Explanation 1
Traffic and signups come from website_traffic_synthetic.csv.
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Lesson 9 of 9
Learners synthesize the module by advising a business team using a short original data scenario.
Module 1 capstone
A small online education platform has collected synthetic data about website visits, signups, student satisfaction, support response time, and study behavior. Students must use statistics to summarize what happened, identify what can and cannot be concluded, choose appropriate visuals, run R code, and write a recommendation.
Business question: Is the learning platform improving in a way that supports healthy growth, or do the data show a more cautious story?
Theory
A small education platform wants to understand traffic, signups, satisfaction, and support delays. Traffic has grown, but the team is unsure whether the extra attention is turning into healthy growth. Your job is to inspect the synthetic data, create clear charts, run first R summaries, answer interpretation questions, and write a short recommendation.
Explanation 1
Traffic and signups come from website_traffic_synthetic.csv.
Explanation 2
Satisfaction and response time come from survey_bias_synthetic.csv.
Explanation 3
The recommendation should name evidence, uncertainty, and a next action.
Worked example
Scenario
Start with head() and summary() for the traffic data. Then summarize visits and signup_count. Use table(device_type) to see the device mix.
Inspection comes before recommendation. A quick summary protects you from overreacting to one row.
Worked example
Scenario
Use a line chart for daily visits, a scatter plot for visits and signups, and a bar chart for device type counts. Then decide which chart communicates the clearest story.
Different charts answer different questions; choose the display that fits the decision.
Worked example
Scenario
The survey bias dataset includes channel, age group, satisfaction, and response time. Treat response_time_seconds as a rough support-delay measure and satisfaction as a customer experience signal.
Survey channel and response patterns can shape the story, so satisfaction data needs source context.
R connection
Start with the R lab code, then adapt it to the capstone questions.
Live R Lab
Use synthetic business datasets to run simple calculations, create objects, read a CSV, summarize variables, and make first graphics.
Ready to run
Rubric
Recommendation quality: 40%. Correct use of summaries and charts: 25%. Clear limitation statement: 20%. Professional communication: 15%. A strong answer is specific, cautious, and useful.
Reflection
Include one chart you would show, one statistic you would quote, and one limitation you would disclose.
Assistant
Coach me through the capstone by asking one question at a time. Do not give me a final recommendation until I have described the evidence and limitations.
Exit check
Check whether your recommendation process is evidence-based and cautious.
Checkpoint
Question 1 of 3. Answered 0/3. Passing score: 75%.