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Fundamental Skills in Bioinformatics

Vincenzo Lagani, David Gomez-Cabrero, Robert Lehman, Zafer Ali, and Jesper Tegner

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.

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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.

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What's inside

Syllabus

Module 1: Introduction to Programming (using R)
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.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Designed for biology and biomedical learners who want to enhance their quantitative and programming proficiency
Suitable for beginners with minimal prior knowledge in programming or data analysis
Empowers learners to develop long-term skills in programming and data analysis, establishing a strong foundation for future growth
Imparts practical skills in data analysis through a series of hands-on exercises
Covers essential programming languages, including R and Python, while emphasizing their practical applications in bioinformatics
Provides guidance for learners to continuously expand their knowledge and skills in programming and data analysis

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Create your own learning path. Save this course to your list so you can find it easily later.
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Reviews summary

Foundational bioinformatics for life scientists

According to learners, this course offers a largely positive and practical introduction to bioinformatics, primarily for those in biological and biomedical fields with little to no prior coding experience. Students particularly highlight the hands-on activities and the RNA-seq case study as highly valuable and directly applicable to research. While the course provides a solid foundation in R and Python, some learners with absolutely zero programming background found the pace occasionally challenging, especially in the Unix module. More recent reviews indicate that initial technical issues have been largely resolved, demonstrating ongoing improvement.
Offers a broad overview, providing fundamental skills rather than deep mastery.
"The course has good intentions, but the coverage felt a bit shallow in some areas."
"Good for a general overview, but not for mastery."
"It's a solid start, but don't expect to be an expert overnight."
"It's a stepping stone, not a deep dive."
Earlier technical issues with lab environments appear to have been resolved.
"I believe the course has improved over time as I heard earlier versions had some technical glitches that seem to be resolved now."
"Some technical issues in the virtual environment at the beginning of the course, but those were quickly resolved by the support team."
"I noticed some minor glitches in older versions that seem to have been fixed."
A gentle and comprehensive entry point for biological students new to programming.
"This course was exactly what I needed to bridge the gap between my biology background and data analysis."
"Fantastic course for biologists wanting to learn coding for bioinformatics."
"Perfect for my needs as a researcher. I needed to learn R and Python for my experiments."
"I had no programming experience whatsoever, and this course gently guided me through R and Python."
Provides essential hands-on experience with real-world bioinformatics data.
"The RNA-seq case study was incredibly practical and directly applicable to my research."
"The practical examples, especially the RNA-seq analysis, made everything click."
"The practical application with RNA-seq data is brilliant. It's challenging but rewarding."
"I learned so much about R, Python, and even basic Unix commands that are crucial for my lab work."
Learners with prior coding found early modules too basic and overall depth insufficient.
"While the course aims to teach fundamental skills, I found it somewhat superficial for someone with a basic understanding of programming."
"It's clearly designed for absolute beginners, which wasn't my case."
"The R sections were very elementary, and the Python part was too short."
Some found the pace fast, particularly the Unix and Python modules, needing extra effort.
"If you have absolutely zero background, you might struggle to keep up without prior self-study."
"I found this course very difficult to follow. The pace was too fast for someone with absolutely zero background."
"The Unix commands were a good addition, though I had to rewatch those lectures multiple times."
"Python was a bit fast, but still helpful."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Fundamental Skills in Bioinformatics with these activities:
Review R programming basics
This activity will help you refresh your understanding of R programming basics, which will be essential for success in this course.
Browse courses on R Programming
Show steps
  • Review online tutorials on R programming
  • Complete practice exercises on R programming
  • Create a small R program to analyze a simple dataset
Review programming basics
Review the fundamentals of programming language, particularly R and Python, for easier understanding of the concepts discussed in the course.
Browse courses on Programming
Show steps
  • Go over essential programming concepts such as variables, data types, and control flow.
  • Practice solving simple coding problems to reinforce your understanding.
  • Review basic statistical concepts such as mean, median, and standard deviation.
Create a personalized resource guide
Organize and expand your learning materials by compiling a resource guide with relevant articles, tutorials, and tools.
Show steps
  • Gather and curate online resources, such as articles, tutorials, and videos.
  • Organize the resources into categories or topics.
  • Add your own notes and annotations to enhance your understanding.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Read 'Bioinformatics Data Skills' by Vince Buffalo
Gain a deeper understanding of bioinformatics data analysis and visualization techniques by reading this recommended book.
View Bioinformatics Data Skills: Reproducible and... on Amazon
Show steps
  • Read the book and take notes on key concepts and methods.
  • Work through the exercises and examples provided in the book.
  • Apply the techniques you learn to your own bioinformatics projects.
Solve Python programming challenges
This activity will help you develop your Python programming skills by solving challenging problems.
Browse courses on Python Programming
Show steps
  • Find online Python programming challenges
  • Attempt to solve the challenges on your own
  • Review solutions to the challenges and identify areas for improvement
Join a bioinformatics study group
Engage with fellow students and discuss course concepts, share resources, and help each other with assignments.
Show steps
  • Find or create a study group with other students taking the course.
  • Meet regularly to discuss the course material and work on assignments together.
Solve programming exercises
Strengthen your programming skills by solving coding exercises and challenges related to the topics covered in the course.
Show steps
  • Find online coding platforms or resources that provide practice problems.
  • Start with easier exercises and gradually increase the difficulty level.
  • Debug your code and try to find efficient solutions.
Write a blog post on a bioinformatics topic
This activity will help you solidify your understanding of a bioinformatics topic by writing a blog post about it.
Browse courses on Bioinformatics
Show steps
  • Choose a bioinformatics topic that you are interested in
  • Research the topic and gather information
  • Write a blog post that explains the topic in a clear and concise way
Explore advanced programming techniques
Enhance your knowledge by exploring advanced programming techniques and concepts that can complement the course material.
Show steps
  • Identify advanced programming topics that you want to learn.
  • Find online tutorials, courses, or books that cover those topics.
  • Work through the tutorials and apply the new techniques in your own projects.
Contribute to open-source bioinformatics projects
Enhance your practical skills and contribute to the bioinformatics community by participating in open-source projects.
Show steps
  • Identify open-source bioinformatics projects that align with your interests.
  • Join the project and start contributing code, documentation, or other resources.
Develop a bioinformatics project
Apply the skills learned in the course to create a bioinformatics project that demonstrates your understanding and proficiency.
Show steps
  • Identify a biological question or problem that you want to address.
  • Gather and prepare the necessary data.
  • Design and implement a bioinformatics workflow to analyze the data.
  • Interpret the results and draw conclusions.

Career center

Learners who complete Fundamental Skills in Bioinformatics will develop knowledge and skills that may be useful to these careers:
Bioinformatician
Programming in R and Python is essential for a Bioinformatician, whose primary responsibilities include analyzing and interpreting large datasets related to biology and genetics. The skills acquired in this course can provide a solid foundation for working with the necessary tools and technologies used by Bioinformaticians. Furthermore, the focus on data analysis will enable learners to understand and extract meaningful insights from complex biological data.
See salaries and explore the career path for Bioinformatician
Data Analyst
Data Analysts play a crucial role in analyzing, interpreting, and presenting data to derive meaningful insights. This course provides a comprehensive overview of data analysis techniques, covering programming in R and Python, statistical concepts, and data visualization. These skills are essential for developing a foundation in data analysis, which is highly valued in the industry.
See salaries and explore the career path for Data Analyst
Data Scientist
Data Scientists bridge the gap between data and business, leveraging their expertise in data analysis and programming to solve complex problems. This course offers a solid introduction to data analysis using R and Python, along with essential statistical concepts. By developing these skills, learners can lay the groundwork for a career as a Data Scientist and contribute effectively to data-driven decision-making.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning systems to solve real-world problems. This course provides a foundation in programming, data analysis, and statistical concepts, which are fundamental for understanding and applying machine learning algorithms. The skills gained in this course will be valuable for individuals aspiring to become Machine Learning Engineers.
See salaries and explore the career path for Machine Learning Engineer
Quantitative Analyst
Quantitative Analysts (Quants) use mathematical and statistical models to analyze financial data and make investment decisions. This course provides a solid foundation in programming and data analysis, which are essential skills for Quants. By mastering these skills, learners can enhance their understanding of financial data and develop the analytical abilities required for success in this field.
See salaries and explore the career path for Quantitative Analyst
Research Analyst
Research Analysts conduct in-depth research and analysis to provide insights and recommendations on various industries, companies, and markets. This course offers a comprehensive overview of data analysis techniques, programming, and statistical concepts, which are essential for conducting rigorous research and analyzing data effectively. The skills gained in this course will be highly valuable for individuals pursuing a career as a Research Analyst.
See salaries and explore the career path for Research Analyst
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a foundation in programming, data structures, and algorithms, which are essential for Software Engineers. By mastering these skills, learners can enhance their understanding of software development principles and best practices, preparing them for a successful career in software engineering.
See salaries and explore the career path for Software Engineer
Statistician
Statisticians collect, analyze, interpret, and present data to derive meaningful insights and inform decision-making. This course provides a comprehensive overview of statistical concepts and data analysis techniques, which are essential for a Statistician. By mastering these skills, learners can develop a strong foundation in statistical analysis and prepare for a career in this field.
See salaries and explore the career path for Statistician
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure to support data analysis and decision-making. This course provides a foundation in data management, programming, and data analysis, which are essential for Data Engineers. By mastering these skills, learners can develop the technical expertise required to succeed in this growing field.
See salaries and explore the career path for Data Engineer
Business Analyst
Business Analysts bridge the gap between business and technology, analyzing business processes and identifying areas for improvement. This course provides a foundation in data analysis, programming, and statistical concepts, which are valuable for Business Analysts. By mastering these skills, learners can develop a deeper understanding of data-driven decision-making and enhance their ability to contribute to business strategy.
See salaries and explore the career path for Business Analyst
Health Data Analyst
Health Data Analysts leverage data analysis techniques to improve patient care and healthcare systems. This course provides a foundation in programming, data analysis, and statistical concepts, which are essential for Health Data Analysts. By mastering these skills, learners can develop the expertise required to analyze healthcare data and contribute to evidence-based decision-making in the healthcare industry.
See salaries and explore the career path for Health Data Analyst
Financial Analyst
Financial Analysts analyze financial data and make recommendations on investments and financial decisions. This course provides a foundation in programming, data analysis, and statistical concepts, which are valuable for Financial Analysts. By mastering these skills, learners can develop a deeper understanding of financial data and enhance their ability to make informed investment decisions.
See salaries and explore the career path for Financial Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in various industries. This course provides a foundation in programming, data analysis, and statistical concepts, which are valuable for Operations Research Analysts. By mastering these skills, learners can develop the analytical abilities required to identify and solve operational challenges effectively.
See salaries and explore the career path for Operations Research Analyst
Epidemiologist
Epidemiologists investigate the distribution and determinants of health-related events and diseases in populations. This course provides a foundation in programming, data analysis, and statistical concepts, which are valuable for Epidemiologists. By mastering these skills, learners can develop the analytical abilities required to identify and analyze patterns of disease occurrence and contribute to public health initiatives.
See salaries and explore the career path for Epidemiologist
Biostatistician
Biostatisticians apply statistical methods to analyze biological data and draw conclusions about health and disease. This course provides a foundation in programming, data analysis, and statistical concepts, which are essential for Biostatisticians. By mastering these skills, learners can develop the expertise required to analyze complex biological data and contribute to advancements in biomedical research.
See salaries and explore the career path for Biostatistician

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Fundamental Skills in Bioinformatics.
Cover image
Command-Line Rust
Save
Focuses specifically on Python programming for bioinformatics and provides a thorough introduction to the Python language and its applications in bioinformatics. It covers topics such as sequence analysis, data visualization, and machine learning, and is suitable for both beginners and experienced programmers.
Command-Line Rust
Paperback
Check
Command-Line Rust
Kindle Edition
Check
Cover image
R for Data Science
Save
Provides a comprehensive introduction to data science using R. It covers topics such as data manipulation, data visualization, and statistical modeling, and is suitable for both beginners and experienced programmers.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
Bioinformatics and Functional Genomics
Save
Provides a comprehensive overview of bioinformatics and functional genomics. It covers topics such as DNA sequencing, gene expression analysis, and comparative genomics, and is suitable for both beginners and experienced researchers.
Bioinformatics and Functional Genomics
Hardcover
$$$$
Bioinformatics and Functional Genomics
Paperback
$$$$
Bioinformatics and Functional Genomics
Paperback
$$$$
Bioinformatics and Functional Genomics
Kindle Edition
$$$$
Cover image
BIOINFORMATICS: A PRACTICAL GUIDE TO THE ANALYSIS...
Save
Provides a comprehensive overview of bioinformatics and its applications. It covers topics such as sequence analysis, gene expression analysis, and proteomics, and is suitable for both beginners and experienced researchers.
Bioinformatics: A Practical Guide To The Analysis...
Paperback
$$
Cover image
Bioinformatics
Save
Provides a comprehensive overview of bioinformatics and its applications. It covers topics such as sequence analysis, gene expression analysis, and genome analysis, and is suitable for both beginners and experienced researchers.
Bioinformatics: Sequence and Genome Analysis
Kindle Edition
$$$
Bioinformatics: Sequence and Genome Analysis
Paperback
$$
Cover image
Bioinformatics Programming Using Python
Save
Provides a practical guide to programming for bioinformatics using Python. It covers a wide range of topics, including sequence analysis, data manipulation, and statistical analysis, and is written in a clear and concise style.
Bioinformatics Programming Using Python: Practical...
Paperback
$$
Bioinformatics Programming Using Python by Mitchell...
Unknown Binding
$$$
Bioinformatics Programming Using Python: Practical...
Kindle Edition
$$
Cover image
Python for Data Analysis
Save
Provides a practical guide to data analysis using Python. It covers topics such as data manipulation, data visualization, and statistical modeling, and is suitable for both beginners and experienced programmers.
Python for Data Analysis
Paperback
Check
Python for Data Analysis
Kindle Edition
Check
Cover image
Biological Sequence Analysis
Save
Provides a comprehensive overview of algorithms for computational biology. It covers topics such as sequence alignment, gene finding, and phylogenetic analysis, and is suitable for readers with a strong background in computer science.
Biological Sequence Analysis: Probabilistic Models...
Paperback
$$
Biological Sequence Analysis: Probabilistic Models...
Paperback
$$
Biological Sequence Analysis: Probabilistic Models...
Kindle Edition
$$
Cover image
Statistical Methods in Bioinformatics
Save
Provides a comprehensive overview of statistical methods used in bioinformatics. It covers topics such as probability theory, statistical inference, and data analysis, and is suitable for readers with a strong background in mathematics.
Statistical Methods in Bioinformatics: An...
Hardcover
$$$$
Statistical Methods in Bioinformatics: An...
Kindle Edition
$$$

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Effort
4-week course. On average the student will require approximately between 6 to 8 hours per week.
Level
Introductory
Via
Coursera
Institution
King Abdullah University of Science and Technology
Instructors
Vincenzo Lagani
David Gomez-Cabrero
Robert Lehman
Zafer Ali
Jesper Tegner
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Designed for biology and biomedical learners who want to enhance their quantitative and programming proficiency
Suitable for beginners with minimal prior knowledge in programming or data analysis
Empowers learners to develop long-term skills in programming and data analysis, establishing a strong foundation for future growth
Imparts practical skills in data analysis through a series of hands-on exercises
Covers essential programming languages, including R and Python, while emphasizing their practical applications in bioinformatics
Provides guidance for learners to continuously expand their knowledge and skills in programming and data analysis
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