When this happens...
Job Created
Job Started
Job Completed
Automatically do this!
Enable Integrations or automations with these events of Phrase and Coursera: Data Science Specialization
Triggers when a Phrase Job has been created, but not yet started.
Triggers when a Phrase Job is marked as in-progress.
Triggers when a Phrase Job has been marked as completed.
Starts an existing job that is in draft state.
Completed a job that is in the 'in_progress' state.
Delete's a job from phrase.
Creates a new comment in the specified job.
This action will delete a comment from the specified job.
This action updates a comment to a specific job.
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To start, connect both your Phrase and Coursera: Data Science Specialization accounts to viaSocket. Once connected, you can set up a workflow where an event in Phrase triggers actions in Coursera: Data Science Specialization (or vice versa).
Absolutely. You can customize how Phrase 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 Phrase 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 Phrase 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.
Phrase is a comprehensive localization management platform that streamlines the translation and localization process for businesses, enabling them to reach global audiences effectively.
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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