Exploring Data Types & Data Structures via Globalmart Challenges

Globalmart is a rapidly growing e-commerce startup with a strong presence in the North American market. Specializing in three core business lines—Technology, Office Supplies, and Furniture—the company has successfully scaled its customer base and order volume.

The Challenge:

While sales are booming, the company's internal operations are struggling to keep pace. Key business processes are still reliant on manual effort and basic tools, which were sufficient in the early days but are now creating significant operational friction. The data analytics team has identified critical bottlenecks that are impacting both efficiency and customer satisfaction:

• Reactive Customer Service: Manually sifting through hundreds of daily product reviews is slow and inconsistent. The inability to quickly gauge customer sentiment means the company is often late to address product issues or identify positive trends.
• High Support Load: The customer service team is overwhelmed with a constant stream of inquiries about order delivery times. This not only leads to a poor customer experience but also ties up valuable support resources that could be focused on more complex issues.
• Risk of Financial Errors: The final calculation of order totals is performed using simple, non-scalable methods. This approach is prone to human error, risking billing inaccuracies that can damage customer trust and create complex financial reconciliation problems.

As a newly hired Data Analyst at Globalmart, you have been tasked with spearheading the transition from these manual workflows to automated solutions using Python. Your manager believes that Python's versatility and power are key to building a robust, scalable, and efficient data processing pipeline for the company.

This hands-on will guide you through solving these exact challenges. You will build practical scripts to automate key business functions, directly addressing Globalmart's pain points. By completing these tasks, you'll not only solve real-world problems but also master the fundamental Python skills necessary for any modern data analyst role.