HR Analytics and Talent Management Certificate

Open Enrollment, no application or enrollment requirements.

Program Overview

There are two courses in the certificate program:

• Workforce Analytic Essentials
• Applied Workforce Analytics & Organizational Impact

Participants can enroll in one or both courses, and will be eligible for a Credly badge for each – if they choose to enroll in both courses, they will be eligible to pursue the certificate.

Workforce Analytic Essentials

Workforce Analytics Essentials builds the core skills HR professionals need to turn workforce data into better talent decisions. Participants learn how HR analytics supports talent management and business performance, how to evaluate and manage HR data effectively, and how AI-enabled tools are shaping the future of people analytics. With over 70% of organizations using HR analytics and nearly 60% expected to leverage AI in HR decision-making within the next two years, this course focuses on practical application, ethical use, and strategic impact (Deloitte, Gartner). The program equips participants to confidently connect workforce insights to recruiting, development, performance, and succession strategies.

By the end of this course, participants will be able to:

1. Understand the role of HR analytics in driving talent and business outcomes
2. Evaluate HR data sources, metrics, and data quality for decision-making
3. Assess the opportunities and risks of AI in HR analytics
4. Analyze workforce data to identify trends and implications
5. Translate analytics insights into actionable talent strategies

Workforce Analytic Essentials – Course Outline

Week 1: Introduction to HR Analytics & Talent Management

• Explain what HR analytics is and describe its strategic role in talent management, supported by current industry statistics on analytics adoption.
• Articulate how HR analytics drives business value by connecting workforce insights to organizational performance outcomes.

Week 2: Data Collection & Management in HR

• Identify common sources of HR data and evaluate the strengths and limitations of each (HRIS, ATS, LMS, engagement surveys, performance systems).
• Consider various HR metrics and the purposes/best use cases for each.
• Apply principles of data quality—accuracy, consistency, and completeness—to improve workforce analytics reliability.

Week 3: HR Systems & AI Tools for HR Analytics

• Describe how AI methods such as natural language processing and machine learning are being applied in HR analytics, referencing current adoption statistics.
• Evaluate the opportunities and risks of using AI in HR, including bias, transparency, and compliance considerations.

Week 4: People Analytics and Talent Management Strategies

• Connect HR analytics insights to core talent management processes such as recruiting, development, performance, and succession planning.
• Analyze sample workforce datasets to identify trends and translate findings into talent strategy implications.
  

Applied Workforce Analytics & Organizational Impact

Applied Workforce Analytics & Organizational Impact advances HR professionals from understanding workforce data to using it to drive measurable business results. Participants learn to interpret descriptive and diagnostic analytics, apply predictive and prescriptive models to talent decisions, and translate insights into clear recommendations leaders can act on. As organizations using advanced people analytics are 2.5 times more likely to improve workforce outcomes and data-driven decision-making can improve productivity by 5–6%, this course focuses on practical application, ethical decision-making, and ROI-driven interventions (McKinsey, PwC). The program culminates in building an analytics-driven HR culture that embeds evidence-based decision-making into everyday talent practices.

By the end of this course, participants will be able to:

1. Distinguish and apply descriptive, diagnostic, predictive, and prescriptive analytics to real workforce challenges
2. Interpret HR dashboards and data to identify patterns, root causes, and talent risks
3. Use predictive and prescriptive analytics to inform hiring, retention, and performance decisions
4. Evaluate ethical considerations related to advanced analytics, including bias, privacy, and transparency
5. Develop an action plan to build data literacy and embed analytics into HR and leadership decision-making

Applied Workforce Analytics & Organizational Impact – Course Outline

Week 1: Descriptive & Diagnostic Workforce Analytics

• Distinguish between descriptive analytics (“what happened”) and diagnostic analytics (“why it happened”) and apply both to real workforce scenarios.
• Interpret dashboards and reports to identify patterns, correlations, and root causes in HR data.
• Use data storytelling techniques to translate descriptive and diagnostic insights into clear narratives for leaders.
• Explain how predictive analytics models forecast attrition, performance, readiness, and other talent outcomes.
• Identify variables that influence predictive models and assess how they can be used to improve workforce planning.
• Evaluate the ethical implications of predictive modeling in HR, including fairness, privacy, and algorithmic transparency.
• Define prescriptive analytics and differentiate it from predictive analytics in terms of decision-making value.
• Use prescriptive analytics techniques to recommend targeted actions for improving hiring, retention, or performance.
• Calculate the potential ROI of prescriptive interventions using scenario-based decision models.
• Identify the capabilities, behaviors, and structures needed for an HR team to operate as an analytics-driven function.
• Develop an action plan to embed evidence-based decision-making into daily HR practices and talent conversations.
• Assess organizational readiness for analytics adoption and propose steps to strengthen data literacy and leadership buy-in.

Week 2: Predictive Analytics in Human Capital Management

• Explain how predictive analytics models forecast attrition, performance, readiness, and other talent outcomes.
• Identify variables that influence predictive models and assess how they can be used to improve workforce planning.
• Evaluate the ethical implications of predictive modeling in HR, including fairness, privacy, and algorithmic transparency.

Week 3: Prescriptive Analytics & Decision-Making

• Define prescriptive analytics and differentiate it from predictive analytics in terms of decision-making value.
• Use prescriptive analytics techniques to recommend targeted actions for improving hiring, retention, or performance.
• Calculate the potential ROI of prescriptive interventions using scenario-based decision models.

Week 4: Building an Analytics-Driven HR Culture

• Identify the capabilities, behaviors, and structures needed for an HR team to operate as an analytics-driven function.
• Develop an action plan to embed evidence-based decision-making into daily HR practices and talent conversations.
• Assess organizational readiness for analytics adoption and propose steps to strengthen data literacy and leadership buy-in.

 What You'll Learn

By completing the HR Analytics and Talent Management Certificate, participants will be able to: Use workforce analytics to drive better talent decisions and support organizational performance. Evaluate HR data sources, metrics, and data quality to ensure reliable, decision-ready insights. Apply descriptive, predictive, and prescriptive analytics to hiring, performance, retention, and succession challenges. Interpret HR dashboards and workforce trends to identify risks, opportunities, and root causes. Leverage AI in HR analytics responsibly, addressing bias, privacy, and transparency concerns. Translate analytics insights into clear, actionable recommendations leaders can implement. Build data literacy and embed analytics into HR practice, strengthening evidence-based decision-making across the organization.