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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mathstat/
课程评论：没有评论
**课程名称：** 数据科学中的数理统计 **课程概述：** 本课程深入讲解数理统计学的基本原理，重点介绍估计方法，如矩估计法和最大似然估计法（MLEs）。课程将通过评估估计量的偏差、方差和效率来评价估计量，并探索渐近统计学，包括中心极限定理和置信区间。 **课程亮点：** * 57个引人入胜的视频讲座，采用创新的灯板技术，提供互动式学习体验。 * 每节课均配有深入的讲义，突出视频课程中的关键词汇、示例和解释。 * 每章末的练习题，帮助巩固理解，提升课程技能。 **主要主题：** * 基本概率分布：伯努利分布、均匀分布和正态分布。 * 期望值及其与样本均值的联系。 * 矩估计法用于开发估计量。 * 估计量的期望值和无偏估计量。 * 随机变量和估计量的方差。 * 费舍尔信息量和Cramér-Rao下界。 * 中心极限定理。 * 置信区间。 **适合人群：** * 具备初步统计学经验，希望深入学习数理统计基础的学生。 * 希望在求职面试中巩固或提升统计学知识的数据科学专业人士。 * 有志于培养统计思维和增强分析能力的人士。 **先修要求：** 本课程要求具备高中代数知识和变量方程求解能力。部分章节会涉及初步微积分概念，如微分和积分。然而，即使没有微积分基础，数学能力较强的学习者也能跟上课程，仅会错过少数次要的数学细节。
This course teaches the foundations of mathematical statistics, focusing on methods of estimation such as the method of moments and maximum likelihood estimators (MLEs), evaluating estimators by their bias, variance, and efficiency, and explore asymptotic statistics, including the central limit theorem and confidence intervals.Course Highlights:57 engaging video lectures, featuring innovative lightboard technology for an interactive learning experienceIn-depth lecture notes accompanying each lesson, highlighting key vocabulary, examples, and explanations from the video sessionsEnd-of-chapter practice problems to solidify your understanding and refine your skills from the courseKey Topics Covered:Fundamental probability distributions: Bernoulli, uniform, and normal distributionsExpected value and its connection to sample meanMethod of moments for developing estimatorsExpected value of estimators and unbiased estimatorsVariance of random variables and estimatorsFisher information and the Cramer-Rao Lower BoundCentral limit theoremConfidence intervalsWho This Course Is For:Students with prior introductory statistics experience, looking to delve deeper into mathematical foundationsData science professionals seeking to refresh or enhance their statistics knowledge for job interviewsAnyone interested in developing a statistical mindset and strengthening their analytical skillsPre-requisites:This course requires a solid understanding of high school algebra and equation manipulation with variables. Some chapters utilize introductory calculus concepts, such as differentiation and integration. However, even without prior calculus knowledge, those with strong math skills can follow along and only miss a few minor mathematical details.
