Enable Integrations or automations with these events of Breeze and Coursera: Data Science Specialization
Triggers when a new project is created.
Triggers when a new file is added.
Triggers when a new task is created.
Creates a new project.
Creates a new task.
Creates a new workspace.
List all active projects
list of all workspaces
list all activity entries
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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 Breeze and Coursera: Data Science Specialization accounts to viaSocket. Once connected, you can set up a workflow where an event in Breeze triggers actions in Coursera: Data Science Specialization (or vice versa).
Absolutely. You can customize how Breeze 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 Breeze 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 Breeze 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.
Breeze is a project management tool designed to help teams streamline their workflows, manage tasks, and collaborate effectively. It offers features such as task tracking, time tracking, and project planning to ensure that projects are completed on time and within budget.
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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