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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/compstatsintro
课程评论：没有评论
课程名称：贝叶斯统计入门 概述：本课程旨在向有志于成为数据科学家的学员介绍计算统计学。学员将从基础的概率、贝叶斯建模和推断开始学习。该课程是三门课程中的第一门，期间将使用Python和Jupyter笔记本进行贝叶斯建模的演示和实践。课程网站可访问：https://sjster.github.io/introduction_to_computational_statistics/docs/index.html。课程笔记本可通过访问网页上的说明进行下载，网址为：https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html。课程讲师为Dr. Srijith Rajamohan和Dr. Robert Settlage。 课程大纲： 1. **环境设置**：介绍小组课程的计算环境，让用户熟悉数据科学的Databricks生态系统，并提供不需要设置的Binder访问笔记本的方法。 2. **概率基础入门**：学习概率和统计的基础知识，重点是熟悉相关术语和概念。 3. **常见分布的动手介绍**：介绍常见分布，并提供生成、绘制和与这些分布互动的Python代码。学习如何为各种分布执行最大似然估计（MLE）以及对非参数分布进行核密度估计（KDE）。 4. **采样算法**：介绍生成分布的各种采样算法，同时提供执行采样的Python代码。 本课程为希望深入了解贝叶斯统计的学员提供了全面的基础和实践机会。
Name:Environment Setup
Description:Introduction to the compute environment for the Specialization. The users will be introduced to the Databricks Ecosystem for Data Science. The users can also deploy the notebooks to Binder for setup-free access.
Name:Introduction to the Fundamentals of Probability
Description:In this module, you will learn the foundations of probability and statistics. The focus is on gaining familiarity with terms and concepts.
Name:A Hands-On Introduction to Common Distributions
Description:Tis module will be an introduction to common distributions along with the Python code to generate, plot and interact with these distributions. You will also learn how to perform Maximum Likelihood Estimation (MLE) for various distributions and Kernel Density Estimation (KDE) for non-parametric distributions.
Name:Sampling Algorithms
Description:This module introduces you to various sampling algorithms for generating distributions. You will also be introduced to Python code that performs sampling.
The objective of this course is to introduce Computational Statistics to aspiring or new data scientists. The attendees will start off by learning the basics of probability, Bayesian modeling and inference. This will be the first course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html. The instructors for this course will be Dr. Srijith Rajamohan and Dr. Robert Settlage.
