Open enrolment
AI+ Data Certification Program
Master Data Science, Machine Learning, and Generative AI for Data-Driven Decision-Making
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.
Course fee
$4,950
per participant, incl. exam voucher where applicable
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
Customisation & Delivery Options
Ideal for:
Public enrolment
Corporate teams (customisable schedule and labs)
Industry-specific adaptations (finance, government, healthcare)
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
Curriculum
The 5-day outline.
Every day combines instruction with hands-on labs. You leave having done it, not just heard it.
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
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
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
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
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
More dates
Same course. Other dates & cities.
Let’s put AI to work in your institution.
One call, no obligation. In 30 minutes you will know where AI fits in your organisation, what it takes, and where to start.
Trusted by governments and enterprises across the GCC.