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
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
Instructor: Ronald 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 […]
Instructor: Ronald 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 […]
Instructor: Ronald Doku, Ph.D. Part II will build on concepts discuss in Part I. We will go through examples of how to conduct an A/B test on a real-world use case, covering all necessary processes from designing the experiment to interpreting the results. Discuss situations when A/B testing doesn’t work. By the end of the […]
Instructor: Ronald Doku, Ph.D. Part II will build on concepts discuss in Part I. We will go through examples of how to conduct an A/B test on a real-world use case, covering all necessary processes from designing the experiment to interpreting the results. Discuss situations when A/B testing doesn’t work. By the end of the […]
Instructor: TBD. Topics to be determined.
Instructor: TBD. Topics to be determined.
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.