Course Overview
- Course Title: Certified Statistical Modelling & Inference
- Instructor: Muhammad Shafiq (Data Scientist, AI & ML Engineer, Lecturer, Researcher)
- Target Audience:
- Aspiring data scientists and quantitative analysts
- Professionals seeking statistical modeling and inference skills
- Researchers requiring predictive analytics and hypothesis testing expertise
- Students preparing for data science roles or academic research
- Prerequisites:
- Basic understanding of statistics (descriptive statistics recommended)
- Familiarity with Python or R (practical exercises included)
Curriculum Highlights
- Key Topics Covered:
- Ordinary Least Squares (OLS) Regression (assumptions, diagnostics, regularization: Ridge, Lasso)
- Generalized Linear Models (GLMs) (Logistic Regression, Poisson Regression)
- Hypothesis Testing (p-values, confidence intervals, statistical significance)
- Bayesian Inference (fundamentals and comparative analysis)
- Model Interpretation & Communication (technical and non-technical stakeholder reporting)
- Key Skills Learned:
- Building and validating predictive statistical models
- Applying regularization techniques to prevent overfitting
- Conducting rigorous hypothesis testing and inference
- Implementing GLMs for non-normal data distributions
- Interpreting and communicating model outputs effectively
Course Format
- Duration: Self-paced (3 practice tests, video lectures, hands-on assignments)
- Format: Online course (lifetime access, mobile-compatible)
- Resources:
- Real-world case studies (industry-relevant datasets)
- Coding assignments (Python/R implementations)
- Downloadable materials (lecture slides, code templates)
- Quizzes & practice tests (certification preparation)