Statistics for AI, ML & Data Science
Descriptive, Inferential, Predictive & Prescriptive Statistics — the foundation every machine learning model depends on, taught properly for the first time for most participants.
A comprehensive foundation across all four branches of statistics.
Comprehensive program to obtain a solid foundation in descriptive, inferential, predictive & prescriptive statistical concepts, as well as data analytics and interpretation.
All fundamental concepts in statistical significance hypothesis testing — t-test, ANOVA, Regression, Chi Square — are covered, as well as predictive modeling using Regression, Classification, and more.
A must for anyone serious about a Data Science career.
For executives and researchers engaged in Data Analytics, Interpretation & Reporting for performance measurement, quality, innovation, and forecasting across industries. A must for those aspiring to work in Data Science, Machine Learning & AI, since it provides the robust foundation needed to build effective ML and AI models.
Statistics is the overlooked gem of the corporate world.
Statistics was long thought to belong to academia, where conclusions needed to meet precise thresholds — while in the corporate world, decisions were made on experience and intuition. Today, with the Big Data revolution, leading companies worldwide are employing advanced statistical methods to guide their growth. Correctly applying the right statistical methods and techniques is the key to unlocking the power hidden in data.
What this module covers.
- Principles of Statistical Data Analysis
- Understanding sources of data
- Types of Variables
- Measures of Central Tendency
- Measures of Dispersion
- Random Variables
- Sampling Techniques & Estimation
- Sampling Strategies & Experimental Designs
- Probability Distributions — Binomial, Normal, t, F, Chi Square
- Statistical Inferences using Hypothesis Testing
- Z-test, t-test, Chi Square tests
- Correlation and Regression
- ANOVA, MANOVA
- Foundations of Statistical Modeling
- Non-Parametric tests
Approved since 2014
30+ years practitioner-led
100% in-person
Max 8 learners
Our recommended learning structure for the Data Science Series.
| Level | Course | |
|---|---|---|
| Level O – Base | AI Literacy & Productivity Courses | Click here |
| Level I | Descriptive Statistics, Data Interpretation, KPI | Click here |
| Level II | KPI Development and Measurement | Click here |
| Level II | Power BI for Business Analytics | Click here |
| Level IV | Advanced Data Mining & Manipulation with Python | Click here |
| Level V | Inferential & Predictive Statistics for AI and Data Science | You are here |
| Level VI | Predictive Modeling & Evaluation with Machine Learning | Click here |
| Level VII | Advance Machine Learning and AI with Python | Click here |
| Level VIII | Applied Analytics | Click here |
| Level IX | Neural Network & Unsupervised Learning for Advanced AI | Click here |
More on Data Science and its applications.
- Python Course in Dubai — Why In-Person Training Still Wins in 2026
- AI vs Machine Learning: What Every Analyst Needs to Know
- Deep Learning Explained: Transforming Business Careers
- Why Learn Machine Learning — Career Growth in Dubai
- Business Analytics Career: Shaping Strategic Decisions
- 7 Essential Statistics Tips for Data Science Success
- Predictive Analytics: Driving Data-Backed Business Decisions
See the full article library on the blog for the complete list of related reading.
Common questions about this course.
Do I need a math background for this course?
No advanced math background is required — the course builds statistical concepts from the ground up, using business case studies rather than abstract theory.
Why does statistics matter if I'm going into Machine Learning, not research?
Every ML model rests on statistical assumptions. Understanding them is the difference between running a model and actually trusting — or correctly challenging — its output.
What software is used?
Participants bring their own laptop with Microsoft Excel installed; all topics are taught using relevant, industry-specific case studies and examples.
