The programme will be conducted in an interactive seminar style, supported by relevant case studies, group breakout sessions and practice cases. You will have hands-on experience applying cutting-edge analytics tools to solve business-related issues.

Comprising two modules of 3 days each, the programme curriculum is designed for you to progress sequentially. We may assume that you are familiar with the concepts covered in Module 1 when you attend Module 2.

Module 1:

Topics Covered
Day 1:
Introduction to Python and Generative AI for Data Analytics
  • Introduction to Python Programming
    • Setting up Python environment
    • Python basics: variables, operators, loops, conditionals, functions, objects
  • Introduction to Generative AI Concepts
    • Setting up of Generative AI environment
    • Overview of Generative AI basics: code explanation, coding assistant
  • Hands-on Session: Python Basics with Generative AI
    • Writing your first Python script with Generative AI coding assistant
    • Building your first customised formula
Day 2:
Data Wrangling and Cleaning Data 
  • Data Collection, Cleaning and Insights
    • Working with typical datasets (finance, insurance, customer, health etc.)
    • Techniques for cleaning and transforming data: handling missing values, dealing with outliers, and ensuring data consistency
    • Working with various input sources (e.g., databases) and output requirements.
  • Hands-on Session: Data Wrangling with Pandas
    • Using Python's Pandas library to clean and prepare data for analysis with Generative AI coding assistant. 
Day 3:
Data Visualisation for Analytics
  • Introduction to Data Visualisation
    • The role of data visualisation in making sense of data
    • Common visualisation tools
    • Use of Generative AI in generating visualisations and providing insights.
  • Hands-on Session: Creating Visualisations and Insights
    • Building insightful visualisations to identify key patterns for different types of data
  • Hands-on Session: Prompt Engineering Techniques
  • Presentation and Assessment

Module 2:

Topics Covered
Day 1:

Introduction to Text Analytics and Statistical Modelling

  • Introduction to Python Programming
    • Overview of text analytics – Word Cloud, Sentiment Analysis
    • Enhancing text analytics with Generative AI – text summary, translation
  • Statistical Modelling
    • Apply statistical tests (A/B testing, t-tests)
    • Apply Multivariate Linear Regression for predictive analysis
  • Hands-on Session: Text Analytics and Statistical Modelling
Day 2:
Time Series Analysis, Clustering 
  • Time Series Analysis
    • Data preparation for time series data
    • Perform time series decomposition, exponential smoothing and fit ARIMA models
  • Hands-on Session: Time Series Analysis
    • Apply time series analysis to a practical dataset.
  • Clustering
    • Data Preparation for K-Means clustering
    • Perform K-Means clustering and post-clustering analysis
  • Hands-on Session: Apply clustering and post-clustering analysis
Day 3:
Assignment Presentation, AI Governance 
  • Data and AI Governance
    • Overview of principles and frameworks for AI governance
  • Presentation and Assessment


Laptop required: Please note that you are required to bring your own laptop or MacBook with Microsoft Excel for this hands-on course.

For more information on the intake dates, trainers and programme fee, please click here.