Enable Integrations or automations with these events of Groove and Coursera: Data Science Specialization
Triggers when a ticket is closed
Triggers when a ticket is tagged
Triggers when a ticket's priority is change
Triggers when a new ticket is created.
Triggers when a ticket is opened.
Triggers when a ticket is moved to a different mailbox.
Starts a new ticket for your customer
Changes the state of a ticket, e.g. from "opened" to "closed".
Changes the state of a ticket to "opened".
Changes the state of a ticket to "closed
Assigns an agent to a ticket.
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To start, connect both your Groove and Coursera: Data Science Specialization accounts to viaSocket. Once connected, you can set up a workflow where an event in Groove triggers actions in Coursera: Data Science Specialization (or vice versa).
Absolutely. You can customize how Groove 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 Groove 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 Groove 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.
Groove is a customer service platform designed to help businesses manage their customer support tickets, streamline communication, and improve overall customer satisfaction.
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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