Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Can I take the course for free?

No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid.

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

Product Analytics Unlocked: Metrics to Meaningful Insight Specialization

Build Product Analytics for Data-Driven Growth.

Transform user behavior data into strategic business decision that drive measurable product success.

Instructors: Professionals from the Industry

Included with

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4 course series

Get in-depth knowledge of a subject

Intermediate level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 course series

Get in-depth knowledge of a subject

Intermediate level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Build scalable SQL data pipelines that transform millions of product events into optimized analytical datasets for business intelligence.
Apply advanced statistical techniques including clustering algorithms, A/B testing, and survival analysis for user segmentation and retention.
Design interactive dashboards and diagnostic frameworks that identify funnel bottlenecks and communicate performance insights effectively
Develop strategic prioritization skills using RICE methodology and data storytelling to influence product roadmaps and business decisions.

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English

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Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from Coursera

Specialization - 4 course series

Develop the complete product analytics skillset needed to bridge technical execution with strategic business impact. Through this four-course program, you'll learn to build robust SQL-based data pipelines, apply advanced statistical techniques like clustering algorithms and A/B testing, and create compelling analytical narratives that influence product roadmaps.

You'll gain hands-on experience with user segmentation, retention optimization through survival modeling, funnel analysis, and strategic prioritization frameworks like RICE scoring. Beyond technical proficiency, you'll develop critical communication skills to translate complex data findings into actionable business recommendations using structured storytelling approaches.

Each course builds progressively from foundational data infrastructure through advanced behavioral analysis to strategic decision-making, preparing you for senior roles in product analytics, growth analytics, and data-driven product management. By completion, you'll possess the integrated capabilities that top product teams depend on: technical depth with SQL, Python, and statistical modeling combined with strategic thinking and presentation skills that directly impact business outcomes and product direction.

Applied Learning Project

Complete industry-realistic projects that simulate authentic product analytics challenges: construct scalable data pipelines processing 10+ million user events with performance-optimized SQL architectures; develop comprehensive user segmentation and retention systems using machine learning clustering and survival analysis techniques; build interactive dashboards that diagnose conversion funnel bottlenecks and communicate performance insights; and create strategic prioritization frameworks that convert analytical findings into executive-ready business recommendations.

These integrated projects require both sophisticated technical implementation and compelling stakeholder communication, preparing you for the dual demands of senior product analytics roles where analytical rigor meets strategic influence.

Data Pipelines and SQL for Product Analytics

Course 1 9 hours

What you'll learn

Build scalable data pipelines using SQL and Pandas to transform 10+ million rows of raw event data into structured analytics datasets.
Design and optimize star schemas with Type-2 slowly changing dimensions to track historical changes in product analytics data.
Compare and implement advanced SQL window functions across different dialects like Presto and Spark for cross-platform compatibility.
Evaluate existing data warehouse schemas and propose performance refinements using aggregation techniques and indexing strategies.

User Segmentation, Experimentation, and Retention Analytics

Course 2 8 hours

What you'll learn

Apply k-means clustering to segment users and create actionable profiles that inform targeted marketing strategies and product decisions.
Design A/B tests with proper power analysis and identify common biases that can invalidate experimental results and business insights.
Calculate and compare N-day vs rolling retention metrics to evaluate user engagement and distinguish between seasonal and churn patterns.
Build Kaplan-Meier survival curves to analyze retention across user groups and determine statistical significance of differences.

Dashboarding and Funnel Analytics for Product Insights

Course 3 10 hours

What you'll learn

Build interactive dashboards that translate business KPI requests into self-service analytics tools with drill-through functionality.
Create retention heatmaps with event annotations and evaluate visualizations for accessibility and clarity improvements.
Design optimized user activation funnels by selecting critical events and identifying process redundancies for consolidation.
Apply statistical methods to analyze engagement metrics and validate predictive relationships between activation and retention.

Product Analytics for Prioritization & Data-Driven Decisions

Course 4 12 hours

What you'll learn

Design tracking systems by translating product features into event schemas and diagnose funnel regressions to pinpoint user drop-off causes.
Apply RICE prioritization framework to evaluate features and create decision memos linking data trends to strategic roadmap recommendations.
Analyze churn logs to identify attrition root causes and establish success criteria with counter-metrics for new product features.
Build predictive models using decision trees and logistic regression, then present insights using structured data storytelling frameworks.

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Professionals from the Industry

238 Courses 35,970 learners

Offered by

Coursera

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Frequently asked questions

This program is designed for aspiring and practicing Product Analysts, Growth Analysts, and data-driven Product Managers who want to advance into senior-level roles. It is equally suited for Data Analysts and Business Intelligence professionals looking to transition into product-focused analytics, where technical execution meets strategic business impact.

Learners should have a basic to intermediate understanding of SQL and familiarity with core data concepts such as tables, schemas, and data types. A foundational knowledge of product metrics like DAU, MAU, retention, and conversion rates is recommended, along with basic statistics including hypothesis testing and probability. Some exposure to Python or scripting is helpful but not required, as the program is structured to build proficiency progressively.

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.