Johns Hopkins University 3 credits Data Science
This course delves deeper into the computational analytics of Data Science by introducing foundational and advanced methods in Bayesian analysis and causal inference through lectures and hands-on exercises. Topics include probabilistic modeling, regression techniques, propensity scores, difference-in-differences, conditional treatment effects, and advanced methods such as panel data analysis, metalearners, and Gaussian processes. Students will learn to construct, evaluate, and diagnose Bayesian and causal models using tools like PyMC3, Bambi, and metalearners. The course emphasizes practical applications, including addressing biases, leveraging panel data, and extending causal analyses to real-world decision-making. Hands-on exercises reinforce critical concepts, and students will synthesize methods to solve complex problems. Prerequisite(s): EN.685.648 Data Science
Our subject experts help with essays, research papers, case studies, discussion posts, and capstone projects for these programs β written from scratch to your course requirements.
Program and course information is reproduced from each university's published academic catalog for informational purposes and may change. Always confirm current requirements with your university. We are an independent academic support service and are not affiliated with or endorsed by the universities listed.
Expert guides on essay writing, citation styles, study skills, and academic success.
View All Articles
Learn how to write a good research question for a paper. Narrow your topic, identify a research problem, and create a focused question with practical examples.
Learn how to choose a research topic you can actually research. Discover how to narrow broad ideas, find reliable sources, and develop a focused research question.
Learn how to cite sources in a research paper using APA, MLA, and Chicago styles, with in-text citation examples and reference-list tips.