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Computational Biology is a multidisciplinary approach to applying data-scientific methods, processes, or theories to the study of biological systems. The field includes foundations in Mathematics, Statistics, Chemistry, Genetics, Genomics, Computer Science, Evolution, and related disciplines.

An example of Computational Biology is performing experiments that produce data—building sequences of molecules, for instance—and then using methods such as machine learning to analyze the data.

When mastered, Computational Biology enables successful learners to bring drug discovery and disease prevention expertise to Biotechnology, Pharmaceuticals, and other essential fields.‎

As Data Science roles pertaining to Biology become increasingly in-demand, learners exploring Computational Biology can find opportunities in commercial, academic, and government domains. The positions learners can look forward to include Computer and Information Research Scientist, Computational Biology Professor, Software and Instrumentation Test Engineer, Bioinformatics Scientist, and related roles.

Some industries in which Computational Biology learners will find exciting opportunities include Medical Device and Equipment, Research and Testing, Agricultural Chemicals, Pharmaceuticals, National Institutes of Health, and more.‎

Courses in Computational Biology offered through Coursera explore various methods for extracting data from biological experiments. Learners discover how algorithms, data structures, and other computational methods can help them analyze DNA sequencing data. The basics in biological systems are covered so learners have a foundational understanding of DNA, genomics, and DNA sequencing. Hands-on learning of data science tools is available in many courses as well, where learners use Python, R Programming, and more to analyze real genomes and DNA sequencing datasets.‎

Computational biology sits at the intersection of machine learning and life science, so if you possess skills in computer science and any of the various disciplines of biology, you’re ready to start learning computational biology. Having a working understanding of biology, pharmacology, and genetics can help you gain a foothold in learning computational biology, and the deeper your knowledge of those fields, the better off you’ll be in your studies. You can also benefit from a background in coding and computer programming, especially if you have experience using discrete mathematics for research and data analysis. An understanding of statistics can also give you an advantage in studying computational biology because you can apply the principles of statistics and data analysis to problem-solving within scientific disciplines.‎

Since computational biology—also known as bioinformatics—is a scientific discipline that relies on the analysis of large amounts of data, people with analytical natures or backgrounds in statistics are well-suited for careers in this area. Individuals who are passionate about life science and want to apply technology to studying its various fields will enjoy jobs in computational biology. Computer programmers who have an interest in biology or genetics can combine their skills in building data structures with their passion for bioinformatics. Whether you want to work in academic research or for a medical corporation, you can put your scientific and computational knowledge to work in a career in computational biology.‎

Computational biology might be the right field for you if you’re interested in biology and genetics beyond family trees and the insights from DNA test kits ordered online. If you have the desire to help develop the next revolutionary prescription or a passion to use data to help solve scientific issues, learning computational biology could be a good fit for you. You can take the skills in data analysis and computer programming that you learn studying computational biology to conduct research that helps lead to scientific breakthroughs. From pharmaceutical studies to academic research, learning computational biology offers rewarding opportunities for those who want to combine computer skills with scientific exploration.‎