Inferential Statistics Training – KHDA Approved Program
Master inferential statistics fundamentals from hypothesis testing and confidence intervals to regression analysis and statistical inference. With our industry-aligned Inferential Statistics training program, you can learn to analyze sample data, draw valid conclusions about populations, and support data-driven decision-making across business, technology, healthcare, and public-sector environments.
Course Duration: 15–20 Weeks | 60 Hours
Delivery Mode:Classroom / Online / Blended
Inferential Statistics Training – Program Overview
Inferential Statistics Training in Dubai – Who Is It For?
- Students, graduates, and data enthusiasts building inferential statistics skills for analytics and research roles
- Business and market professionals using statistical inference for forecasting and decision-making
- Technical, operational, and data professionals applying inferential statistics for analysis and optimization
- Healthcare and public sector professionals using statistical methods for research and policy decisions
Skills You’ll Develop with Our Inferential Statistics Course in Dubai
Gain in-demand analytical and professional skills that prepare you to apply inferential statistics across multiple industries.
Statistical Inference
Learn how to draw reliable conclusions about populations using sample data.
Develop the ability to frame, test, and interpret statistical hypotheses.
Estimation Techniques
Understand confidence intervals and parameter estimation under uncertainty.
Regression Analysis
Analyze relationships between variables to support prediction and decision-making.
Software, Tools, Languages & Frameworks
Inferential Statistics Training Course Curriculum
Module 1: Foundations of Statistical Inference
- Overview of inferential statistics
- Population vs sample concepts
- Role of probability in inference
- Sampling methods and bias
Module 2: Estimation and Confidence Intervals
- Point estimation
- Interval estimation
- Confidence levels and interpretation
- Margin of error
Module 3: Hypothesis Testing
- Null and alternative hypotheses
- One-tailed and two-tailed tests
- Type I and Type II errors
- p-values and decision rules
Module 4: Regression and Correlation Analysis
- Correlation concepts and interpretation
- Simple linear regression
- Assumptions and limitations
- Applied business and healthcare examples
Module 5: Practical Applications and Projects
- Real-world case studies
- Business, technology, and healthcare datasets
- Insight interpretation and reportings
Module 6: Deep Learning and Neural Networks in Medical Applications (20 hrs)
- Concept of neural network – Perceptron, Neuron
- Types of Neural network – ANN, CNN, RNN and their differences
- ANN: Basic concepts, structure, forward/backward propagation in health scenarios
- Apply ANN to Stroke prediction dataset (Keras / TensorFlow)
- CNN: Image classification (chest X-rays, CT scans) with Keras/TensorFlow
- RNN/LSTM: ICU patient time series, ECG prediction
- Transfer Learning using pre-trained models (e.g., ResNet, VGG for MRI data)
- Hands-on: Build and test a CNN model for diabetic retinopathy
