COURSE
Designing a Coursera Course
A well-designed Course brings together knowledge and skills that learners can apply in a meaningful context. Define what learners should be able to do by the end of the Course, then use the Course-level objectives to organize the Modules and align learning activities with instruction, practice, feedback, and assessment.
A Course at a glance
2–4 learning objectives
per Course
3–6 modules
per Course
2–8 hours
of learner time
Course Certificate
on completing the Course
Use this section as a reference during your design meetings. Your Learning Experience Designer will guide you through the relevant questions, and you’ll record your decisions in the template as you work together.
Write Course objectives
Once the Course has a clear purpose, translate that purpose into 2–4 Course-level learning objectives. Use the table below to move from the subject of the Course to what learners will be able to demonstrate. A Course-level objective typically brings together relevant knowledge and skills in a meaningful context.
Draft your objectives
Use the following stem to begin each objective:
By the end of this Course, learners will be able to…
Consider using observable action verbs such as
Some verbs may need more specificity
These verbs may describe your instructional intention, but on their own, they do not clearly indicate what learners will be able to demonstrate.
Complete the verb with what learners will act on and, when helpful, the context or criteria. A broad instructional intention such as “understand how data can support a business decision” can be translated into different observable objectives, depending on what learners should demonstrate:
- Evaluate a dataset for relevance and completeness.
- Recommend a data source for a specific business question.
- Design a simple analysis plan to address a business need.
Choose the objective(s) that best match what learners should be able to demonstrate by the end of the Course.
Worked example
Writing Course objectives for Foundations of Data Analytics for Business
Throughout this guide, we use Data Analytics for Business Decisions as a worked example. On the Specialization page, you saw how its three Courses work together. Here, we’ll look more closely at the first Course, Foundations of Data Analytics for Business, and translate its purpose into Course-level objectives.
That purpose can be translated into a small set of observable Course-level objectives.
By the end of the Course, learners will be able to:
- Translate a business need into a focused question that can be investigated with data.
- Identify the data needed to investigate a business question.
- Evaluate a dataset for relevance, completeness, and basic quality concerns.
- Prepare a simple dataset for an introductory business analysis.
These objectives describe what learners can demonstrate, rather than topics the instructor intends to cover. For example, “data quality” names a subject. “Evaluate a dataset for relevance, completeness, and common quality issues” describes what learners will do with their knowledge of that subject.
Check your objectives
Is it observable?
Can learners demonstrate the objective through something they do, create, analyze, evaluate, or decide?
Is it achievable within this Course?
Can learners reasonably achieve the objective through the course’s instruction, practice, feedback, and assessment?
Is it meaningful to learners?
Does the objective describe a capability learners can use beyond the course in a professional, academic, or other real-world context?
Is it appropriate for a self-paced online experience?
Can learners develop and demonstrate the capability through the course activities and assessments, without depending on live discussion, immediate instructor support, or synchronous participation?
Does it fit the larger learning pathway?
If the course is part of a specialization, certificate, or program, do its objectives support the broader pathway goals while giving the course a clear purpose of its own?
Focus the Course scope
Now that you've checked your objectives, use them to guide decisions about the Course's scope.
For each topic, resource, or activity, ask:
- What knowledge or skills will learners gain from this?
- Do learners need it to achieve a course objective?
- Will it give learners an opportunity to practice or demonstrate what they should be able to do?
Use your answers to decide what to include, leave out, or save for another Course. A strong Course is not simply a collection of related topics. Each element should help learners develop the knowledge and skills they need to achieve the intended outcomes.
You might ask
What knowledge and skills do learners need to achieve the Course objectives?
Rather than asking
Which topics should this Course cover?
Worked example
Using a Course objective to guide scope
Consider one objective from Course 1: Foundations of Data Analytics for Business:
Course objective
Learners will be able to prepare a simple dataset for an introductory business analysis.
This objective helps faculty distinguish between content that directly supports the intended learning and content that, while interesting or valuable, may not be necessary in this Course.
Include
Learners will likely need opportunities to:
- identify the data fields relevant to a business question;
- check for missing, duplicate, or inconsistent values;
- restructure and format a simple dataset; and
- document data-preparation decisions.
Each element contributes directly to learners’ ability to prepare data for an introductory analysis.
Consider leaving out
Learners may not need:
- advanced statistical modeling;
- complex programming techniques;
- large-scale data engineering; or
- specialized methods that are not used in the Course analysis.
These topics may be valuable in another context, but they are not necessary for achieving this Course objective. Including them could increase the Course’s time and complexity without adding to the skill learners are expected to develop.
Design for flexibility
Coursera Courses often work well when they are modular and self-contained. When possible, provide enough context for learners to understand the Course's purpose, why the learning matters, and what prior knowledge or skills they need to succeed without requiring them to have completed a specific earlier Course.
Some subjects are best learned through a sequence. When learners genuinely need knowledge or skills from an earlier Course, make that dependency clear. The goal is not to eliminate prerequisites, but to make Courses as portable and understandable on their own as the learning allows.
More modular
This Course assumes familiarity with basic spreadsheet formulas and functions.
More dependent on a specific sequence
As you learned in Course 1, you will now build on the framework introduced in Module 3…
Complete your Course plan
Use this page to work through your design decisions, then record them in your Coursera Course Design Document. The sample below shows how those decisions might come together for Course 1: Foundations of Data Analytics in the example Specialization: Data Analytics for Business Decisions. Use it as a reference as you develop your own plan.
Bring your draft answers to your next conversation with your Learning Experience Designer. It's okay to leave fields blank if you're unsure, and we'll work through it together.
Course title
Best practice
Course title
Write for the learner deciding whether this Course is relevant to them. A strong title makes the topic and learner value immediately clear while giving the Course a distinct purpose. Favor specific, familiar language over academic numbering or internal terminology.
Foundations of Data Analytics for Business
Course description
Best practice
Course description
Describe the Course from the learner's perspective. Lead with what learners will gain or be able to do, rather than with a list of topics. Provide enough context for the Course to make sense on its own, even when it is part of a larger learning pathway.
Designed for professionals, students, and career changers who want to use data more effectively in business settings, this beginner-level course introduces the process of turning a business need into a question that data can help answer. Learners will practice framing business questions, identifying data requirements, evaluating data for relevance and quality, and preparing a simple dataset for analysis.
Through realistic business examples, guided practice, quizzes, and applied activities, learners will build foundational skills in business problem framing, data requirements analysis, data quality assessment, and spreadsheet-based data preparation. These skills can help learners communicate more effectively with data professionals, make more informed decisions, and prepare for further study or roles involving business analytics.
Taught by Jordan Lee, a faculty member specializing in business analytics and evidence-informed decision-making, the course offers an applied, self-paced introduction to the full process from business question to analysis-ready dataset.
Estimated time to complete
Best practice
Estimated time to complete
Estimate the learner's total experience, including videos, readings, practice, activities, and assessments — not just instructional content. Keep the scope realistic for a self-paced Coursera Course that learners may complete in shorter sessions around other responsibilities.
Approximately 7 hours
Difficulty level
Best practice
Difficulty level
Choose the level based on the knowledge, skills, and independence learners need to succeed, not on how sophisticated the subject sounds. A complex topic can still be appropriate for beginners when foundational concepts and sufficient support are built into the Course.
Beginner
Primary domain / sub-domain
Best practice
Primary domain / sub-domain
Choose the category that most directly reflects the Course's primary subject and learner value. Use the most specific appropriate Coursera domain rather than trying to represent every discipline the Course touches.
Business → Business Analytics
Secondary domain / sub-domain
Best practice
Secondary domain / sub-domain
Use a secondary domain when another area meaningfully represents the Course. It should describe a substantial part of the learning experience, not simply broaden discoverability.
Data Science → Data Analysis
Tagged skills
Best practice
Tagged skills
Choose specific skills learners will actively practice or demonstrate in this Course. Avoid broad subject labels, and select tags that align closely with the Course objectives, activities, and assessments.
Recommended background
Best practice
Recommended background
Include only the knowledge or experience learners actually need before beginning the Course. Distinguish true prerequisites from experience that may simply be helpful, and avoid creating unnecessary barriers for otherwise appropriate learners.
No prior experience with data analytics or programming is required. Basic familiarity with spreadsheets and workplace mathematics may be helpful.
What learners will learn
Best practice
What learners will learn
Write 2–4 observable, measurable Course objectives that describe meaningful capabilities learners should be able to demonstrate by the end of the Course. Objectives should be achievable within the Course and valuable to learners beyond simply completing it.
Begin with action verbs such as analyze, apply, create, evaluate, identify, interpret, recommend, or communicate. Verbs such as understand, learn, or become familiar with may describe your instructional intention, but they do not clearly indicate what learners will be able to demonstrate.
- Translate a business need into a focused question that can be investigated with data.
- Identify the data needed to investigate a business question.
- Evaluate a dataset for relevance, completeness, and basic quality concerns.
- Prepare a simple dataset for an introductory business analysis.
Instructor information and bio source link
Best practice
Instructor information and bio source link
Provide concise, learner-facing information that establishes the instructor's relevant expertise and connection to the Course topic. Prioritize experience that helps learners understand why this instructor is well positioned to guide the learning experience.
If an approved faculty bio and headshot already exist, provide a build-ready source link rather than recreating the material.
Jordan Lee is a faculty member specializing in business analytics and evidence-informed decision-making. Their teaching and research focus on helping organizations frame questions, evaluate performance, and use data to make more informed decisions.
Your templates
Your Learning Experience Designer will share your template(s) ahead of your first design meeting.