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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-for-biostatistics-analyzing-infectious-diseases-data/
课程评论：没有评论
课程名称：生物统计学中的Python：分析传染病数据 课程概述：欢迎参加生物统计学中的Python：分析传染病数据课程。本课程是一门综合性的项目导向课程，您将循序渐进地学习如何对传染病数据集进行复杂的分析和可视化。本课程完美结合了生物统计学与Python，为您提供应对公共卫生实际挑战所需的工具和技术。课程主要集中于三个重要方面：首先是数据分析，您将从多个角度探讨传染病数据；其次是时间序列预测，您将逐步学习如何使用STL模型预测传染病的传播；第三是公共健康政策，您将学习如何基于流行病学模型制定数据驱动的公共健康政策。 在导言部分，您将学习生物统计学的基本原理，了解在分析生物统计数据时常遇到的挑战及我们将使用的统计模型，例如季节性趋势分解模型（STL）。接下来，您还将学习如何使用Kermack-McKendrick方程计算传染病传播率，这是进入编码环节之前非常重要的概念。此外，您将了解多种可能加速传染病传播的因素，如人口密度、医疗服务可及性和抗原变异。 完成生物统计学的相关学习后，您将开始项目部分。首先，您将逐步学习如何设置Google Colab IDE，以及如何从Kaggle查找和下载传染病数据集。项目部分将分为三个主要部分：第一部分是进行探索性数据分析；第二部分是构建预测模型，使用时间序列模型预测未来的传染病传播；第三部分是进行流行病学建模，并利用结果制定公众健康政策以减缓传染病的传播。 在开始课程之前，我们需要思考一个问题：为什么要学习生物统计学，特别是传染病分析？首先，如果您有兴趣在公共卫生或医疗行业工作，掌握生物统计学知识将极为有益，并能帮助您提升职业发展。此外，您还将学习到许多可以应用于其他项目的宝贵技能，例如时间序列分解可以用于预测股票、房地产、商品和加密货币市场。最后，这门课程也将训练您成为更出色的公共健康政策制定者，您将深入学习如何做出数据驱动的决策，并考虑其他外部因素。 课程内容包括但不限于以下内容： - 学习生物统计学和传染病分析的基本原理 - 使用SIR模型计算传染病传播率 - 了解加速传染病传播的因素，如人口密度和群体免疫 - 学习如何从Kaggle获取和下载数据集 - 数据清洗，包括移除缺失行和重复值 - 使用Z分数法检测潜在异常值 - 分析人口与疾病传播率之间的相关性 - 分析感染患者的人口统计特征 - 使用热图绘制各县的传染病分布 - 分析传染病年度趋势 - 进行置信区间分析 - 使用时间序列分解模型预测传染病传播率 - 使用SIR模型进行流行病学建模 - 评估公共健康政策 欢迎您加入这个激动人心的学习旅程！
Welcome to Python for Biostatistics: Analyzing Infectious Diseases Data course. This is a comprehensive project-based course where you will learn step by step on how to perform complex analysis and visualization on infectious diseases datasets. This course is a perfect combination between biostatistics and Python, equipping you with the tools and techniques to tackle real-world challenges in public health. The course will be mainly concentrating on three major aspects, the first one is data analysis where you will explore the infectious diseases data from multiple perspectives, the second one is time series forecasting where you will be guided step by step on how to forecast the spread of infectious diseases using STL model, and the third one is public health policy where you will learn how to make a data driven public health policy based on epidemiological modeling. In the introduction session, you will learn the basic fundamentals of biostatistics, such as getting to know more about challenges that we commonly face when analyzing biostatistics data and statistical models that we will use, for instance STL which stands for seasonal trend decomposition. Then, you will continue by learning how to calculate infectious disease transmission using Kermack-McKendrick equation, this is a very important concept that you need to understand before getting into the coding session. Afterward, you will also learn several factors that can potentially accelerate the spread of infectious diseases, such as population density, healthcare accessibility, and antigenic variation. Once you have learnt all necessary information about biostatistics, we will start the project. Firstly, you will be guided step by step on how to set up Google Colab IDE. Not only that, you will also learn how to find and download infectious diseases dataset from Kaggle. Once, everything is ready, we will enter the main section of the course which is the project section The project will be consisted of three main parts, the first part is to conduct exploratory data analysis, the second part is to build forecasting model to predict the spread of the diseases in the future using time series model, meanwhile the third part is to perform epidemiological modelling and use the result to develop a public health policy to slow down the spread of the infectious disease.First of all, before getting into the course, we need to ask this question to ourselves: why should we learn biostatistics, particularly infectious diseases analysis? Well, there are many reasons why, firstly, if you are interested in working in the public health or healthcare industry, having biostatistics knowledge would be very beneficial and help you to level up your career. In addition to that, you will also learn a lot of valuable skill sets that can be implemented in other projects, for example, time series decomposition can be used to forecast stock, real estate, commodity, and cryptocurrency markets. Last but not least, this course will also train you to be a better public health policy maker as you will extensively learn how to make data driven decisions and take other external factors into consideration.Below are things that you can expect to learn from this course:Learn the basic fundamentals of biostatistics and infectious disease analysisLearn how to calculate infectious disease transmission rate using SIR modelLearn several factors that accelerate the spread of infectious disease, such as population density, herd immunity, and antigenic variationLearn how to find and download datasets from KaggleLearn how to clean dataset by removing missing rows and duplicate valuesLearn how to detect potential outliers using Z score methodLearn how to find correlation between population and disease rateLearn how to analyze infected patient demographicsLearn how to map infectious disease per county using heatmapLearn how to analyze infectious disease yearly trendLearn how to perform confidence interval analysisLearn how to forecast infectious disease rate using time series decomposition modelLearn how to do epidemiological modeling using SIR modelLearn how to perform public health policy evaluation
