Calculate Daily Survey Response Rates by Country

Question

You are asked to calculate the daily survey response rates by country using a dataset. The goal is to demonstrate your data manipulation skills and your ability to derive insights from data.

Codemia · Accepted Answer

To calculate the daily survey response rates by country, I would first define the key metrics: the number of responses received and the total number of surveys sent out per country for each day. The response rate can be calculated using the formula:

**Response Rate = (Number of Responses / Total Surveys Sent) * 100**.

Next, I would identify the data sources and ensure that I have access to the necessary datasets.

I would start by loading the relevant datasets into a DataFrame using Python's Pandas library or querying directly using SQL. If using SQL, I would write a query to aggregate the data by country and date, summing the responses and counting the total surveys sent. Here is an example SQL query:

```sql
SELECT country, date, COUNT(response_id) AS total_responses, COUNT(survey_id) AS total_surveys
FROM survey_data
GROUP BY country, date;
```

This query groups the data by country and date, providing the necessary counts for the calculation.

After obtaining the aggregated data, I would calculate the response rates using Python. Here is a sample code snippet:

```python
import pandas as pd

# Assuming df is the DataFrame containing the aggregated data

df['response_rate'] = (df['total_responses'] / df['total_surveys']) * 100
```

This code adds a new column for the response rate to the DataFrame. I would also check for any anomalies in the data, such as countries with unusually high or low response rates.

To effectively communicate the results, I would visualize the response rates using libraries like Matplotlib or Seaborn. A line chart could be used to show trends over time for each country. Here’s an example of how to create a simple line plot:

```python
import matplotlib.pyplot as plt

# Plotting the response rates
plt.figure(figsize=(12, 6))
for country in df['country'].unique():
    country_data = df[df['country'] == country]
    plt.plot(country_data['date'], country_data['response_rate'], label=country)

plt.title('Daily Survey Response Rates by Country')
plt.xlabel('Date')
plt.ylabel('Response Rate (%)')
plt.legend()
plt.show()
```

This visualization helps identify trends and patterns in response rates across different countries.

While performing this analysis, I would consider potential challenges such as missing data, variations in survey distribution, and the need for normalization if the number of surveys sent varies significantly by country. I would discuss strategies for addressing these issues, such as imputing missing values or standardizing the survey distribution process.

Calculate Daily Survey Response Rates by Country

Meta

What the Interviewer Expects

Key Topics to Cover

How to Approach This

Possible Follow-up Questions

Sharpen Your Skills on Codemia

Sample Answer

Approach

Data Preparation

Submit Your Answer

Meta Data Scientist Interview Guide

Related Questions

Optimize a slow query on products

Calculate conversion rate per user from orders

Window function: rank over sessions

Optimize a slow query on orders

Write a query to find time to first action from sessions