SELECT experience_level, AVG(salary_in_usd) AS avg_salary
FROM salary_data
GROUP BY experience_level;

# Import required libraries for plotting
import matplotlib.pyplot as plt
import seaborn as sns
# Set up the style for the graphs
sns.set(style="whitegrid") # Initialize the list for storing graphs
graphs = [] plt.figure(figsize=(10, 6))
sns.barplot(x='experience_level', y='salary_in_usd', data=df, estimator=lambda x: sum(x) / len(x))
plt.title('Average Salary by Experience Level')
plt.xlabel('Experience Level')
plt.ylabel('Average Salary (USD)')
plt.xticks(rotation=45)
graphs.append(plt.gcf())
plt.show()

# Filter the data for Entry_Level and Experienced levels
entry_experienced = df[df['experience_level'].isin(['Entry_Level', 'Experienced'])] # Filter the data for Mid-Level and Senior levels
mid_senior = df[df['experience_level'].isin(['Mid-Level', 'Senior'])] # Plotting the Entry_Level vs Experienced graph
plt.figure(figsize=(10, 6))
sns.barplot(x='experience_level', y='salary_in_usd', data=entry_experienced, estimator=lambda x: sum(x) / len(x) if len(x) != 0 else 0)
plt.title('Average Salary: Entry_Level vs Experienced')
plt.xlabel('Experience Level')
plt.ylabel('Average Salary (USD)')
plt.xticks(rotation=45)
graphs.append(plt.gcf())
plt.show()

# Plotting the Mid-Level vs Senior graph
plt.figure(figsize=(10, 6))
sns.barplot(x='experience_level', y='salary_in_usd', data=mid_senior, estimator=lambda x: sum(x) / len(x) if len(x) != 0 else 0)
plt.title('Average Salary: Mid-Level vs Senior')
plt.xlabel('Experience Level')
plt.ylabel('Average Salary (USD)')
plt.xticks(rotation=45)
graphs.append(plt.gcf())
plt.show()

SELECT job_title, COUNT(*) AS job_count
FROM salary_data
GROUP BY job_title
ORDER BY job_count DESC
LIMIT 10;

plt.figure(figsize=(12, 8))
sns.countplot(y='job_title', data=df, order=df['job_title'].value_counts().index[:10])
plt.title('Most Common Job Titles in Data Science')
plt.xlabel('Job Count')
plt.ylabel('Job Title')
graphs.append(plt.gcf())
plt.show()

SELECT company_size, AVG(salary_in_usd) AS avg_salary, MIN(salary_in_usd) AS min_salary, MAX(salary_in_usd) AS max_salary
FROM salary_data
GROUP BY company_size;

plt.figure(figsize=(12, 8))
sns.barplot(x='company_size', y='salary_in_usd', data=df, estimator=lambda x: sum(x) / len(x) if len(x) != 0 else 0, order=['Small', 'Medium', 'Large'])
plt.title('Salary Distribution by Company Size')
plt.xlabel('Company Size')
plt.ylabel('Average Salary (USD)')
plt.xticks(rotation=45)
graphs.append(plt.gcf())
plt.show()

SELECT company_location, COUNT(*) AS job_count
FROM salary_data
GROUP BY company_location
ORDER BY job_count DESC
LIMIT 10;

plt.figure(figsize=(12, 8))
sns.countplot(y='company_location', data=df, order=df['company_location'].value_counts().index[:10])
plt.title('Geographical Distribution of Data Science Jobs')
plt.xlabel('Job Count')
plt.ylabel('Company Location')
graphs.append(plt.gcf())
plt.show()

SELECT job_title, AVG(salary_in_usd) AS avg_salary
FROM salary_data
GROUP BY job_title
ORDER BY avg_salary DESC
LIMIT 10;

<SQL Query here> Create a Python graph to visualize the top 10 highest-paying job titles in Data Science, similar to the insights gathered from the given SQL query above.