Module 06 — Error Handling, Exceptions, and Debugging#

Graduate MSBA Module Overview

Real-world data is messy. Files have missing values. Users enter unexpected inputs. APIs return incomplete records. Calculations encounter edge cases. In business analytics, your code needs to handle these situations gracefully rather than crashing.

Error handling is how Python manages unexpected situations without stopping execution. An exception is Python’s signal that something unexpected occurred. The try/except structure lets you anticipate potential errors, catch them when they occur, and respond intelligently. Debugging is the complementary skill of identifying and fixing errors when they do occur.

Course Connections#

These concepts connect directly to building trustworthy analytics workflows. An analysis that crashes when it encounters a missing value is not production-ready. A pipeline that handles exceptions gracefully, logs meaningful errors, and continues processing is the foundation of reliable, professional analytics work.

Quick Code Example#

def safe_calculate_average(purchase_list, customer_name):
    try:
        if len(purchase_list) == 0:
            raise ValueError('Purchase list is empty')
        average = sum(purchase_list) / len(purchase_list)
        return round(average, 2)
    except TypeError:
        print(f'Warning: Invalid data type in purchases for {customer_name}')
        return None
    except ValueError as e:
        print(f'Warning: {e} for {customer_name}')
        return None

customers = [
    {'name': 'Alice Johnson', 'purchases': [250.50, 180.75, 420.25]},
    {'name': 'Bob Martinez', 'purchases': []},
    {'name': 'Carol Chen', 'purchases': [300.00, 'N/A', 290.75]}
]

for customer in customers:
    avg = safe_calculate_average(customer['purchases'], customer['name'])
    if avg:
        print(f"{customer['name']}: Average Purchase ${avg}")

Expected Output:

Alice Johnson: Average Purchase $283.83
Warning: Purchase list is empty for Bob Martinez
Warning: Invalid data type in purchases for Carol Chen

Audio Overview#

Learning Progression#

Module Pages#

• Concept → — Deep narrative on error handling and debugging
• Advanced → — Extended code with multiple exception types and logging
• Notebook → — Jupyter notebook lab description