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.
Fundamental Skills in Bioinformatics
Gain insight into a topic and learn the fundamentals.
4.3
(58 reviews)
Beginner level
Recommended experience
24 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Basics of R
Basics of Python
How to analyze bulk RNAseq count data
How to analyze single cell RNAseq count data
Details to know
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Assessments
13 assignments
Taught in English
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There are 4 modules in this course
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.
What's included
17 videos2 readings4 assignments
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.
What's included
20 videos1 reading2 assignments6 programming assignments
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.
What's included
19 videos1 reading4 assignments4 programming assignments1 ungraded lab
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.
What's included
19 videos2 readings3 assignments4 programming assignments
Instructors
Instructor ratings
4.6 (18 ratings)
Recommended if you're interested in Data Analysis
Peking University
University of California San Diego
University of California San Diego
Icahn School of Medicine at Mount Sinai
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Frequently asked questions
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