Fundamental Skills in Bioinformatics

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King Abdullah University of Science and Technology

Fundamental Skills in Bioinformatics

Instructors: Jesper Tegner

15,038 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

92 reviews

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

92 reviews

Beginner level

Recommended experience

2 weeks to complete

at 10 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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

13 assignments

Taught in English

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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.

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

Programming Assignment Basics Quiz 10 minutes
Exploratory Data Analysis and Visualization in R 30 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

Instructor ratings

(27 ratings)

Jesper Tegner

King Abdullah University of Science and Technology

1 Course 15,038 learners

Offered by

King Abdullah University of Science and Technology

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Reviewed on Jun 18, 2025

Thanks for the team who made this valuable course. All the best!

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