HR Analytics using Python


  • In the contemporary business environment, data science, Python, and AI are among the most valuable competencies for HR professionals to possess. The following abilities can assist HR professionals:

    • Gain insights into the workforce, including employee engagement, performance, retention, and development, through the analysis of large and complex data sets.
    • Leverage predictive and prescriptive analytics to forecast forthcoming talent requirements, detect potential hazards, and enhance human resources strategies and policies.
    • Implement machine learning, and artificial intelligence methodologies to automate and optimize a range of human resources responsibilities, including employee feedback, learning and development, talent acquisition, and career coaching.
    • Utilize data visualization and storytelling tools, including reports, charts, graphs, and dashboards, to effectively and persuasively convey information.
    • Collaborate with other organizational functions and stakeholders to leverage AI and data for improved business outcomes and decision making.

     

Session 1: Introduction to HR Analytics with Python

 

  • Session Duration: 3 hours
  • Introduction of data science and AI in HR profession
  • Overview and understanding of HR Analytics and its relevance in HR decision-making.
  • Why python is important to learn AI based HR analytics?
  • A brief introduction to Python and its role in HR data analysis.
  • Github account, Kaggle account, and Installations and setting up the Python environment in VS Code.

 

Session 2: Data Science Essentials with Python

 

  • Session Duration: 3 hours
  • Conda environment for data science
  • Variables and data types in python in the context of HR
  • Understanding the data science workflow in HR Analytics.
  • Methods of data collection, storage, and retrieval using Python libraries.
  • Exploring HR data using modules and libraries in python e.g. Pandas and NumPy.

 

Session 3: Basic Statistics application in Python for HR Analytics

  • Session Duration: 3 hours
  • Preprocessing HR data using Python.
  • Understanding and using Python for statistical analysis in HR.
  • Exploring HR functions like recruitment, onboarding, and performance management.
  • How Python can be used to optimize decision making in HR processes.
  • Critical analysis of HR data to improve HR functions.
  • Big data in AI
  • Data cleaning techniques with Python for HR datasets.
  • Descriptive statistics using Python.
  • Regression analysis basics in HR Analytics.
  • Handling missing data and outliers.

 

Session 4: Exploratory Data Analysis (EDA) in HR Analytics with Python

  • Session Duration: 3 hours
  • Introduction to EDA with basic terminologies in Python.
  • Data visualization for insights using libraries like Matplotlib and Seaborn.
  • Pattern recognition and trends in HR data.

 

Session 5: Building Interactive HR Analytics Web Apps with Streamlight

  • Session Duration: 3 hours
  • Introduction to the Streamlit library for creating web applications.
  • Hands-on experience in building a basic HR analytics web application using Streamlit in VS Code.

 

Note: Participants will learn how to use Streamlit within VS-Code to create interactive web applications that can visualize and share HR analytics insights with stakeholders. This feature allows participants to gain practical experience in deploying data-driven HR solutions.

 

Session 6: Machine Learning for HR Analytics using Python

  • Session Duration: 3 hours
  • Introduction to machine learning and its applications in HR.
  • Developing predictive models for HR outcomes using Python libraries
  • Model evaluation and interpretation for HR decisions.
  • Final project

 

Mr. Kamran Hameed

PhD Management Scholar (University of Management & Technology), and teaching BBA and MBA level courses. Teaching courses include Strategic Management, Professional Practices, Managing Knowledge in Organizations and Human Resource Management. Research areas include strategic management, entrepreneurial ecosystems and innovation Management, Sustainable Environment, and Organization behavior. Provided consultancies and training on survey research, data analysis, and monitoring and evaluation of government projects.

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