
When this happens...
Automatically do this!
Enable Integrations or automations with these events of Shipday and Coursera: Data Science Specialization
Schedule a New Order.
Search order by email and phone number.
Delete Order By Id
Adds a new Carrier .
Delete a Carrier
Update your Order
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Workflow automation is the process of using technology to execute repetitive tasks with minimal human intervention, creating a seamless flow of activities.
To start, connect both your Shipday and Coursera: Data Science Specialization accounts to viaSocket. Once connected, you can set up a workflow where an event in Shipday triggers actions in Coursera: Data Science Specialization (or vice versa).
Absolutely. You can customize how Shipday data is recorded in Coursera: Data Science Specialization. This includes choosing which data fields go into which fields of Coursera: Data Science Specialization, setting up custom formats, and filtering out unwanted information.
The data sync between Shipday and Coursera: Data Science Specialization typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Shipday and Coursera: Data Science Specialization. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
Shipday is an all-in-one delivery management platform that helps local businesses plan routes, dispatch drivers, and provide real-time order tracking with automated SMS ETAs. It supports both in-house and third‑party delivery workflows, integrates with ecommerce and POS platforms, and offers AI-powered dispatch and route optimization to speed up last‑mile deliveries.
Learn MoreMaster the art of data science with Coursera’s Data Science Specialization, created by Johns Hopkins University. This program guides you through the full data science pipeline—from data wrangling and statistical analysis to machine learning and data visualization—using R and real-world projects.
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