Fundamental Skills in Bioinformatics
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Fundamental Skills in Bioinformatics
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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
Skills you'll gain
Details to know
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There are 4 modules in this course
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 in programming (and data analysis) and the guidelines for improving their knowledge on it. The course will include Programming in R, programming in Python, Unix server, and reviewing basic concepts of statistics.
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
17 videos•Total 123 minutes
- Brief introduction to the course•2 minutes
- Lecture: Programming and R•4 minutes
- Lecture: Introduction to RStudio•3 minutes
- Coding Lecture: First contact with RStudio•9 minutes
- Introduction•1 minute
- Lecture: Data types in R•3 minutes
- Lecture: Data structures in R•6 minutes
- Coding Lecture: Data types in R - atomic and vectors •15 minutes
- Coding Lecture: Data types in R - lists and matrices•17 minutes
- Coding Lecture: Data types in R - data frames•7 minutes
- Lecture: Introduction to Control Flow•4 minutes
- Lecture: Loops•4 minutes
- Coding Lecture: If statements•9 minutes
- Coding Lecture: loop statements•9 minutes
- Lecture: Loading and Writing•6 minutes
- Coding Lecture: Loading and Writing•18 minutes
- Basics + where to learn more•5 minutes
2 readings•Total 40 minutes
- Setting up R•30 minutes
- Available data sets to be used in the course.•10 minutes
4 assignments•Total 95 minutes
- Introduction to R Quiz•20 minutes
- Data Types in R Quiz•15 minutes
- Control Flow in R Quiz•30 minutes
- Loading and Writing in R Quiz•30 minutes
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
20 videos•Total 162 minutes
- Introduction to Module 2•1 minute
- Lecture: Logical values, logical vectors and operations with them.•6 minutes
- Coding Lecture: Logical Vectors, part 1.•11 minutes
- Coding Lecture: Logical Vectors, part 2.•7 minutes
- Lecture: Data Quality Control.•3 minutes
- Coding Lecture: Quality Control.•6 minutes
- Lecture: Exploratory Data Analysis.•8 minutes
- Coding Lecture: EDA part 1.•9 minutes
- Coding Lecture: EDA part 2.•8 minutes
- Lecture: Correlation•12 minutes
- Coding Lecture: correlation in R•6 minutes
- Lecture: Linear Models•8 minutes
- Coding Lecture: example of a linear model•6 minutes
- Coding Lecture: evaluation of a linear model in R•6 minutes
- Lecture: t-test & ANOVA•19 minutes
- Coding Lecture: t-test.•8 minutes
- Coding Lecture: ANOVA•7 minutes
- Introduction to the dataset: Data set 4.•3 minutes
- Guided analysis.•26 minutes
- Lecture: R packages•4 minutes
1 reading•Total 10 minutes
- How do R programming assignments work?•10 minutes
2 assignments•Total 40 minutes
- Exploratory Data Analysis and Visualization in R•30 minutes
- Programming Assignment Basics Quiz•10 minutes
6 programming assignments•Total 345 minutes
- Operating with logical values and matrices•180 minutes
- Quality control of the data •45 minutes
- Correlation analysis•30 minutes
- Linear models•30 minutes
- t-test and ANOVA•30 minutes
- First analysis of an expression dataset.•30 minutes
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
19 videos•Total 151 minutes
- Introduction to the module•2 minutes
- Lecture: Python and R•2 minutes
- The Python ecosystem•11 minutes
- Python installation and environments•21 minutes
- Jupyter Lab•18 minutes
- Lecture: Python native data structures•2 minutes
- Coding Lecture: Fundamentals in data types•9 minutes
- Coding Lecture: Lists and Tuples •9 minutes
- Coding Lecture: Sets and Dictionaries•8 minutes
- Lecture: flow control and functions.•1 minute
- Coding Lecture: if conditions, for and while loops.•17 minutes
- Coding Lecture: declare and using functions in Python•6 minutes
- Lecture: overview of modules in Python•2 minutes
- Lecture: numpy•1 minute
- Coding Lecture: numpy•10 minutes
- Lecture: pandas•1 minute
- Coding Lecture: pandas•14 minutes
- Coding lecture: pandas for data exploration•9 minutes
- Coding Lecture: Visualization•9 minutes
1 reading•Total 3 minutes
- Free online Python resources•3 minutes
4 assignments•Total 40 minutes
- Python primitive values and data structures•10 minutes
- Python syntax: for, if statements and functions•10 minutes
- The numpy package•10 minutes
- The pandas package•10 minutes
4 programming assignments•Total 120 minutes
- Python data structures•30 minutes
- Python control flow•30 minutes
- The NumPy package•30 minutes
- The pandas package•30 minutes
1 ungraded lab•Total 45 minutes
- Visualization with the pandas package•45 minutes
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
19 videos•Total 142 minutes
- Overview of the week•2 minutes
- Lecture: Introduction to the case study•3 minutes
- Lecture: RNA-seq technology and data normalisation•6 minutes
- Coding Lecture: Loading and normalizing RNA-seq data•19 minutes
- Lecture: Principal Component Analysis•5 minutes
- Coding Lecture: PCA analysis in R for RNA-seq data•12 minutes
- Lecture: Finding differentially expressed genes•3 minutes
- Coding Lecture: Differential expression analysis in R•15 minutes
- Lecture: From RNA-seq to scRNA-seq•3 minutes
- Lecture: Representing scRNA-seq experiments in Python•3 minutes
- Coding Lecture: Loading a scRNA-seq experiment in Python•11 minutes
- Lecture: Preprocessing scRNA-seq data•3 minutes
- Coding Lecture: scRNA-seq preprocessing•11 minutes
- Lecture: UMAP and dimensionality reduction in single-cell studies•3 minutes
- Lecture: Cell type identification•4 minutes
- Coding Lecture: Clustering and cell type identification with Python•12 minutes
- Coding Lecture: scRNA-seq analysis in R•14 minutes
- Lecture: bioAI•11 minutes
- Thanks (for all the fish)•1 minute
2 readings•Total 20 minutes
- Relevant material for Week 4•10 minutes
- Reference resources for single-cell analysis in Python•10 minutes
3 assignments•Total 12 minutes
- Lecture: Representing scRNA-seq experiments in Python•10 minutes
- scRNA-seq preprocessing•1 minute
- Clustering and cell type indentification with Python•1 minute
4 programming assignments•Total 120 minutes
- Analysis of bulk RNAseq CD4+ T-cell data•30 minutes
- The anndata package: managing scRNA-seq data in Python•30 minutes
- scRNA-seq preprocessing with the scanpy package•30 minutes
- Cell type identification•30 minutes
Instructors
Explore more from Data Analysis
- Status: PreviewB
Birla Institute of Technology & Science, Pilani
Course
- Status: PreviewU
University of California San Diego
Course
- Status: Free TrialU
University of California San Diego
Specialization
- Status: Free TrialJ
Johns Hopkins University
Course
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Reviewed on Jun 18, 2025
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