An Interactive 5-Day Training Course

An Interactive 5-Day Training Course

AI+ Data Certification Program

AI+ Data Certification Program

AI+ Data Certification Program

Master Data Science, Machine Learning, and Generative AI for Data-Driven Decision-Making

Master Data Science, Machine Learning, and Generative AI for Data-Driven Decision-Making

Description

14th - 18th Jun 2026

London, UK

$4,950

Welcome to our AI+ Data Certification Program — a comprehensive, hands-on course covering data science foundations, statistics, programming, machine learning, and Generative AI for data professionals.

Includes:
Hands-on Labs, Real-World Datasets, Capstone Project & AI CERTs Certification Preparation.

Certification Target

This course prepares learners for the AI CERTs™ AI+ Data certification — a globally recognised credential validating proficiency in data science, machine learning, and Generative AI for data-driven applications. 

Course Overview

The AI+ Data certification equips professionals with vital skills for data science. It covers key concepts like Data Science Foundations, Statistics, Programming, and Data Wrangling. Participants delve into advanced topics such as Generative AI and Machine Learning, preparing them for complex data challenges. 

The program includes a hands-on capstone project focusing on Employee Attrition Prediction. Emphasis is placed on Data-Driven Decision-Making and Data Storytelling for actionable insights. 

Instructor

We bring you top-tier global AI experts as instructors — professionals who combine: 


  • Strong academic backgrounds from leading international universities 


  • Extensive industry experience across multiple sectors 


  • Deep specialisation in AI, machine learning, and data science 


  • Multilingual communication abilities, ensuring clarity for diverse audiences 

Who Should Attend?

  • Aspiring data scientists and analysts 


  • Business analysts transitioning to data science 


  • Software developers and engineers 


  • IT and digital professionals working with data 


  • Anyone seeking a structured data science certification 

Prerequisites

  • Basic knowledge of computer science and statistics (beneficial but not mandatory) 


  • Keen interest in data analysis 


  • Willingness to learn programming languages such as Python and R 

Learning Objectives

Upon completion, participants will be able to: 


  • Apply the complete data science lifecycle from problem framing to deployment 


  • Perform statistical analysis, hypothesis testing, and probability modelling 


  • Build supervised and unsupervised machine learning models 


  • Create compelling data visualisations and storytelling narratives 


  • Apply Generative AI techniques for data synthesis and augmentation 

Organisational Impact

Investing in data science capabilities creates measurable organisational value: 

• Enable evidence-based decision-making across all business functions 

• Unlock predictive insights from existing data assets 

• Automate data analysis workflows and reduce manual processing 

• Build internal data science competency and reduce vendor dependency 

• Accelerate AI and analytics initiatives with skilled practitioners 

AI+ Data Certification Program

Course Outline:

Day 1

Foundations of Data Science & Statistics
  • What is Data Science: methodologies, tools, and applications 


  • Data Science Life Cycle: problem framing, data prep, EDA, modelling, evaluation, deployment 


  • Descriptive and inferential statistics, probability distributions 


  • Lab: Statistical analysis and hypothesis testing with Python 

Day 1

Foundations of Data Science & Statistics
  • What is Data Science: methodologies, tools, and applications 


  • Data Science Life Cycle: problem framing, data prep, EDA, modelling, evaluation, deployment 


  • Descriptive and inferential statistics, probability distributions 


  • Lab: Statistical analysis and hypothesis testing with Python 

Day 1

Foundations of Data Science & Statistics
  • What is Data Science: methodologies, tools, and applications 


  • Data Science Life Cycle: problem framing, data prep, EDA, modelling, evaluation, deployment 


  • Descriptive and inferential statistics, probability distributions 


  • Lab: Statistical analysis and hypothesis testing with Python 

Day 2

Data Sources, Programming & Feature Engineering
  • Structured, semi-structured, and unstructured data types 


  • Python and R for data science: NumPy, Pandas, Matplotlib, dplyr, ggplot2 


  • Data wrangling: imputation, outlier handling, normalisation, transformation 


  • Lab: Data manipulation and visualisation with Python and R 

Day 2

Data Sources, Programming & Feature Engineering
  • Structured, semi-structured, and unstructured data types 


  • Python and R for data science: NumPy, Pandas, Matplotlib, dplyr, ggplot2 


  • Data wrangling: imputation, outlier handling, normalisation, transformation 


  • Lab: Data manipulation and visualisation with Python and R 

Day 2

Data Sources, Programming & Feature Engineering
  • Structured, semi-structured, and unstructured data types 


  • Python and R for data science: NumPy, Pandas, Matplotlib, dplyr, ggplot2 


  • Data wrangling: imputation, outlier handling, normalisation, transformation 


  • Lab: Data manipulation and visualisation with Python and R 

Day 3

Exploratory Data Analysis & Visualisation
  • EDA techniques: summary statistics, distributions, correlations 


  • Visualisation types: histograms, scatter plots, box plots, heatmaps 


  • Choosing the right visualisation for different data types 


  • Lab: Creating visualisations using Matplotlib, Seaborn, and ggplot2 

Day 3

Exploratory Data Analysis & Visualisation
  • EDA techniques: summary statistics, distributions, correlations 


  • Visualisation types: histograms, scatter plots, box plots, heatmaps 


  • Choosing the right visualisation for different data types 


  • Lab: Creating visualisations using Matplotlib, Seaborn, and ggplot2 

Day 3

Exploratory Data Analysis & Visualisation
  • EDA techniques: summary statistics, distributions, correlations 


  • Visualisation types: histograms, scatter plots, box plots, heatmaps 


  • Choosing the right visualisation for different data types 


  • Lab: Creating visualisations using Matplotlib, Seaborn, and ggplot2 

Day 4

Machine Learning: Supervised & Unsupervised
  • Supervised learning: linear regression, logistic regression, decision trees, SVM, kNN, random forests 


  • Unsupervised learning: k-means clustering, hierarchical clustering 


  • Ensemble methods: bagging, boosting, XGBoost, stacking 


  • Lab: Building and evaluating ML models end-to-end 

Day 4

Machine Learning: Supervised & Unsupervised
  • Supervised learning: linear regression, logistic regression, decision trees, SVM, kNN, random forests 


  • Unsupervised learning: k-means clustering, hierarchical clustering 


  • Ensemble methods: bagging, boosting, XGBoost, stacking 


  • Lab: Building and evaluating ML models end-to-end 

Day 4

Machine Learning: Supervised & Unsupervised
  • Supervised learning: linear regression, logistic regression, decision trees, SVM, kNN, random forests 


  • Unsupervised learning: k-means clustering, hierarchical clustering 


  • Ensemble methods: bagging, boosting, XGBoost, stacking 


  • Lab: Building and evaluating ML models end-to-end 

Day 5

Generative AI for Data & Capstone Project
  • Introduction to Generative AI: autoencoders, GANs, VAEs 


  • Applications: data synthesis, augmentation, anomaly detection 


  • Data-driven decision-making and storytelling 


  • Capstone: Employee attrition prediction — complete ML pipeline from data prep to deployment 

Day 5

Generative AI for Data & Capstone Project
  • Introduction to Generative AI: autoencoders, GANs, VAEs 


  • Applications: data synthesis, augmentation, anomaly detection 


  • Data-driven decision-making and storytelling 


  • Capstone: Employee attrition prediction — complete ML pipeline from data prep to deployment 

Day 5

Generative AI for Data & Capstone Project
  • Introduction to Generative AI: autoencoders, GANs, VAEs 


  • Applications: data synthesis, augmentation, anomaly detection 


  • Data-driven decision-making and storytelling 


  • Capstone: Employee attrition prediction — complete ML pipeline from data prep to deployment 

Exam Information

This course prepares participants for the AI CERTs™ AI+ Data certification exam. The exam validates proficiency in data science foundations, statistical analysis, machine learning, and Generative AI applications.

What's Included

  • Expert-led instruction 


  • Hands-on labs with real-world datasets 


  • Capstone project: Employee Attrition Prediction 


  • Digital courseware and resources 


  • AI CERTs™ certification exam voucher 

Customisation & Delivery Options

Ideal for: 


  • Public enrolment 

  • Corporate teams (customisable schedule and labs) 

  • Industry-specific adaptations (finance, government, healthcare) 

Let’s supercharge your Emerging Tech Advantage.

Trusted by governments and enterprises across the Middle East to deliver multi-day AI training and strategic enablement programs.

Let’s supercharge your Emerging Tech Advantage.

Trusted by governments and enterprises across the Middle East to deliver multi-day AI training and strategic enablement programs.

Let’s supercharge your Emerging Tech Advantage.

Trusted by governments and enterprises across the Middle East to deliver multi-day AI training and strategic enablement programs.