24 Week 9 Project: Grade Analysis Tool
Make sure you’ve completed: - All of Part I and Part II - Chapter 10: Working with Data - Understanding of CSV files and data processing
You should be ready to: - Process real-world data files - Calculate statistics and insights - Handle messy, imperfect data - Create meaningful reports
24.1 Project Overview
This project combines everything you’ve learned about data processing to create a real tool that teachers and students can use. You’ll analyze grade data from CSV files, calculate statistics, identify trends, and generate actionable insights.
This is where programming becomes genuinely useful - solving real problems with real data.
24.2 The Problem to Solve
Educators need to understand their students’ performance! Your grade analyzer should: - Read grade data from CSV files - Calculate class averages, medians, and ranges - Identify struggling students - Find grade distribution patterns - Generate progress reports - Handle missing or invalid data gracefully
24.3 Architect Your Solution First
Before writing any code or consulting AI, design your grade analyzer:
1. Understand the Data
What might a gradebook CSV look like?
StudentID,Name,Quiz1,Quiz2,MidTerm,Project1,Quiz3,Final,Attendance
001,Alice Johnson,85,92,88,91,89,94,95
002,Bob Smith,78,65,82,79,81,77,88
003,Charlie Brown,91,88,94,96,93,89,100
004,Diana Prince,,85,90,88,87,92,92
005,Eve Wilson,45,52,48,65,58,61,752. Design Your Analysis Features
Plan what insights you’ll generate: - [ ] Individual student summaries - [ ] Class performance statistics - [ ] Grade distribution analysis - [ ] Improvement/decline trends - [ ] Missing assignment identification - [ ] At-risk student alerts
3. Identify Data Challenges
Real gradebook data has problems: - [ ] Missing grades (empty cells) - [ ] Invalid entries (“absent”, “N/A”, “103%”) - [ ] Inconsistent formatting - [ ] Extra or missing columns - [ ] Student names with special characters
24.4 Implementation Strategy
Phase 1: Basic Data Loading
- Read CSV file safely
- Handle missing values
- Convert grades to numbers
- Validate data ranges
Phase 2: Core Analytics
- Calculate averages per student
- Compute class statistics
- Identify grade distributions
- Generate basic reports
Phase 3: Advanced Insights
- Trend analysis (improvement/decline)
- Correlation between assignments
- At-risk student identification
- Visual data representation
24.5 AI Partnership Guidelines
Effective Prompts for This Project
✅ Good Learning Prompts:
"I'm analyzing grade data from CSV. Some cells are empty or contain
'N/A'. Show me how to safely convert grade values to numbers,
handling these edge cases.""I have a list of student grade dictionaries. How do I calculate
the median grade for the class? Show me both sorted and
statistics module approaches."❌ Avoid These Prompts: - “Build a complete grade analysis system” - “Create a machine learning model for grade prediction” - “Add database integration and web interface”
AI Learning Progression
Data Cleaning Phase: Handling messy data
"My CSV has grades like '85', '92.5', 'N/A', '', and '102'. How do I clean and validate these values?"Statistics Phase: Mathematical analysis
"I need to calculate mean, median, and standard deviation for a list of grades. Show me simple implementations."Pattern Recognition: Finding insights
"How can I compare a student's recent grades to their earlier grades to detect improvement or decline?"
24.6 Requirements Specification
Functional Requirements
Your grade analyzer must:
- Data Processing
- Read standard CSV grade files
- Handle missing or invalid grades
- Support multiple assignment types
- Validate grade ranges (0-100)
- Statistical Analysis
- Calculate student averages
- Compute class statistics (mean, median, mode)
- Find grade distributions
- Identify outliers
- Reporting Features
- Individual student reports
- Class summary statistics
- At-risk student alerts
- Grade trend analysis
- Error Handling
- Graceful handling of bad data
- Clear error messages
- Data validation warnings
- Missing file handling
Learning Requirements
Your implementation should: - [ ] Use file I/O for CSV processing - [ ] Demonstrate data cleaning techniques - [ ] Apply statistical calculations - [ ] Show real-world data handling - [ ] Include comprehensive error handling
24.7 Sample Interaction
Here’s how your analyzer might work:
📊 GRADE ANALYSIS TOOL 📊
════════════════════════════
Loading grades from 'class_grades.csv'...
✅ Found 25 students with 7 assignments each
CLASS SUMMARY
═════════════
Total Students: 25
Assignments: Quiz1, Quiz2, MidTerm, Project1, Quiz3, Final, Attendance
Overall Class Statistics:
- Average: 84.2%
- Median: 86.0%
- Highest: 98.5% (Alice Johnson)
- Lowest: 52.3% (Eve Wilson)
- Standard Deviation: 12.4
ASSIGNMENT BREAKDOWN
═══════════════════
Quiz1: Avg 82.1% | Range: 45-98
Quiz2: Avg 79.8% | Range: 52-97
MidTerm: Avg 85.3% | Range: 48-96
Project1: Avg 87.2% | Range: 65-98
Quiz3: Avg 84.6% | Range: 58-95
Final: Avg 83.9% | Range: 61-94
Attendance:Avg 91.2% | Range: 75-100
AT-RISK STUDENTS
═══════════════
⚠️ Eve Wilson (Student ID: 005)
- Current Average: 52.3%
- Missing: 0 assignments
- Trend: Improving (+8% from early to recent grades)
- Recommendation: Schedule tutoring session
GRADE DISTRIBUTION
═════════════════
A (90-100): 6 students (24%)
B (80-89): 11 students (44%)
C (70-79): 5 students (20%)
D (60-69): 2 students (8%)
F (0-59): 1 student (4%)
INDIVIDUAL REPORTS
═════════════════
[Showing top 3 students]
1. Alice Johnson (ID: 001)
Average: 91.4% | Grade: A
Strongest: Final (94%), Quiz2 (92%)
Needs work: Quiz1 (85%)
2. Charlie Brown (ID: 003)
Average: 91.3% | Grade: A
Strongest: Project1 (96%), MidTerm (94%)
Needs work: Final (89%)
[Full reports available - press Enter to see all students]24.8 Development Approach
Step 1: Safe CSV Reading
Start with robust file handling:
import csv
def load_grades(filename):
"""Load grades from CSV file with error handling"""
students = []
try:
with open(filename, 'r') as file:
reader = csv.DictReader(file)
for row in reader:
students.append(row)
except FileNotFoundError:
print(f"Error: Could not find file '{filename}'")
return None
except Exception as e:
print(f"Error reading file: {e}")
return None
print(f"Loaded {len(students)} student records")
return studentsStep 2: Data Cleaning Functions
Handle messy real-world data:
def clean_grade(grade_str):
"""Convert grade string to float, handling edge cases"""
if not grade_str or grade_str.strip() == "":
return None
# Remove common non-numeric characters
cleaned = grade_str.strip().replace('%', '')
# Handle common text values
if cleaned.lower() in ['n/a', 'na', 'absent', 'missing']:
return None
try:
grade = float(cleaned)
# Validate range
if 0 <= grade <= 100:
return grade
else:
print(f"Warning: Grade {grade} outside valid range")
return None
except ValueError:
print(f"Warning: Could not parse grade '{grade_str}'")
return None
def clean_student_grades(student):
"""Clean all grades for a student"""
cleaned = {}
cleaned['name'] = student.get('Name', 'Unknown')
cleaned['id'] = student.get('StudentID', 'Unknown')
# Get all assignment columns (skip Name and StudentID)
assignment_columns = [col for col in student.keys()
if col not in ['Name', 'StudentID']]
cleaned['assignments'] = {}
for assignment in assignment_columns:
grade = clean_grade(student.get(assignment, ''))
cleaned['assignments'][assignment] = grade
return cleanedStep 3: Statistical Analysis
Build your analysis toolkit:
def calculate_student_average(student):
"""Calculate average grade for a student"""
grades = [g for g in student['assignments'].values() if g is not None]
if not grades:
return None
return sum(grades) / len(grades)
def calculate_class_statistics(students):
"""Calculate class-wide statistics"""
all_averages = []
for student in students:
avg = calculate_student_average(student)
if avg is not None:
all_averages.append(avg)
if not all_averages:
return None
all_averages.sort()
n = len(all_averages)
stats = {
'count': n,
'mean': sum(all_averages) / n,
'median': all_averages[n//2] if n % 2 == 1 else
(all_averages[n//2-1] + all_averages[n//2]) / 2,
'min': min(all_averages),
'max': max(all_averages)
}
# Calculate standard deviation
mean = stats['mean']
variance = sum((x - mean) ** 2 for x in all_averages) / n
stats['std_dev'] = variance ** 0.5
return statsStep 4: Trend Analysis
Identify patterns in performance:
def analyze_student_trend(student):
"""Analyze if student is improving or declining"""
grades = []
assignments = student['assignments']
# Get grades in chronological order (assuming column order)
for assignment, grade in assignments.items():
if grade is not None:
grades.append(grade)
if len(grades) < 3: # Need enough data points
return "Insufficient data"
# Compare first third vs last third
third = len(grades) // 3
early_avg = sum(grades[:third+1]) / (third+1)
late_avg = sum(grades[-third-1:]) / (third+1)
improvement = late_avg - early_avg
if improvement > 5:
return f"Improving (+{improvement:.1f}%)"
elif improvement < -5:
return f"Declining ({improvement:.1f}%)"
else:
return "Stable"24.9 Advanced Features
Grade Distribution Analysis
def analyze_grade_distribution(students):
"""Analyze how grades are distributed"""
distribution = {'A': 0, 'B': 0, 'C': 0, 'D': 0, 'F': 0}
for student in students:
avg = calculate_student_average(student)
if avg is not None:
if avg >= 90:
distribution['A'] += 1
elif avg >= 80:
distribution['B'] += 1
elif avg >= 70:
distribution['C'] += 1
elif avg >= 60:
distribution['D'] += 1
else:
distribution['F'] += 1
total = sum(distribution.values())
if total > 0:
for grade in distribution:
count = distribution[grade]
percentage = (count / total) * 100
print(f"{grade} ({grade_ranges[grade]}): {count} students ({percentage:.1f}%)")At-Risk Student Identification
def identify_at_risk_students(students, threshold=70):
"""Find students who might need help"""
at_risk = []
for student in students:
avg = calculate_student_average(student)
if avg is not None and avg < threshold:
# Count missing assignments
missing_count = sum(1 for g in student['assignments'].values()
if g is None)
trend = analyze_student_trend(student)
at_risk.append({
'student': student,
'average': avg,
'missing_assignments': missing_count,
'trend': trend
})
return sorted(at_risk, key=lambda x: x['average'])24.10 Real-World Data Challenges
Challenge 1: Extra Credit Handling
def handle_extra_credit(grade):
"""Handle grades over 100% properly"""
if grade > 100:
return min(grade, 110) # Cap at 110%
return gradeChallenge 2: Different Grading Scales
def normalize_grade(grade, scale='100'):
"""Convert different grading scales to 100-point scale"""
if scale == '4.0':
return (grade / 4.0) * 100
elif scale == 'letter':
letter_to_number = {'A': 95, 'B': 85, 'C': 75, 'D': 65, 'F': 50}
return letter_to_number.get(grade.upper(), 0)
return grade24.11 Testing with Sample Data
Create test data to verify your analyzer:
def create_sample_data():
"""Generate sample grade data for testing"""
sample_csv = """StudentID,Name,Quiz1,Quiz2,MidTerm,Project1,Final
001,Alice Johnson,85,92,88,91,94
002,Bob Smith,78,,82,79,77
003,Charlie Brown,91,88,94,96,89
004,Diana Prince,N/A,85,90,88,92
005,Eve Wilson,45,52,48,65,61"""
with open('sample_grades.csv', 'w') as f:
f.write(sample_csv)24.12 Practice Extensions
Extension 1: Progress Tracking
- Compare current grades to previous semesters
- Track improvement over time
- Generate progress charts
Extension 2: Assignment Analysis
- Identify which assignments are most difficult
- Find correlations between different assignments
- Suggest which assignments to review
Extension 3: Class Comparison
- Compare multiple class sections
- Identify teaching effectiveness
- Benchmark against standards
24.13 Common Pitfalls and Solutions
Pitfall 1: Assuming Clean Data
Problem: Real data is messy with missing values Solution: Always validate and clean first
Pitfall 2: Division by Zero
Problem: Calculating averages with no valid grades Solution: Check for empty lists before dividing
Pitfall 3: Hardcoded Column Names
Problem: Code breaks when CSV format changes Solution: Dynamically detect assignment columns
Pitfall 4: No Data Validation
Problem: Grades of 150% or -20% crash calculations Solution: Validate ranges and handle outliers
24.14 Reflection Questions
After completing the project:
- Data Quality: What surprised you about real-world data?
- Statistics Understanding: Which calculations were most insightful?
- Error Handling: How did you make your code robust?
- User Value: How would teachers actually use this tool?
24.15 Next Week Preview
Excellent work! Next week, you’ll build a Weather Dashboard that pulls live data from APIs, creating a real-time application that connects to the internet. You’ll see how external data sources make programs dynamic and current!
Your grade analyzer proves you can turn raw data into actionable insights - a skill valuable in any field! 📊