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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-for-healthcare/
课程评论：没有评论
课程名称：医疗健康数据科学 概述： 本课程旨在向医疗健康领域的学生和专业人士介绍使用Python进行数据科学的基础知识。课程涵盖的主题广泛，从基本的编程技能到先进的数据分析和机器学习技术，所有内容均与医疗健康应用相关。通过互动式Jupyter笔记本，参与者将学习如何分析和解释复杂的医疗健康数据，最终获得能够为临床决策提供信息、提升患者护理以及推动医疗创新的见解。 目标受众： - 医疗健康学生（医学、药学、护理、公共健康等） - 希望将数据科学融入实践的医疗健康专业人士（临床医生、研究人员、管理人员） 先修条件： - 基本统计知识 - 基本计算机技能 - 无需编程经验 课程结束时，参与者将能够： - 设置并导航Jupyter笔记本环境 - 理解并应用Python数据科学基础 - 从各种来源获取和导入医疗健康数据 - 清理和预处理数据以便进行分析 - 执行探索性数据分析（EDA）以发现趋势和模式 - 应用数据预处理技术为建模做好准备 - 构建和评估基本的机器学习模型以预测健康风险 课程结构： 本课程总共包含八个模块，涵盖从介绍和设置编码环境到构建和评估机器学习模型的主题。每个模块包括示例编码笔记本、视频、测验和补充笔记，以增强学习体验。
This course is designed to introduce healthcare students and professionals to the fundamentals of data science using Python. It covers a wide range of topics from basic programming skills to advanced data analysis and machine learning techniques, all within the context of healthcare applications. Through interactive Jupyter notebooks, participants will learn how to analyze and interpret complex healthcare data, ultimately gaining insights that can inform clinical decisions, enhance patient care, and drive healthcare innovation.Target Audience• Healthcare students (medicine, pharmacy, nursing, public health, etc.)• Healthcare professionals (clinicians, researchers, administrators) looking toincorporate data science into their practicePrerequisites• Basic understanding of statistics• Basic computer skills• No prior programming experience requiredBy the end of this course, participants will be able to:• Set up and navigate the Jupyter notebook environment.• Understand and apply Python basics for data science.• Acquire and import healthcare data from various sources.• Clean and preprocess data for analysis.• Perform exploratory data analysis (EDA) to uncover trends and patterns. • Apply data preprocessing techniques to prepare data for modeling.• Build and evaluate a basic machine learning model to predict health risk.Course StructureThe course contains eight modules in total, covering topics from an introduction and setting up your coding environment to building and evaluating a machine learning model.Each module includes example coding notebooks, videos, quizzes, and supplementary notes to enhance your learning experience.
