HR Analytics with Generative AI Applications Certification Course

HR decisions increasingly require evidence-based insights to guide talent strategy, optimize costs, and align people practices with business goals. This HR Analytics course provides a structured, statistics-first pathway: from understanding data types and data quality, through descriptive and diagnostic analysis, to predictive and prescriptive techniques and the responsible use of generative AI. Across five days of hands-on practice, participants will learn how to clean, analyse, visualise, interpret, and communicate people data in ways that directly support better workforce and business decisions.

ILO-1: Explain the evolution of HR analytics, the four phases of HR analytics impact, and the four levels of analytics

ILO-2: Identify, calculate, and interpret key HR metrics across staffing, talent acquisition, total rewards, learning & development, performance management, and talent management.

ILO-3: Classify HR variables correctly, assess data quality, clean HR datasets, and apply basic statistical concepts including measures of central tendency, spread, and distribution.

ILO-4: Perform univariate and bivariate analyses using appropriate visualizations to explore patterns and relationships in HR data and apply basic statistical tests to answer diagnostic questions.
ILO-5: Build and interpret simple predictive models and convert predictions into prescriptive, prioritized HR actions such as targeted interventions or budget allocation.

ILO-6: Use generative AI tools to support HR analytics workflows while recognizing their limitations and ensuring responsible, human‑led decision-making.

Part 1: HR Analytics & Data Foundations

Module 1: What is HR Analytics & Why it Matters

• Role of HR analytics in workforce and business decisions
• Four phases / four levels of analytics impact
• Tool landscape: Excel, Power BI,Tableau, Python
• Introduction to GenAI and the rise of AI in people analytics

Module 2: Data types, sources, and quality

• Numerical vs categorical variables in HR
• Typical HR data sources
• Cleaning basics and documentation / data dictionary habits
• People data and cloud LLMs: Common pitfalls with sensitive people data

Module 3: Two lanes of analytics tooling

• Structured data: statistics and predictive models
• ● Text and comments: Natural Language Processing with AI

Module 4: Central tendency, spread, distributions

• Measures of center and spread
• AI-assisted charts generation
• Histograms and boxplots
• Use cases: pay, tenure, time-to-hire

Part 2: Descriptive HR Analytics & Core Metrics

Module 5: Staffing, recruitment, C&B

• Key metrics and cases
• Univariate and bivariate views
• Segments and cross-domain links
• AI-assisted executive summary and accuracy check

Module 6: L&D, performance, talent

• Key metrics and cases

• Fairness and calibration in performance signals

• AI-enabled ratings and review tools

Module 7: Visualization and narrative hygiene

• Chart choice and storyline for leaders

• AI-generated slide titles and talk tracks

• Challenging generated narratives without raw sensitive data

Part 3: Diagnostic HR Analytics & Statistical Tests

Module 8: Diagnostics and hypothesis testing basics

• Introduction to hypothesis test
• Relationship types: numeric–numeric, categorical–categorical, mixed
• Correlation and non-causation

Module 9: Diagnostic Case Studies Across HR Functions

• Voluntary turnover: associated factors
• Hiring sources and quality of hire
• Pay gap after grade controls
• Training and test-score change
• Metric definitions and defensible claims
• Confounding, omitted variables, proxy risk

Module 10: Unstructured text as HR data

• Comments and surveys: PII and aggregation
• Themes and sentiment as support to quantitative diagnosis

Part 4: Predictive & Prescriptive HR Analytics

Module 11: Introduction to Predictive Analytics in HR

• Linear and logistic regression
• Simple explainable models vs complex black boxes

Module 12: Time Series & Workforce Planning

• Trend, seasonality, stationarity
• Headcount demand forecast
• AI assisted forecasting

Module 13: From Predictive to Prescriptive

• From “What may happen” to “What should we do.”
• Generative brainstorming of interventions with AI
• Human-in-the-loop documentation for recommendations

Part 5: Closer look on GenAI Applications in HR Analytics

Module 14: Closer look on GenAI Applications in HR Analytics

• Generative AI: landscape and vocabulary
• How generative AI changes HR analytics workflows
• Applications in analysis, communication, and text
• Limitations, evaluation, and responsible use
• Data security best practices and vendor considerations

Students who have completed the assessments, and attended at least 80% of the course will be presented with a Certificate awarded by PolyU SPEED.

1.  Application form can be downloaded from this website.  Before completing the application form, please read carefully the "Guide for Applicants" on the form.

2.  Please submit the completed application form with a copy of your HKID card and settle the course fee by bank draft/ crossed cheque/ On-line Card Payment System for each course applied. For details of payment methods, please refer to Section 4 of the application form or this web page.

3.  Non-permanent residents of Hong Kong Special Administrative Region (HKSAR) holding

(i) work visa/ entry permit; OR

(ii) visa/ entry permit under the Immigration Arrangements for Non-local Graduates (IANG) (except those from Mainland, Macao and Taiwan); OR

(iii) dependent visa/ entry permit (except those from Mainland, Macao and Taiwan), issued by the Director of Immigration of HKSAR Government may enroll in short-term courses in SPEED without seeking prior approval from the Director of Immigration.

These applicants are required to attach a copy of their identity document(s) and the valid visa/ entry permit to their application.  On admission, they will be required to provide the original identity document(s) and visa/ entry permit for verification. Throughout the course of study, it is the responsibility of individual students to ensure that their identity documents and visas/ entry permits are valid. Other non-local applicants can neither be registered as students nor commence their studies on the short-term courses in SPEED.

4.  Applications should be submitted on or before the deadline for application via one of the following means -

(a) By post:

Send the following documents to
CPCE Academic Registry - Continuing Education
Room N301, 3/F, North Tower, PolyU West Kowloon Campus, 9 Hoi Ting Road, Yau Ma Tei, Kowloon, Hong Kong

• Completed application form
• A copy of your HKID Card and valid visa/ entry permit (if applicable)
• A bank draft/ crossed cheque/ payment confirmation page (for online payment)

(b) By e-mail (for application with payment settled via online system only):

Send the following documents by e-mail to ce.car@speed-polyu.edu.hk

• Completed application form
• A copy of your HKID Card and valid visa/ entry permit (if applicable)
• A bank draft/ crossed cheque/ payment confirmation page (for online payment)

Please choose either one of the following payment methods for course fee: 

(1)  Bank Draft/ Crossed Cheque

You could submit a bank draft/ crossed cheque (made payable to "The Hong Kong Polytechnic University") by post or by hand.  Please write your name, the course code and class code at the back of the bank draft/ crossed cheque.  Post-dated cheque will not be accepted.

(2)  On-line Card Payment System (by Visa/ Master/ China UnionPay card)
You could settle the course fee via On-line Card Payment System by Visa/ Master/ China UnionPay card.  For successful payment, please print the payment confirmation page for record and quote the invoice number (as shown on the payment confirmation page) on Section 4 of the application form. No separate hardcopy of the payment receipt will be issued for you.

According to the School's Refund Policy, course fees paid are normally not refundable, except cases of unsuccessful applications/ course cancellation.  Fees paid and places allocated are not transferrable.