Coursera

Product Analytics for Prioritization & Data-Driven Decisions

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Coursera

Product Analytics for Prioritization & Data-Driven Decisions

包含在 Coursera Plus

深入了解一个主题并学习基础知识。
中级 等级

推荐体验

1 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度
深入了解一个主题并学习基础知识。
中级 等级

推荐体验

1 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • 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.

要了解的详细信息

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最近已更新!

March 2026

授课语言:英语(English)

了解顶级公司的员工如何掌握热门技能

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Product Analytics Unlocked: Metrics to Meaningful Insight 专项课程 专项课程的一部分
在注册此课程时,您还会同时注册此专项课程。
  • 向行业专家学习新概念
  • 获得对主题或工具的基础理解
  • 通过实践项目培养工作相关技能
  • 获得可共享的职业证书

该课程共有13个模块

You will systematically analyze user interfaces to establish comprehensive tracking frameworks that capture meaningful behavioral data.

涵盖的内容

4个视频2篇阅读材料2个作业

You will systematically investigate conversion funnel performance drops through segmentation analysis and root cause methodology to identify specific optimization opportunities.

涵盖的内容

2个视频2篇阅读材料3个作业

You will learn the RICE prioritization framework to systematically evaluate competing product features, eliminating subjective bias through structured impact, confidence, and effort assessments.

涵盖的内容

2个视频1篇阅读材料2个作业

You will learn the creation of executive-level decision memos that transform analytical insights into actionable strategic recommendations with comprehensive risk assessment.

涵盖的内容

3个视频1篇阅读材料2个作业

You will systematically examine customer churn data using Pareto analysis and cohort segmentation to identify underlying behavioral patterns and system-level friction points that drive user attrition.

涵盖的内容

2个视频2篇阅读材料2个作业

You will develop comprehensive success criteria frameworks with specific KPIs, counter-metrics, and target thresholds for onboarding features that prevent unintended consequences and ensure holistic performance measurement.

涵盖的内容

1个视频2篇阅读材料3个作业

You will learn the systematic application of industry funnel benchmarks to create predictive forecasting models that estimate Monthly Active User growth from referral initiatives.

涵盖的内容

3个视频1篇阅读材料2个作业

You will learn the systematic creation of prioritization matrices using collaborative frameworks to guide strategic resource allocation decisions across competing product initiatives.

涵盖的内容

2个视频1篇阅读材料3个作业

You will learn the systematic six-step framework that transforms ad-hoc product investigations into rigorous, reproducible analyses that stakeholders can trust and validate.

涵盖的内容

2个视频2篇阅读材料1个作业

You will learn the critical evaluation skills needed to select optimal classification models for product analytics scenarios by systematically comparing decision trees and logistic regression based on interpretability, data characteristics, and preprocessing requirements.

涵盖的内容

2个视频2篇阅读材料3个作业

You will learn the fundamentals of transforming technical analytics into compelling business narratives using proven storytelling structures.

涵盖的内容

3个视频1篇阅读材料1个作业

You will develop advanced skills in connecting quantitative user segmentation data to qualitative personas for strategic decision-making and stakeholder communication.

涵盖的内容

3个视频1篇阅读材料3个作业

You will create a comprehensive product analytics report that combines user funnel analysis, churn investigation, and strategic prioritization recommendations. This integrated project demonstrates proficiency in translating raw data into actionable business insights that drive product decisions.

涵盖的内容

3篇阅读材料1个作业

获得职业证书

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位教师

Professionals from the Industry
247 门课程 36,457 名学生

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常见问题

¹ 本课程的部分作业采用 AI 评分。对于这些作业,将根据 Coursera 隐私声明使用您的数据。