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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/fundamental-skills-in-bioinformatics
课程评论：没有评论
课程名称：生物信息学基础技能 课程概述：本课程提供生物信息学基本技能的广泛且主要实用的概述，旨在支持生物与医疗专业的学生在没有编程或定量分析背景的情况下，能够同时发展定量和编程技能。通过本课程，学生将掌握进行基本数据分析所需的实践技能，学员将学习长期可应用的技能。 课程大纲： 模块1：编程入门（使用R） 描述：第一模块探讨R语言的编程基础，包括R和RStudio的使用、数据类型、循环和条件语句的理解。此外，模块还将介绍RMarkDown作为共享代码的工具。 模块2：编程入门II（使用R） 描述：第二模块主要有两个目标。首先，掌握逻辑值和向量的使用及其在质量控制中的应用；其次，通过学习基本统计分析（如探索性数据分析、相关性、线性模型、T检验和ANOVA）来实践编程技能。最后，模块将探索R编程的可用资源。 模块3：Python编程 描述：第三模块将介绍Python编程语言的基础知识。首先，比较Python和R语言，并学习Python的编程语法；其次，使用两个关键的Python模块：pandas和numpy。 模块4：生物信息学案例研究 - RNA-seq批量和单细胞数据分析 描述：最后一模块将专注于将编程知识应用于实际的RNA-seq数据分析。使用R分析批量RNA-seq数据，使用Python分析单细胞RNA-seq数据，随后将两种分析结果进行整合。最终，模块将提供深入掌握R的见解和技能。
Name:Module 1: Introduction to Programming (using R)
Description:The first module will explore the basics of programming through R and this will include: working in R and RStudio, understanding data types, loops and ifs. Additionally, the module will provide an introduction to RMarkDown as a tool for sharing code that we will use in the coding lectures.
Name:Module 2: Introduction to Programming II (using R)
Description:The second module will focus on two aims. Firstly, to master the use of logical values and vectors and its applications in quality control. Secondly, to practice the programming skills while learning how to perform basic statistical analysis. This will include: explorative data analysis, correlation, linear models, T-test, and ANOVA. Finally, we will explore the available resources for R programming.
Name:Module 3: Programming in Python
Description:The third module will provide the basics of the Python programming language. First, the module will compare Python and R language and learn the programming syntax of Python. Second, the module will work with two key Python modules: pandas and numpy.
Name:Module 4: Bioinformatics case study - RNA-seq bulk and single-cell data analysis
Description:The final module will focus on applying knowledge and understanding of programming in the analysis of real RNA-seq data. R will be used for analysing of bulk RNA-seq and Python for single- cell RNA-seq. The results of both analyses will then be integrated. Finally, the module will provide insights in how to gain deeper knowledge and skills in R.
The course provides a broad and mainly practical overview of fundamental skills for bioinformatics (and, in general, data analysis). The aim is to support the simultaneous development of quantitative and programming skills for biological and biomedical students with little or no background in programming or quantitative analysis. Through the course, the student will develop the necessary practical skills to conduct basic data analysis. Most importantly, participants will learn long-term skills
