Skills you'll gain: Bioinformatics, Probability & Statistics, Theoretical Computer Science, Algorithms, Computer Programming, Graph Theory, Mathematics, Computational Thinking, Entrepreneurship, Leadership and Management, Problem Solving, Research and Design, Combinatorics, Business Psychology, Communication, Customer Support, Journalism, Marketing, Microsoft Excel, Supply Chain and Logistics
Beginner · Specialization · 3-6 Months
Skills you'll gain: Bioinformatics, Computer Programming, Probability & Statistics, Python Programming, Statistical Programming, Algorithms, Theoretical Computer Science
Beginner · Course · 1-4 Weeks
Skills you'll gain: Bioinformatics, Probability & Statistics, Algorithms, Theoretical Computer Science, Databases, Human Computer Interaction, Machine Learning, Markov Model, User Experience
Mixed · Course · 3-6 Months
Skills you'll gain: Probability & Statistics, Bioinformatics, Computer Programming, Statistical Programming, Python Programming, Data Analysis, R Programming, Theoretical Computer Science, Algorithms, Business Analysis, General Statistics, Statistical Analysis, Data Analysis Software, Computer Programming Tools, Biostatistics, Other Programming Languages, Software Engineering, Big Data, Data Management, Data Visualization, Amazon Web Services, Cloud Applications, Cloud Computing, Cloud Platforms, Statistical Tests, Basic Descriptive Statistics, Computational Thinking, Exploratory Data Analysis, Human Computer Interaction, Software Architecture, User Experience, Accounting, Advertising, Communication, Computer Graphics, Financial Analysis, Graphics Software, Marketing
Intermediate · Specialization · 3-6 Months
Skills you'll gain: Probability & Statistics, Bioinformatics, Data Analysis, Business Analysis, General Statistics, Statistical Analysis, Algorithms, Computer Programming, Software Engineering, Statistical Tests, Theoretical Computer Science
Mixed · Course · 1-4 Weeks
Skills you'll gain: Python Programming, Computer Programming, Statistical Programming, Basic Descriptive Statistics, Computational Thinking, Data Analysis, Exploratory Data Analysis, Bioinformatics, Probability & Statistics
Mixed · Course · 1-4 Weeks
Skills you'll gain: Business Analysis, Data Analysis, General Statistics, Probability & Statistics, R Programming, Statistical Analysis, Statistical Programming, Bioinformatics, Biostatistics, Big Data, Data Management, Data Visualization, Accounting, Advertising, Communication, Computer Graphics, Financial Analysis, Graphics Software, Marketing
Mixed · Course · 1-4 Weeks
For anyone interested in pursuing a career in bioinformatics, some of the best free courses they can access are Introduction to Bioinformatics, Whole Genome Sequencing of Bacteria, Bioinformatics Algorithms Part 1, Metagenomics: Analysis of Genetic Material from Environmental Samples and Data-Driven Models for Precision Medicine. These courses offer a great introductory to advanced understanding, providing learners with the fundamentals in bioinformatics.
For those just starting out in bioinformatics, Coursera offers several great courses to get you started. The Bioinformatics Specialization provides an in-depth understanding of the essentials of bioinformatics. For a more targeted approach, Bioinformatics Methods 1 and Bioinformatics Methods 2 focus on the application of bioinformatics tools to analyze genomic data. Plant Bioinformatics Methods is a great resource for those interested in working with plant genomics and DNA Analysis provides an introduction to analyzing DNA sequences.
If you're looking to gain more advanced knowledge in the area of bioinformatics, some of the best courses are Genomic Data Science Specialization, Accessing Bioinformatics Databases with BioPython, Data, Genes, and Medicine, Systems Biology Specialization and Plant Bioinformatics.
Bioinformatics illustrate a unique interdisciplinary approach to computing in modern biology. They combine Computer Science, Information Engineering, Mathematics, and Statistics to analyze and interpret biological data. Computer programming methodologies, such as Python, are applied in Bioinformatics for the purpose of managing the structure, function, evolution, mapping, and editing of genomes—or, an organism’s complete set of DNA.
Bioinformatics are critical to understanding normal versus abnormal genomes, and are even said to have sparked a revolution in medical discoveries. That is likely because Bioinformatics enables learners to leverage data and information from genomic datasets, helping to identify the genetic basis for diseases and providing a clearer path to finding treatments.
As big data proliferates in all fields, many new job opportunities lie in Data Science and Bioinformatics. Career opportunities start at Bioinformatician and branch out into careers in Bioengineering, Computational Science, Software Engineering, Machine Learning, Mathematics, Statistics, Molecular Biology, Biochemistry, Information Technology, Clinical Research, and other fields that heavily rely on information derived from genomes.
Learners interested in Bioinformatics will find hands-on courses that put them at the center of genome-related challenges.
Covering the basics, you’ll investigate DNA replication, the role of DNA patterns, and other ways to garner information from DNA. Methodologies you can explore include multiple sequence alignments, phylogenetics, gene expression data analysis, protein interaction networks, and more.
Learners can also experience real-life Bioinformatics scenarios within scientific settings. You'll discover algorithms for solving various biological problems, and take on assignments designed to help you implement these algorithms in Python.
Successful learners will be able to address many questions in modern biology that need a computational approach in order to be answered.
A background in biology, math, or programming can be useful in the study of bioinformatics. Coursework in biology, specifically molecular biology, can help you understand some of the terms you will encounter as you learn bioinformatics. You'll likely find some knowledge of basic calculus and linear algebra as well as descriptive and inferential statistics beneficial for learning and practicing bioinformatics analysis methods. Additionally, experience with database management or using a programming language like Python, R, or Java may also be useful as you explore bioinformatics methods and tools researchers use in their work.
People working in the field of bioinformatics come from diverse backgrounds, but what they have in common is a passion for analyzing data and a desire to understand the role of DNA in medicine. Some start their careers as computer scientists who decide to apply their skills to the study of molecular biology. Others are biologists who want to take a more active role in the analysis of laboratory discoveries. Many of them enter the field because it combines their interest in data science, programming, and biology. They typically thrive in environments where they learn new information and have a chance to apply their discoveries to real-world issues.
If you're fascinated by the natural world and enjoy discovering what makes people tick on a molecular level, then learning bioinformatics may be right for you. You'll be able to take a closer look at topics like DNA and RNA sequencing, protein interaction networks, and epigenetic patterns. Learning bioinformatics can help prepare you for a career shift if you already have a background in computer science or programming and want to apply it to research used to treat diseases like cancer. Bioinformatics is a broad field, so you have an opportunity to develop and enhance your skills that can be used in a variety of settings and roles, from laboratories to data warehouses.