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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/biostatistics
课程评论：没有评论
课程名称：数学生物统计入门营 1 课程概述：本课程介绍在初级数据分析中使用的基本概率和统计概念。课程将以入门级别教授，适合拥有大三或大四数学背景的学生，具有微积分的基本知识。虽然线性代数和编程的知识在课程中是有益的，但并不是必需的。 课程大纲： 1. **介绍、概率、期望值和随机向量** 描述：您将深入探讨数学生物统计的世界。在接下来的几周中，您将学习概率、期望、条件概率、分布、置信区间、自助法、二项比例等内容。模块1涵盖实验、概率、变量、质量函数、密度函数、累积分布函数、期望、变异和向量等主题。 2. **条件概率、贝叶斯法则、似然性、分布和渐近理论** 描述：本模块涵盖条件概率、贝叶斯法则、似然性、分布及渐近理论。这些是数学生物统计和统计学中的基本核心概念。完成本模块后，您应能够识别并理解这些关键概念。 3. **置信区间、自助法和绘图** 描述：本模块探讨置信区间、自助法和绘图等内容。它们是数学生物统计和统计学中的核心概念。完成本模块后，您应能够识别并掌握这些重要概念。 4. **二项比例和对数** 描述：本模块涵盖二项比例和对数。这些是数学生物统计和统计学中的核心概念。完成本模块后，您应能够识别并掌握这些关键概念。 通过本课程，学生将获得重要的数学和统计工具，增强在生物统计领域的分析能力。
Name:Introduction, Probability, Expectations, and Random Vectors
Description:You are about to undergo an intense and demanding immersion into the world of mathematical biostatistics. Over the next few weeks, you will learn about probability, expectations, conditional probabilities, distributions, confidence intervals, bootstrapping, binomial proportions, and much more. Module 1 covers experiments, probability, variables, mass functions, density functions, cumulative distribution functions, expectations, variations, and vectors.
Name:Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics
Description:This module covers Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics. These are the most fundamental core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.
Name:Confidence Intervals, Bootstrapping, and Plotting
Description:This module covers Confidence Intervals, Bootstrapping, and Plotting. These are core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.
Name:Binomial Proportions and Logs
Description:This module covers Binomial Proportions and Logs. These are core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.
This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
