Graded Quiz: Exploratory Data Analysis :Data Analysis with Python (Applied Data Science Specialization) Answers 2025

1. Question 1

While exploring a dataset with Pandas, Elina wants summary statistics (count, mean, std). What should she use?

• ❌ head()

• ❌ tail()

• ✅ describe()

• ❌ summary()

Explanation:
describe() gives statistical summaries like count, mean, std, min, max — exactly what Elina needs.

2. Question 2

Pearson correlation is close to zero. What does this indicate?

• ❌ It indicates that the mean of the data is near zero.

• ❌ It indicates uncertainty between the two variables.

• ❌ It indicates minimal deviation between variables.

• ✅ It indicates that two variables are not correlated.

Explanation:
A correlation close to zero means no linear relationship exists between the variables.

3. Question 3

You want to reshape grouped data so one variable appears in rows and another in columns. Which method?

• ❌ merge()

• ❌ groupby()

• ✅ pivot()

• ❌ pcolor()

Explanation:
pivot() restructures data into a matrix-like table with rows, columns, and values.

4. Question 4

df_grp = df_test.groupby([‘body-style’], as_index=False).mean()
What are df_grp[‘price’] values?

• ❌ It averages the body-style variable data values.

• ❌ It writes the mean value of each body style price to the data frame. (Too vague)

• ❌ It averages the price for all body labels.

• ✅ It averages the price for each body style.

Explanation:
Grouping by body-style and taking mean computes the average price for each body-style category.

5. Question 5

Peak RPM vs price shows low/high RPM produce low/high prices with no clear trend. What does this indicate?

• ✅ Weak or no correlation

• ❌ Strong negative correlation

• ❌ Uncertain correlation

• ❌ Strong positive correlation

Explanation:
If no consistent pattern exists, the correlation is weak or absent.

🧾 Summary Table