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The Course Outline for BI Courses
The Course Outline for BI Courses

For BI SMEs: Learn about creating a course outline during the course spec process.

Miranda Van Ommeren avatar
Written by Miranda Van Ommeren
Updated over 7 months ago

A course outline describes the flow of a course on a lesson by lesson basis. In a BI course, there are two lessons in a chapter (note this is different from our Python/R/SQL courses). For each lesson, please state:

  • 3-5 measurable learning objectives

  • The terms introduced in the lesson

  • The dataset(s) used in the lesson’s screencasts and exercises

  • The different applications, services, or websites you plan on using

  • The chapter and lesson titles

In the learning objectives, we expect you to write brief statements that describe what students will be expected to learn by the end of the lesson. For help writing learning objectives, please refer to Course-level learning objectives. The terms will give an overview of the new terminology that is introduced. Additionally, you'll need to list the dataset(s) as well as the different applications, services, or websites you plan on using. This will help us get an idea of the technical feasibility of teaching the content on our platform. You will also need to include descriptive titles for each chapter and lesson.

Please keep in mind we have a 550-word maximum for Scripts. All Audition material is included in this 550-word limit.
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​Policy on Plagiarism
Plagiarism of any kind is not tolerated by DataCamp and will result in the forfeiture of the audition process. Any content produced during the audition through any means other than your own original thought process should not be presented as your own.

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Here is a chapter example from a course, to illustrate how lessons and chapters should be scoped:

Chapter 2: The Kimball Model

Lesson 2.1: Facts and Dimensions

Learning Objective(s): Student will be able to…

  • Understand the key insights behind the Kimball model (star schema)

  • Learn about facts and dimensions

  • Decide what kinds of data fits in a fact versus a dimension

  • Import and combine other data files as supporting dimensions

  • Break out a wide data set into a fact and supporting dimensions

Terms introduced: Kimball model, Star schema, Fact, Dimension

Dataset(s): Wide World Importers Data Warehouse

Application/services: Power BI Desktop

Lesson 2.2: Star and Snowflake Schemas

Learning Objective(s): Student will be able to...

  • Form queries across facts and dimensions, whether in a star or snowflake schema

  • Differentiate between star and snowflake schemas and see how Power BI is optimized for a star schema

  • Use the Performance analyzer to compare performance between star and snowflake models

Terms introduced: Snowflake schema, Hierarchical data, Performance analyzer

Dataset(s): Wide World Importers Data Warehouse

Application/services: Power BI Desktop

DataCamp will provide feedback on the course outline based on these criteria:

  • Audience

    • Will the course’s learner profiles be able to achieve this level of knowledge from a 4-hour course?

    • Is the topic presented in an interesting and relevant way for learners?

  • Communication

    • Are the names of chapters and lessons descriptive and informative?

    • Are the learning objectives well-formed, using measurable verbs?

    • Is there a clear list of datasets and terms for each lesson?

  • Design

    • Is the quantity of material appropriate for a 4-hour course?

    • Do you provide sufficient motivation for your subject in each lesson?

    • Does the content of the course match the course overview?

  • Content

    • Is the content technically accurate?

    • Does the material follow a logical progression (i.e., tell a story)?

    • If applicable, does each lesson enable non-multiple choice exercises (i.e., not purely conceptual)?
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