
When this happens...
New Contact
New Comment
New Message
Automatically do this!
Enable Integrations or automations with these events of Missive and Coursera: Data Science Specialization
Triggers when a new contact is created
Triggers when a new comment is added
Triggers when a new message is received through email ,sms etc
Creates a new contact
Updates an existing contact details
Creates a new task
Find an existing contact . Optionally Creates a new contact if none is found
Creates a new draft and optionally sends it
Creates a new post
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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 Missive and Coursera: Data Science Specialization accounts to viaSocket. Once connected, you can set up a workflow where an event in Missive triggers actions in Coursera: Data Science Specialization (or vice versa).
Absolutely. You can customize how Missive 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 Missive 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 Missive 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.
Email, group chat and tasks for productive teams. One app for all your internal and external communication.
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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