Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

Today’s HR teams need strong data literacy and the ability to use AI safely, ethically, and effectively. In this course, you’ll learn the full HR analytics lifecycle—from preparing datasets and building dashboards to communicating insights and monitoring outcomes—augmented with responsible, human‑validated AI. Using synthetic HR datasets (never real employee data) and tool‑agnostic conceptual AI demonstrations, you’ll practice analyzing trends, drafting stakeholder‑specific insights, identifying risks, and validating AI‑generated outputs. You’ll explore data storytelling, ethical phrasing, bias mitigation, and AI pitfalls—while completing hands‑on activities and role‑based exercises aligned to modern HR analytics work.

Syllabus

Analytics Overview: Finding and Preparing Your Data

This module introduces the foundations of HR analytics, including the differences between data, metrics, and analytics, and why clearly defined goals and success metrics matter. Learners explore common HR metrics and best practices for capturing and preparing data for analysis. The module also introduces safe data practices, including privacy and PII considerations, and shows where AI can assist with early‑stage data preparation tasks while reinforcing the importance of human oversight.

Building Dashboards and Analyzing Your Data

In this module, learners focus on turning prepared data into meaningful insights through dashboards and analysis. Topics include designing effective HR analytics dashboards, analyzing data at both macro and micro levels, and asking the right investigative questions. Learners also examine how AI can assist with dashboard personalization, trend interpretation, and summarizing employee feedback, while practicing how to validate AI‑generated insights and avoid over‑reliance.

Storytelling With Data and Inspiring Action

This module explores how to transform analytics insights into compelling stories that resonate with different audiences and drive action. Learners examine the principles of data storytelling, visual design considerations, and common pitfalls in analytics presentations. The module also covers how to craft effective calls to action and apply ethical and bias‑aware language. Throughout, learners practice using AI to draft narratives and recommendations, then refine them using human judgment.

Maximizing Impact: Implementing and Monitoring Analytics Results for Success

The final module focuses on turning insights and calls to action into real‑world impact. Learners explore how to create implementation plans, define success metrics, and monitor results over time. The module highlights how AI can support ongoing performance summaries and trend detection, while emphasizing validation, human override, and escalation criteria. By the end, learners understand how to track outcomes, communicate results, and continuously improve analytics initiatives.