
When this happens...
New or Changed Collection
New or Updated Card
Attached or Detached Label on a Card
Attached or Detached Resource on a Card
New or Changed Comment
Card Deleted
Collection Deleted
Automatically do this!
Enable Integrations or automations with these events of vPlan and Coursera: Data Science Specialization
Triggers when a new collection is added or changed.
Triggers when a card is added or updated.
Triggers when a label is attached or detached from a card.
Triggers when a resource is attached or detached from a card.
Triggers when a new comment is added or changed.
Triggers when a card is deleted.
Create a new Activity.
Create a new resource.
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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 vPlan and Coursera: Data Science Specialization accounts to viaSocket. Once connected, you can set up a workflow where an event in vPlan triggers actions in Coursera: Data Science Specialization (or vice versa).
Absolutely. You can customize how vPlan 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 vPlan 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 vPlan 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.
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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