Connect Reamaze and Coursera: Data Science Specialization to Build Intelligent Automations
Enable Integrations or automations with these events of Reamaze and Coursera: Data Science Specialization
Enable Integrations or automations with these events of Reamaze and Coursera: Data Science Specialization
Triggers
New conversation
Trigger when new conversation is created
New message
Trigger when new message is created
New contact
Trigger when new contact is created
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Frequently Asked Questions
To start, connect both your Reamaze and Coursera: Data Science Specialization accounts to viaSocket. Once connected, you can set up a workflow where an event in Reamaze triggers actions in Coursera: Data Science Specialization (or vice versa).
Absolutely. You can customize how Reamaze 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 Reamaze 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 Reamaze 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.
About Reamaze
Reamaze is a web-based customer communication platform that integrates email, social media, and live chat functionalities to provide a comprehensive support solution for businesses.
Learn MoreAbout Coursera: Data Science Specialization
Master 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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