Enable Integrations or automations with these events of Productboard and Coursera: Data Science Specialization
Triggers when a new note or piece of feedback is created in Productboard.
Triggers when a new feature/sub-feature is created
Returns detail of all notes
Returns detail of all companies
Returns detail of all features
Returns detail of all components
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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 Productboard and Coursera: Data Science Specialization accounts to viaSocket. Once connected, you can set up a workflow where an event in Productboard triggers actions in Coursera: Data Science Specialization (or vice versa).
Absolutely. You can customize how Productboard 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 Productboard 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 Productboard 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.
Productboard is a product management system that helps teams get the right products to market faster. It provides tools for gathering user feedback, prioritizing features, and aligning everyone on the roadmap.
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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