorical data)
– Regression or predictive models (for more advanced cases)

4. Use Available Data Only
 In some cases, you can perform analysis using just the complete parts of the dataset — as long as it doesn’t bias your results.

5. Flag and Acknowledge Missing Data
 Be transparent in reports. Clearly mention how much data is missing and how it was handled.

6. Ask Why the Data Is Missing
 Sometimes missing data reveals a deeper issue (e.g., system errors, survey confusion). Understanding the cause can help prevent future problems.