Week 4 | Module 3A | Experimentation in Data Science (A/B Testing and Statistical Analyses) I

Instructor: Roland Doku, Ph.D. This topic covers the principles and applications of A/B testing, a foundational technique for making data-driven decisions in healthcare, business and various other industries. The session will focus on hypothesis testing, including the formulation of null and alternate hypotheses, and how they apply to experimental design. Attendees will learn about statistical […]

Week 4 | Module 3A | Experimentation in Data Science (A/B Testing and Statistical Analyses) I

Instructor: Roland Doku, Ph.D. This topic covers the principles and applications of A/B testing, a foundational technique for making data-driven decisions in healthcare, business and various other industries. The session will focus on hypothesis testing, including the formulation of null and alternate hypotheses, and how they apply to experimental design. Attendees will learn about statistical […]

Week 8 | Module 5 | Introduction to Machine Learning I 

Instructor: Ebelechukwu Nwafor, PhD This two-part module delves into the core principles of machine learning. In Part I, students will learn about supervised learning techniques, covering linear regression, classification, and model evaluation metrics. They will explore foundational algorithms, including decision trees, support vector machines, and k-nearest neighbors, while focusing on applications in real-world scenarios.

Week 8 | Module 5 | Introduction to Machine Learning I

Instructor: Ebelechukwu Nwafor, PhD This two-part module delves into the core principles of machine learning. In Part I, students will learn about supervised learning techniques, covering linear regression, classification, and model evaluation metrics. They will explore foundational algorithms, including decision trees, support vector machines, and k-nearest neighbors, while focusing on applications in real-world scenarios.

Week 9 | Module 6 | Machine Learning II

Instructor: Ebelechukwu Nwafor, PhD Part II builds on these basics from Part I, introducing unsupervised learning techniques such as clustering and dimensionality reduction. The module will also cover key concepts like overfitting, and model selection. By the end, students will understand both theoretical and practical aspects of machine learning, with hands-on experience in building and […]

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