HUMAN RESOURCES ANALYTICS

Kick off the course by meeting your instructor, getting an overview of the course structure, and understanding what your assignments and final project will entail. 

• Meet your instructor
• Course structure 
• Assignments
• Final project overview

Explore the foundations of HR Analytics, from defining its scope to understanding how AI is transforming the space. Learn how analytics has driven tangible business outcomes in organizations, and begin to identify opportunities to apply similar insights in your workplace.

• Defining HR Analytics 
• Data-driven HR 
• Key principles and roles 
• The HR analytics lifecycle
• AI tools transforming HR
• Case Studies: 5 impactful people analytics projects
• Discussion: What’s the most data-informed HR decision you’ve seen at work—and what made it effective (or not)?

Examine the emerging trends shaping HR Analytics today. Understand which tools and strategies are gaining traction, how AI is enabling smarter workforce decisions, and why analytic skills and critical thinking remain vital.

• Emerging priorities in HR analytics 
• Popular tools: Tableau, Power BI, Plotly
• Skill gaps & reskilling opportunities 
• Analytic skills in an AI-driven world

Assignment #1: Write a 1-page reflection: What is People Analytics, and how could it support a challenge or decision in your current, past, or hypothetical role? Use this as a foundation for your capstone project.

Dive into HR metrics across the employee lifecycle. Learn to establish KPIs that matter, align analytics with business goals, and identify the metrics that truly drive performance in different teams.

• Aligning analytics with business goals
• Critical focus areas
• Selecting metrics for different teams
• Employee lifecycle review

Assignment #2: Identify the employee lifecycle stage for your challenge, define success KPIs, and note key leaders or partners for collaboration.

Understand the legal, compliance, and ethical landscape in HR Analytics. Learn how to recognize and mitigate bias, protect employee data, and build trust while using analytics responsibly.

• Data privacy and protection 
• Data security & HR’s responsibility
• Recognising and mitigating bias
• Case studies: Hirescore Lawsuit & “creepy analytics” examples

Assignment #3: Draft your business case/analytics proposal using the provided template, covering problem statement, business value, ethical/compliance considerations, risks, and mitigations.

Gain practical skills in gathering, evaluating, and preparing HR data. Learn how to create a single source of truth, ensure data quality, and leverage AI-assisted tools for faster and more accurate analysis.

• Common HR data sources 
• Business context & process knowledge
• Data cleaning, merging, anonymizing, & validation
• Discussion: Where have you seen data mislead business decisions?
• Workshop: Work in groups to clean two messy HR data files with duplicates, missing values, and inconsistent formats.

Assignment #4: Select a dataset to use for your analysis. Document in your proposal template where you’d source this data in your current organisation.

Build a statistical and analytical mindset for HR. Learn to frame questions as hypotheses, apply descriptive and inferential statistics, and visualize trends to uncover actionable people insights.

• Framing questions as hypotheses
• Exploratory and inferential statistics 
• Descriptive statistics in HR
• Visualizations used in EDA
• Segmentation strategies
• ChatGPT interpreting statistical results

Assignment #5: Develop testable hypotheses for your dataset and document potential segmentation strategies (tenure, geography, function, etc.).

Step into the role of a data detective. Apply Exploratory Data Analysis with AI support, uncover insights, and practice interpreting and communicating results effectively.

• Guided Case Study: Identifying causes of high attrition in a department over the past year

Assignment #6: Apply EDA to your dataset. Submit 2–3 charts showing trends or confirming relationships and provide your interpretation.

Learn when and how to apply predictive modeling in HR. Explore practical use cases and build a simple predictive model to forecast outcomes such as attrition or promotion likelihood.

• When prediction is useful
• Use cases: Predicting turnover risk, promotion likelihood, hiring outcomes
• Past vs. future analytics
• Workshop: Build a simple predictive model

Assignment #7: Create a modeling blueprint outlining your outcome, features (inputs), method, evaluation, and ethics check.

Assignment #8 (optional): For bonus points: Use ChatGPT to forecast attrition with the IBM dataset. Submit a 1-page summary of predictive attributes.

See how predictive modeling and employee listening drive real organizational impact. Learn what works, what’s messy, and what truly improves retention.

• The importance of employee listening
• Case Study: Lincoln Financial: Employee listening as a driver of retention strategy
• Guest Speaker: Q&A Session

Assignment #9: Identify at least 3 variables you could capture via employee listening to enhance your predictive model.

Turn analysis into insight with compelling data visualization. Learn best practices for dashboards, reports, and storytelling to ensure HR and business audiences understand and act on your insights.

• Principles of good data storytelling
• Choosing visuals for stakeholders
• Dashboard vs. ad-hoc insight delivery
• Demo: Tableau/ Plotly

Final Project Options:

1. Presentation (±5 slides) with EDA findings, recommendations, and KPIs.
2. Dashboard/prototype visualizing key insights.

Learn how to move from insights to sustainable, repeatable impact. Prototype dashboards while addressing governance, usability, and decision-making integration.

• When dashboards add value 
• Dashboard prototyping: Plotly, Streamlit, Tableau
• Governance

Present your final project like a people analytics consultant. Receive structured peer feedback, reflect on key takeaways, and translate your work into a portfolio-ready piece.

• Volunteer final project presentations
• Structured peer review
• Reflection on key takeaways 
• Portfolio piece
• Final career tips & Q&A