Week 3 | Module 2B | Introduction to Python II

Instructor: Moussa Doumbia, Ph.D. The module builds upon Introduction to Python I. Topics we include: Web scraping with python Use matplotlib and seaborn for data visualizations Use plotly for interactive visualizations

Week 3 | Module 2B | Introduction to Python II

Instructor: Moussa Doumbia, Ph.D. The module builds upon Introduction to Python I. Topics we include: Web scraping with python Use matplotlib and seaborn for data visualizations Use plotly for interactive visualizations

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.

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