Tiêu đề BTA Case Study - ZoomRyde.pdf ✅

This document is solely for the use of client personnel. No part of it may be circulated, quoted or reproduced for distribution outside of the client organization without prior written approval of ProcDNA. BTA Case Study - ZoomRyde 2025

Data Provided © 2025 ProcDNA 3 RIDES DATA: CAPTURES DETAILED INFORMATION ABOUT EACH RIDE, INCLUDING IDS OF RIDERS AND DRIVERS, TIME STAMPS (REQUEST, PICKUP, DROP-OFF), TRIP ZONES, RIDE STATUS, FARE PAID, AND RIDER RATING. DRIVERS DETAILS: CONTAINS DRIVER-LEVEL DATA SUCH AS UNIQUE ID, NAME, CITY OF OPERATION, AND REGISTRATION DATE WITH THE PLATFORM. RIDERS DETAILS: STORES RIDER INFORMATION, INCLUDING THEIR UNIQUE ID, SIGNUP DATE, AND ASSOCIATED CITY. CITY ZONES: PROVIDES METADATA FOR CITY ZONES, INCLUDING THEIR NAMES, GEOGRAPHIC COORDINATES, AND CLASSIFICATION BY ZONE TYPE (RESIDENTIAL, BUSINESS, OR MIXED-USE).
Questions © 2025 ProcDNA 4 Write a SQL or Python code to attempt the following – • Question 1: There are some inconsistencies in the data. What quality checks would you implement to ensure the datasets are clean, consistent, and analytics-ready? Write SQL or Python code to find data inconsistencies. • Question 2: Segment drivers based on number of rides: low, medium, high volume. • Question 3: Find the maximum and minimum idle time of the drivers. • Question 4: Identify “burnout risk” drivers — those with >7 hours active time per day • Question 5: If you were asked to build a dashboard showing driver performance, what key visualizations (metrics) would you include? Please refer to this file for a detailed guide on connecting to the platform and data sources, along with the steps to submit your solutions: Guide – Connect to DataLab