What will you learn in API Analytics for Product Managers Course
Understand API fundamentals and their role in product ecosystems
Define and track key API metrics: usage, performance, errors, and adoption
Analyze API data to drive product decisions and enhance developer experiences
Design dashboards and reports to communicate API health and growth
Implement A/B tests and behavioral analytics for API feature rollouts
Program Overview
Module 1: Introduction to API Analytics
⏳ 1 week
Topics: API ecosystem, developer portals, analytics importance
Hands-on: Map out your product’s API landscape and stakeholder needs
Module 2: Key API Metrics & Instrumentation
⏳ 1 week
Topics: Throughput, latency, error rates, adoption, churn metrics
Hands-on: Instrument a sample API with logging to capture essential metrics
Module 3: Data Collection & ETL for APIs
⏳ 1 week
Topics: Log aggregation, event pipelines, data warehousing concepts
Hands-on: Build an ETL workflow to ingest API logs into a BI-ready store
Module 4: Dashboarding & Visualization
⏳ 1 week
Topics: Dashboard design principles, tooling (e.g. Grafana, Tableau)
Hands-on: Create an API health dashboard displaying real-time KPIs
Module 5: Behavioral & Cohort Analysis
⏳ 1 week
Topics: Developer segmentation, feature usage patterns, retention curves
Hands-on: Perform cohort analysis on API adoption over time
Module 6: Experimentation & Growth Strategies
⏳ 1 week
Topics: A/B testing for API features, rate limit experiments, feedback loops
Hands-on: Design and analyze an A/B test for a new API version
Module 7: Advanced Analytics & Predictive Insights
⏳ 1 week
Topics: Anomaly detection, capacity forecasting, usage trend modeling
Hands-on: Implement a simple threshold-based alerting system for anomalies
Module 8: Product Roadmapping & Communication
⏳ 1 week
Topics: Translating analytics into product strategy, stakeholder presentations
Hands-on: Prepare a product roadmap slide deck based on API analytics findings
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Job Outlook
API analytics skills are critical for roles like Product Manager, API Product Owner, and Technical PM
High demand in SaaS, fintech, and platforms that rely on external integrations
Salaries range from $90,000 to $140,000+ depending on experience and industry
Empowers PMs to make data-driven decisions, improving API adoption and revenue growth
Explore More Learning Paths
Boost your API and product management skills with these related courses and resources. These learning paths will help you understand API design, development, and analytics to make data-driven product decisions.
Related Courses
API Design and Fundamentals of Google Cloud’s Apigee API Platform
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Understand the fundamentals of APIs and web services, including request-response protocols and practical applications.
Related Reading
What Is Product Management
Discover how API knowledge supports product managers in delivering better, data-driven products and services.
Specification: API Analytics for Product Managers Course
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FAQs
- Prior technical experience is not mandatory; course assumes beginner-level knowledge.
- Familiarity with BI tools and basic SQL is helpful but not required.
- Focuses on understanding API metrics, dashboards, and analytics interpretation.
- Hands-on exercises allow learners to practice without deep coding.
- Ideal for PMs seeking to bridge business and technical teams.
- Includes hands-on labs to track and analyze API metrics.
- Dashboards are built using real-world tools like Grafana and Tableau.
- Exercises simulate A/B testing and feature adoption analysis.
- Focuses on translating data into strategic product decisions.
- Capstone projects mirror real product management scenarios.
- Teaches tracking adoption, error rates, and latency for insights.
- Hands-on exercises on developer segmentation and feature usage.
- Cohort analysis helps identify retention and churn patterns.
- A/B testing modules enable experimentation on API features.
- Provides strategies to improve developer engagement and API adoption.
- Covers threshold-based alerting for anomaly detection.
- Introduces usage trend modeling and capacity forecasting.
- Focuses on actionable insights rather than complex statistical modeling.
- Hands-on labs provide practical experience with predictive analytics.
- Advanced machine learning approaches are not deeply covered.
- Dedicate 3–5 hours per week to complete modules and exercises.
- Focus on one module or lab at a time for better retention.
- Build dashboards and document API analytics insights for practice.
- Complete cohort analyses and A/B testing exercises incrementally.
- Engage with communities or peers for guidance and feedback.
