It's not AI that's going to knock you out, it's those who know how to use it!
Since 2023, breakthroughs in generative AI and large models are opening a new phase of AI native, and the application of AI in the field of human resources has become more extensive and deeper, covering everything from human resource planning to recruitment, training, compensation performance, and employee experience. How to use AI algorithms to improve the efficiency of human resources and help management decision-making is a skill that HR and managers urgently need to understand and improveTraditional HR data analysisIt is more about using some simple statistical descriptions and calculations, and using some visual means, such as excel charts, or visual tools such as Power BI and Tableau to corroborate and explore the human resources business. Traditional data analysis generally has several limitations, such as dimensional and information limitations, nonlinear understanding, data paradoxes, and correlation interpretation. Data miningIt is the process of searching for information hidden in it through algorithms from a large amount of data, including multi-dimensional quantitative analysis, model algorithms to provide insight conclusions and analysis, which can further break through the limitations of traditional analysis and previous experience, expand our perspective on the problem, and make the story told by the data more convincing. 
In order to help HR understand and master digital skills in the AI era, CIIC launched the "Human Resources Data Analysis and Mining Course", which empowers HR practitioners to comprehensively upgrade their digital skills and apply AI algorithms to improve the efficiency of human resource analysis and decision-making.
Course content. 6 themes, 14 lessons, 5 hours
Course outlineTopic 1: Why should data mining be introduced in human resource management
What is Data Mining.
Limitations of traditional data analysis.
AI Algorithms and Human Resource Applications.
Topic 2:Application-Turnover** with HR application of classification algorithms
Overview of machine learning.
Scenario analysis: resignation**.
Human resources application of classification algorithms.
Theme 3:Application-Employee Compliance Risk** with Clustering Algorithms for HR Applications
Scenario analysis: Employee compliance risk**.
Human resources applications of clustering algorithms
Topic 4:Application-Sales** and Optimal Allocation of Human Resources, Regression Algorithms and Neural Network Applications
Scenario analysis: sales** and optimal allocation of human resources.
Regression algorithms and neural networks for human resource applications.
Topic 5:Applications - Sentiment Analysis & Employee Satisfaction, Natural Language Processing & Generative AI Applications
Scenario Analysis: Sentiment Analysis and Employee Satisfaction.
HR applications for chatbots and generative AI.
Topic 6:Human resource data analysis and mining project implementation and capacity building path
Characteristics of the HR AI project.
Enterprise digital capacity building path (organizational talent structure).
Apply the objectHR practitioners with non-technical backgrounds.
Practitioner of human resources digitalization and data analysis.
Mode of delivery**Learning (CIIC Online Platform).Course instructor
Gao Yuncheng is the Director of Digital Business Consulting of CIIC.
With more than 10 years of experience in database and BI development, focusing on data analysis and application business in the field of human resources, he has led the construction of human resources data warehouse and analysis platform in food retail, medicine and manufacturing.
He was invited by Microsoft Research Asia-INESA Artificial Intelligence Innovation Institute as a lecturer for HR AI algorithm application.
Course Highlights:
In-depth HR application scenarios, detailed AI analysis of four types of casesThe course delves into HR data analysis scenarios, such as turnover**, compliance risk**, human capital measurement, labor market**, research and analysis, etc., and disassembles the application of AI algorithms in detail through practical cases.
The course is HR-friendly with non-technical backgrounds, and AI skills are mastered through tool drillsFor HR with non-technical backgroundsThere will be no teaching in the course explanation, and there will be no derivation of mathematical formulas, but only some simple explanations of the principles of algorithms in some key scenarios, so that everyone can understand the business value of data mining and think about the practical application of AI algorithms.
For Digital & Data Analytics practitionersIn order to have a more intuitive experience, the course will show the implementation process of data mining, and will also provide key scenarios in the courseware to facilitate the learning and application of AI algorithms.
Starting with AI projects, we will promote the digital capacity building of enterprisesThe course not only includes the professional content of data mining, but also teaches the characteristics of human resources AI and digital projects, and explains in detail the path of enterprise digital capacity building from the perspective of organizational talent structure, which also has great reference value for the managers of HR teams.