Bioinformaticians must understand complex biological data sets and build software systems that organize and interpret said data.
So, students in the field need to take courses in different disciplines, including math.
Follow along to learn how to prepare for math in bioinformatics.
Every job in bioinformatics will require at least a moderate understanding of math, especially linear algebra, statistics, and data structures.
Bioinformatics is math-heavy because experts build, optimize, or use software seeking to understand and organize biological information.
First, math is important to analyze and understand biological sequences and structures.
Second, math is crucial for designing and writing programs that can process large amounts of complex data.
For this reason, bioinformatics professions focusing on both elements will need the most math.
Bioinformatics engineers who design new ways of analyzing or interpreting data or invent new types of software must comprehend math abstractly and practically.
Whether you wish to become a bioinformatician, bioinformatics specialist, or bioinformatics engineer, it is essential to grasp mathematics.
That said, the amount and levels of mathematics will depend on the profession you choose to pursue within bioinformatics.
If you are a bioinformatics student who does not enjoy math, you can focus on the applications of current methods and algorithms instead of trying to come up with new ones.
In these cases, you will only need a mid-level understanding of statistics, linear algebra, and algorithms.
However, if your goals include inventing new techniques for organizing or interpreting data, you should pursue advanced math courses.
They will provide you with knowledge of biology and biological structures, understanding and utilizing data, and building programs supporting that data.
In this case, you may need to take further math courses to be able to work with the information.
Every academic institution offers different math courses for bioinformatics; however, the core content will remain the same.
In most bioinformatics programs, you will learn some or all of the following:
- Probability and Statistics: One of bioinformatics’ main goals is to spot significant changes or discrepancies in biological information. It can help researchers, scientists, and doctors answer many biological questions.
- Data Structures and Data Algorithms: Bioinformaticians should understand how to organize biological data so that researchers can quickly access certain types of information.
- Linear Algebra and Calculus: It is crucial to understand foundational mathematics to build software systems for complex data. Classes in linear algebra and calculus can also help with programming, modeling, and running simulations.
- Matrices: Matrices allow bioinformaticians to represent information properly, condense a large amount of information into data, and organize intersecting information without mixing them up. They can also let experts find new ways of interpreting information or extracting conclusions from data.
- Geometry: When you need to compare data points, sufficient geometry skills will come in handy. It is also helpful for any data analysis or interpretation.
- Discrete Math: Discrete math, especially graph theory, is essential for biology and bioinformatics applications.
- Math for Programming: Many of the above types of math help with programming for bioinformatics. Some advanced or specialized programs will also add denotational math, especially if you wish to develop new ways of analyzing or interpreting information.
Whether you are struggling with learning the math required for bioinformatics or you are excited to dive deeper into the content, there are plenty of online resources available:
- Algorithms for Computational Biology: MIT offers this online tutorial on the principles of algorithmic design and analyses of existing algorithms.
- Principles, Statistical and Computational Tools for Reproducible Data Science: Harvard provides this free class on computational tools necessary for programs such as bioinformatics.
- Statistics for Bioinformatics: John Hopkins Whiting School of Engineering introduces statistical methods for bioinformatics in this beginner course.
- Mathematical Thinking in Computer Science: University of California San Diego launched this class concentrating on discrete mathematics for computer science.
- Mathematics of Bioinformatics: Theory, Practice, and Applications: Many bioinformatics students recommend reading and studying this excellent book. It covers mathematics in its theories and applications directly to bioinformatics.
- Tutorials on Bioinformatics & Biomedicine: These helpful tutorials go hand in hand with the book listed above, Mathematics of Bioinformatics.
- Mathematical Foundations of Bioinformatics: University of Minnesota Duluth developed this online book for their introductory math course for bioinformatics.
- Abstract Math: Abstract Math is an excellent resource for anyone struggling with mathematical concepts and theories.
Bioinformatics is a relatively difficult academic program and career.
You must understand and work with complex data, unusual data formats, and large data sets.
Today, researchers and scientists collect biological data at a fast pace.
Depending on your particular job, you may determine how new techniques or programs can analyze, organize, or store new data types.
You may also contend with outdated software programs and continuously upgrade and optimize the systems to process new information.
For these reasons, you must take biology, chemistry, math, computer science, and possibly software engineering courses.
That said, passionate students will fare excellently in their programs and become successful in the field.
Likewise, if you struggle with any course content, you can find valuable resources online, follow workshops, take extra classes, or find academic tutors.
In bioinformatics, it is imperative to have at least a moderate understanding of most math subjects.
You will use math to understand the biological information and to create or optimize programs to process the biological data.
If you do not enjoy math, you may be able to find extra help through your specific academic institution or online courses and tutorials.
These resources can be an immense aid, primarily when you pursue more extensive or specialized math for bioinformatics.