Advanced Statistics for Data Science
Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression. This specialization starts with Mathematical Statistics bootcamps, specifically concepts and methods used in biostatistics applications.
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- Level Expert
- المدة
- الطبع بواسطة Johns Hopkins University
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عن
Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression. This specialization starts with Mathematical Statistics bootcamps, specifically concepts and methods used in biostatistics applications. These range from probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling. This specialization also linear models for data science, starting from understanding least squares from a linear algebraic and mathematical perspective, to statistical linear models, including multivariate regression using the R programming language. These courses will give learners a firm foundation in the linear algebraic treatment of regression modeling, which will greatly augment applied data scientists' general understanding of regression models. This specialization requires a fair amount of mathematical sophistication. Basic calculus and linear algebra are required to engage in the content.Auto Summary
**Advanced Statistics for Data Science** is an expert-level course meticulously designed for those delving into the realms of IT and Computer Science, particularly aspiring biostatisticians and data scientists. This Coursera course offers a comprehensive understanding of fundamental concepts in probability, statistics, and linear models, which are crucial for any data science professional. The course kicks off with Mathematical Statistics bootcamps, covering key topics like probability, distribution, likelihood concepts, hypothesis testing, and case-control sampling, all with a focus on biostatistics applications. It then progresses to an in-depth exploration of linear models for data science. Learners will gain a strong grasp of least squares from both linear algebraic and mathematical perspectives and delve into statistical linear models, including multivariate regression, using the R programming language. This specialization is ideal for individuals who have a solid foundation in basic calculus and linear algebra, as the content demands a significant level of mathematical sophistication. The course equips learners with the behind-the-scenes mechanisms of key modeling tools in data science, enhancing their overall understanding and application of regression models. With subscription options including Starter and Professional, learners can choose a plan that best fits their needs. Perfect for those looking to deepen their expertise, **Advanced Statistics for Data Science** provides the essential skills and knowledge to excel in the field of data science.
Brian Caffo, PhD