Aprenda Summarizing Project Performance | Analyzing and Visualizing Freelance Data

Tracking your project performance is essential for any freelancer who wants to build a sustainable and successful business. By monitoring key metrics such as project completion rates and client satisfaction, you gain valuable insights into your strengths and areas for improvement. These metrics help you identify patterns, set realistic goals, and make informed decisions that can lead to better client relationships and higher earnings. Regularly reviewing your performance ensures that you stay competitive and continuously deliver quality work to your clients.


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# Hardcoded project data with completion status and client ratings
projects = [
    {"name": "Website Redesign", "completed": True, "client_rating": 4.8},
    {"name": "Logo Creation", "completed": True, "client_rating": 5.0},
    {"name": "SEO Optimization", "completed": False, "client_rating": None},
    {"name": "Mobile App", "completed": True, "client_rating": 4.5},
    {"name": "Content Writing", "completed": True, "client_rating": 4.9},
    {"name": "Data Analysis", "completed": False, "client_rating": None}
]

# Calculate completion rate
total_projects = len(projects)
completed_projects = sum(1 for p in projects if p["completed"])
completion_rate = completed_projects / total_projects * 100

# Calculate average client rating for completed projects
ratings = [p["client_rating"] for p in projects if p["completed"] and p["client_rating"] is not None]
average_rating = sum(ratings) / len(ratings) if ratings else 0

print(f"Completion Rate: {completion_rate:.1f}%")
print(f"Average Client Rating: {average_rating:.2f}")

When you interpret your project performance metrics, focus on what the numbers reveal about your freelance business. A high completion rate indicates reliability and effective project management, while a lower rate may signal issues with scope, deadlines, or client communication. Average client ratings reflect how satisfied your clients are with your work; consistently high ratings suggest strong client relationships and quality output. Use these insights to pinpoint which areas are working well and where you might need to adjust your approach, such as improving time management or refining your client onboarding process. Over time, tracking these metrics allows you to set benchmarks and measure your progress as you grow your freelance career.


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def generate_summary_report(projects):
    total_projects = len(projects)
    completed = sum(1 for p in projects if p["completed"])
    ratings = [p["client_rating"] for p in projects if p["completed"] and p["client_rating"] is not None]
    avg_rating = sum(ratings) / len(ratings) if ratings else 0
    report = {
        "Total Projects": total_projects,
        "Completed Projects": completed,
        "Completion Rate (%)": round(completed / total_projects * 100, 1) if total_projects else 0,
        "Average Client Rating": round(avg_rating, 2)
    }
    return report

# Example usage:
summary = generate_summary_report(projects)
for key, value in summary.items():
    print(f"{key}: {value}")

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 2. Capítulo 6

Pergunte à IA

Pergunte o que quiser ou experimente uma das perguntas sugeridas para iniciar nosso bate-papo

Suggested prompts:

Can you explain how to interpret the summary report results?

What other metrics should I track for my freelance projects?

How can I improve my completion rate or client rating?

Deslize para mostrar o menu


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# Hardcoded project data with completion status and client ratings
projects = [
    {"name": "Website Redesign", "completed": True, "client_rating": 4.8},
    {"name": "Logo Creation", "completed": True, "client_rating": 5.0},
    {"name": "SEO Optimization", "completed": False, "client_rating": None},
    {"name": "Mobile App", "completed": True, "client_rating": 4.5},
    {"name": "Content Writing", "completed": True, "client_rating": 4.9},
    {"name": "Data Analysis", "completed": False, "client_rating": None}
]

# Calculate completion rate
total_projects = len(projects)
completed_projects = sum(1 for p in projects if p["completed"])
completion_rate = completed_projects / total_projects * 100

# Calculate average client rating for completed projects
ratings = [p["client_rating"] for p in projects if p["completed"] and p["client_rating"] is not None]
average_rating = sum(ratings) / len(ratings) if ratings else 0

print(f"Completion Rate: {completion_rate:.1f}%")
print(f"Average Client Rating: {average_rating:.2f}")


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def generate_summary_report(projects):
    total_projects = len(projects)
    completed = sum(1 for p in projects if p["completed"])
    ratings = [p["client_rating"] for p in projects if p["completed"] and p["client_rating"] is not None]
    avg_rating = sum(ratings) / len(ratings) if ratings else 0
    report = {
        "Total Projects": total_projects,
        "Completed Projects": completed,
        "Completion Rate (%)": round(completed / total_projects * 100, 1) if total_projects else 0,
        "Average Client Rating": round(avg_rating, 2)
    }
    return report

# Example usage:
summary = generate_summary_report(projects)
for key, value in summary.items():
    print(f"{key}: {value}")

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 2. Capítulo 6

Summarizing Project Performance

1. Why is it important to track project completion rates?

2. Which metric can help you identify your most satisfied clients?

3. How can summary reports support business growth for freelancers?

Summarizing Project Performance

1. Why is it important to track project completion rates?

2. Which metric can help you identify your most satisfied clients?

3. How can summary reports support business growth for freelancers?