Centram Centre for Applied Artificial Intelligence

AI ANALYRICS AND AUTOMATION COURSE for Human Resources Management

AI & ANALYTICS AND AUTOMATION COURSE FOR HUMAN RESOURCE MANAGEMENT

Artificial Intelligence , Analytics and Automation have revolutionized the world of work and no profession has been spared such that either you learn to leverage AI to do your work or it will replace you. The HR management is no exception. This AI and Machine learning course is offered to equip HR professionals to be up to date with the ever changing digital landscape and improve their productivity . .


Course title

AI, Analytics & Automation for Human Resources Management
Target audience

 HR professionals, HRBPs, Talent Acquisition Managers, People-analytics Practitioners.

Course objectives

  1. Explain core AI/ML, analytics, and automation concepts and their relevance to HR.
  2. Identify high-value HR use cases and design appropriate analytical/automation solutions.
  3.  Build and evaluate simple predictive models and dashboards for HR problems.
  4. Design governance, privacy, and fairness controls for HR AI/automation.
  5. Deliver a practical HR-focused automation or analytics solution as a capstone.

Learning outcomes

At then of the course participants should be able to:

  1. Map HR processes to AI/analytics/automation opportunities.
  2.  Implement basic predictive and descriptive analytics workflows (data prep → model → evaluation → deployment considerations).
  3. Create HR dashboards and automate a simple process with RPA or workflow tools.
  4. Conduct bias/fairness and privacy impact assessments for HR models.
  5.  Communicate findings and recommendations to HR and business stakeholders.

Course Duration and delivery modes

This course has 2 delivery options; 1, a 2-week intensive boot camp plus 2 weeks of online training OR 4 weeks of intensive boot camp training, whichever is convenient to the participants.

Course content

Introduction & course framing

  1.  Topics: Why AI/analytics/automation matter for HR; types of analytics (descriptive → prescriptive); technology landscape and vendor categories.
  2.  Activities: Course expectations, team formation for capstone, initial HR problem ideation.
  3.  Assignment: Short essay — top 3 HR challenges in your org and potential tech solutions.

HR data foundations & data governance

  1. Topics: HR data sources (ATS, HRIS, LMS, payroll, surveys, collaboration), data quality, taxonomy (roles/skills), data privacy/regulatory basics (GDPR/CCPA).
  2. Lab: Data inventory exercise; sample data cleaning in Excel or Python (pandas).
  3. Assignment: Create an HR data map for a sample org.

Descriptive & diagnostic analytics for HR

  1. Topics: Key HR metrics (time-to-fill, turnover, engagement), cohort analysis, segmentation, root-cause analysis.
  2.  Lab: Build an HR dashboard in Power BI/Tableau or Google Data Studio using sample HR dataset (IBM HR Analytics).
  3. Assignment: Submit dashboard + one-pager insights.

Predictive analytics fundamentals

  1. Topics: Supervised vs unsupervised learning; common algorithms (logistic regression, decision trees, random forest); evaluation metrics (accuracy, precision, recall, AUC); train/test split and cross-validation.
  2. Lab: Build an attrition prediction model (scikit-learn or no-code AutoML) on sample dataset.
  3. Assignment: Model report with performance and interpretation.

Explainability, fairness & ethics in HR AI

  1. Topics: Sources of bias, disparate impact, explainable AI techniques (SHAP/LIME), human-in-the-loop, auditability, appeals processes.
  2. Activity: Bias audit of the attrition/hiring model; propose mitigation strategies.
  3. Assignment: Fairness impact assessment for your model.

NLP in HR: resumes, job descriptions, sentiment

  1. Topics: Resume parsing, candidate matching, job description optimization, sentiment analysis for surveys/exit interviews, embedding.
  2.  Lab: Resume keyword extraction and candidate-job matching prototype (spaCy or Hugging Face + simple similarity metrics).
  3.  Assignment: Create a matching demo and short evaluation.

Recommender systems & personalization

  1. Topics: Recommendation approaches for learning, internal mobility, mentors; business rules vs collaborative filtering.
  2. Lab: Build a simple course-recommendation engine for employees.
  3. Assignment: Present recommendations and expected benefits.

Automation & RPA for HR operations

  1. Topics: RPA basics, workflow automation, integration with HRIS/ATS, attended vs unattended bots, governance and monitoring.
  2. Lab: Build an automated interview-scheduling workflow or benefits-reconciliation flow using UiPath/Power Automate/Make.com (no-code option).
  3. Assignment: Demo of automation + time-savings estimate.

Advanced analytics: workforce planning & scenario modeling

  1. Topics: Demand forecasting, capacity planning, scenario simulation, optimization basics.
  2. Lab: Simple scenario modeling for headcount planning (Excel or Python).
  3. Assignment: Scenario brief with recommended actions.

Deployment, change management & HR operating model

  1. Topics: Build vs buy decision-making, integration and APIs, change management for adoption, governance frameworks, monitoring & SLA for models/automation.
  2. Activity: Vendor selection case study and checklist creation.
  3. Assignment: Draft deployment plan for capstone project.

Privacy, compliance, and legal considerations

  1. Topics: Consent, data minimization, documentation, record-keeping, regulatory hot spots for automated decisions in hiring/performance, audit trails.
  2. Activity: Create privacy checklist and employee communication sample.

Capstone presentations & wrap-up

  1. Activity: Team capstone demos (analytics dashboard and/or automation prototype
  2. Business case, governance plan, ROI estimate).
  3.  Evaluation: Q&A with panel (instructors, HR leaders); course retrospective and next steps.

Capstone project (team-based)

  1. Objective: Deliver a deployable prototype addressing a real HR problem (e.g., attrition risk dashboard + manager workflow; resume-screening assistant with human-in-loop; automated onboarding chatbot + RPA for forms).
  2. Deliverables: prototype/demo, technical appendix, business case (benefits & risks), governance/fairness/privacy assessment, deployment plan and adoption strategy.
  3.  Presentation: 15–20-minute demo + 10-minute Q&A.

Tools & datasets

  1. Analytics/BI: Power BI, Tableau, Google Data Studio
  2. Python libraries: pandas, scikit-learn, matplotlib/seaborn, SHAP, spaCy, Hugging Face transformers
  3. No-code/low-code: UiPath (RPA), Microsoft Power Automate, Make.com, DataRobot or Google AutoML
  4. ATS/HRIS sandboxes: Workday demo environments or vendor trial accounts
  5. Sample datasets: IBM HR Analytics Attrition dataset (Kaggle), synthetic HRIS/ATS exports, anonymized org datasets if available

Readings & resources

 Blogs & tools: Visier and Eightfold whitepapers, Kaggle tutorials for HR analytics


 Book: “People Analytics” by Ben Waber; “Competing on Analytics” (selected chapters)

Papers/guides: “Guidelines for AI in HR” (SHRM/industry whitepapers), GDPR guidance on automated decisions

Online courses: Coursera/edX People Analytics specialization, Microsoft/UiPath RPA learning

Build successful websites
with Cloud Patterns.

✓ Easy to use

✓ Beautifully designed patterns

Schedule a demo