Correlation analysis (Pearson, Spearman) in Data Analysis Python - Mini Project: Build & Apply

Correlation analysis (Pearson, Spearman)

📖 Scenario: You are a data analyst working with a small dataset of students' study hours and their exam scores. You want to find out how strongly these two variables are related.

🎯 Goal: Build a Python program that calculates both Pearson and Spearman correlation coefficients between study hours and exam scores.

📋 What You'll Learn

Create a dictionary with exact study hours and exam scores

Create a list of tuples from the dictionary

Calculate Pearson and Spearman correlation coefficients using scipy.stats

Print the correlation results exactly as specified

💡 Why This Matters

🌍 Real World

Correlation analysis helps understand relationships between variables, like how study time affects exam scores.

💼 Career

Data analysts and scientists use correlation to find patterns and insights in data for decision making.

1

Create the dataset dictionary

Create a dictionary called student_data with these exact entries: 'hours': [2, 3, 5, 8, 13] and 'scores': [50, 55, 65, 80, 95].

Data Analysis Python

# Create the student_data dictionary with hours and scores
# Your code here

Hint

Use curly braces to create a dictionary with keys 'hours' and 'scores'. Assign the exact lists as values.

2

Prepare data for correlation

Create a list of tuples called data_pairs by pairing each element from student_data['hours'] with the corresponding element from student_data['scores'] using the zip function.

Data Analysis Python

student_data = {'hours': [2, 3, 5, 8, 13], 'scores': [50, 55, 65, 80, 95]}
# Create data_pairs as a list of tuples pairing hours and scores
# Your code here

Hint

Use zip(student_data['hours'], student_data['scores']) and convert it to a list.

3

Calculate Pearson and Spearman correlations

Import pearsonr and spearmanr from scipy.stats. Then calculate the Pearson correlation coefficient and Spearman correlation coefficient between student_data['hours'] and student_data['scores']. Store the coefficients in variables pearson_corr and spearman_corr respectively.

Data Analysis Python

student_data = {'hours': [2, 3, 5, 8, 13], 'scores': [50, 55, 65, 80, 95]}
data_pairs = list(zip(student_data['hours'], student_data['scores']))
# Import pearsonr and spearmanr from scipy.stats
# Calculate pearson_corr and spearman_corr
# Your code here

Hint

Use pearsonr(x, y) and spearmanr(x, y) to get correlation coefficients. Assign only the first returned value to variables.

4

Print the correlation results

Print the Pearson correlation coefficient with the exact text: "Pearson correlation: " followed by pearson_corr rounded to 3 decimal places. Then print the Spearman correlation coefficient with the exact text: "Spearman correlation: " followed by spearman_corr rounded to 3 decimal places.

Data Analysis Python

from scipy.stats import pearsonr, spearmanr

student_data = {'hours': [2, 3, 5, 8, 13], 'scores': [50, 55, 65, 80, 95]}
data_pairs = list(zip(student_data['hours'], student_data['scores']))

pearson_corr, _ = pearsonr(student_data['hours'], student_data['scores'])
spearman_corr, _ = spearmanr(student_data['hours'], student_data['scores'])
# Print the Pearson and Spearman correlation coefficients rounded to 3 decimals
# Your code here

Hint

Use print(f"Pearson correlation: {pearson_corr:.3f}") and similarly for Spearman.