A course outline defines the flow of a course on a lesson-by-lesson basis. For Cloud courses, each chapter typically includes three to four lessons, with every piece of content mapped to a learning outcome. This ensures a well-structured course that delivers value and engages learners effectively.
As detailed in the Cloud Courses on DataCamp documentation, a Cloud course consists of:
Three chapters, each containing three to four lessons.
A detailed outline that includes every lesson, video, and exercise.
What to Include in the Outline
Each exercise in the course should provide the following details:
Learning Outcome: The specific skill or knowledge the exercise will assess.
Exercise Type: Specify the type (e.g., Cloud, Drag and Drop).
Cloud Services: List any cloud services and permissions required.
Skill Mapping: Highlight links to relevant certifications or learning objectives, if applicable.
This level of detail helps determine the technical feasibility of delivering the content effectively on DataCamp’s platform.
Example Lesson Outline
Here’s an example lesson from a Cloud course to illustrate how lessons and exercises should be scoped:
Chapter 1: Understanding Data Factory Pipelines and Shortcuts
Lesson 1.1: Introduction to Fabric Data Pipelines
Certification Skills:
Ingest data using a data pipeline, dataflow, or notebook.
Schedule data pipelines.
Exercise 1.1.0: Introduction to Fabric Data Pipelines
Learning Objectives:
Understand the fundamentals of data pipelines.
Learn pipeline activities: Move and Transform, Metadata and Validation, Control Flow, Orchestrate, and Notifications.
Design dynamic and adaptable pipelines using parameters and variables.
Understand scheduling and automation of data pipelines.
Exercise Type: Video
Exercise 1.1.1: Exploring and Understanding Data Pipeline Activities
Learning Objectives:
Create a Data Pipeline in Microsoft Fabric.
Explore and understand various activity types within a Data Pipeline.
Mapped to: Ingest data using a data pipeline, dataflow, or notebook.
Exercise Type: Cloud
Exercise Idea:
Learners will create an empty data pipeline in Microsoft Fabric and explore activities like "Move and Transform," "Orchestrate," and "Control Flow." They will then answer MCQs to solidify their understanding of these activity types and their role in data pipelines.
Exercise 1.1.2: Controlling Pipeline Flow with Parameters and Variables
Learning Objectives:
Configure "Switch" activity to branch workflows based on parameter values.
Use parameters and variables to control activity flow.
Implement conditional logic within pipelines.
Mapped to: Ingest data using a data pipeline, dataflow, or notebook.
Exercise Type: Cloud
Exercise Idea:
Learners will create a pipeline that processes data differently based on batch parameters. For example, Batch 1 undergoes quick validation, while Batch 2 undergoes a detailed transformation. Using a Switch activity and variables, learners will configure the workflow and validate the output.
Exercise 1.1.3: Implementing Scheduled Pipeline Run
Learning Objectives:
Schedule a pipeline run in Microsoft Fabric.
Understand the differences between scheduled and on-demand runs.
Mapped to: Schedule data pipelines.
Exercise Type: Cloud
Exercise Idea:
Learners will configure a pipeline to run at a specific time and observe its execution. A follow-up question will reinforce their understanding of when to use scheduled versus on-demand runs.
Course Outline Review Criteria
When reviewing the course outline, DataCamp evaluates the following:
1. Audience
Is the course appropriate for the target learner profile within a 4-hour timeframe?
Are the topics engaging and relevant to the intended audience?
2. Design
Is the content quantity appropriate for a 4-hour course?
Does each lesson provide sufficient motivation for the subject?
Does the course align with certification goals, if applicable?
3. Content
Is the material technically accurate?
Does the course follow a logical progression and narrative structure?
Does each lesson include at least two interactive exercises?
The course outline phase ensures clarity, technical feasibility, and alignment with DataCamp’s educational goals.