Statistical Inference

开始时间: 12/07/2015 持续时间: 4 weeks

所在平台: Coursera

课程类别: 信息,技术与设计

大学或机构: Johns Hopkins University(约翰•霍普金斯大学)

授课老师: Brian Caffo

   

课程主页: https://www.coursera.org/course/statinference

Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.

课程评论:没有评论

第一个写评论        关注课程

课程详情

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

课程大纲

In this class students will learn the fundamentals of statistical inference. Students will receive a broad overview of the goals, assumptions and modes of performing statistical inference. Students will be able to perform inferential tasks in highly targeted settings and will be able to use  the skills developed as a roadmap for more complex inferential challenges.

课程评论(0条)

Deep Learning Specialization on Coursera

课程简介

Learn how to draw conclusions about populations or scientific truths from data. This is the sixth course in the Johns Hopkins Data Science Course Track.

课程标签

1人关注该课程

主题相关的课程