Top Programming Languages ​​to Learn in Life Sciences



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As technology is increasingly integrated with research and development, life sciences companies are increasingly looking for candidates who are proficient in computer programming.

However, not every kind of programming experience helps. There are certain languages ​​that programmers must know in order to be successful in this industry.

For reference, here are the best programming languages ​​to learn for those starting or continuing a career in the life sciences industry.

Popular programming languages ​​in dry labs

For decades, research and drug development have relied on experiments performed in wet labs—laboratories that utilize physical samples, chemicals, and liquids. Biopharmaceutical companies are now also filling the seat of computational dry labs.

Dry labs utilize computer models and computer-assisted experiments to generate and analyze data. These dry labs save companies a lot of money, manpower, and time by testing hypotheses on computer models before moving to traditional wet lab methodologies.

Knowledge of computer programming languages ​​is required to conduct experiments in the dry lab. These languages ​​are typically text-based and used to communicate with computers and perform computational analysis.

Learning a programming language helps job seekers establish themselves in the biopharmaceutical dry lab. Additionally, learning to code will help Rife in other applications in his science industry, such as analyzing data from wet labs and contributing to data management.


Python is a widely used, high-level, general-purpose programming language that is easy to learn. It is popular within the scientific community for providing a vast support network for learning to accompany its coding libraries and tools.

This programming language emphasizes code readability by using English words that concisely define the function and purpose of the code. This makes it easier for people new to programming to understand how the code they are writing interacts with the computer.

Python’s support for structured, object-oriented, and functional programming makes it usable in a wide variety of applications. In the life sciences industry, Python is often used for genome sequencing, processing large chemical libraries, machine learning purposes, or other biological computations.

There are many resources for learning Python, such as Python for the Life Sciences. This is an introduction to coding in Python for people in the industry.

R for data analysis

If you’re looking for a role in statistical computing or data analysis, you’ll want to know about R. R is considered one of the most popular languages ​​for biostatisticians.

R is a programming language primarily used for data analysis. It is used to import quantitative datasets, clean them, and perform statistical analyses.

Another major selling point of R is its ability to create data visualizations and static graphics.

Because R is free and open source, businesses can take advantage of its data analysis capabilities and avoid paying high prices for statistical software. R is also useful for tackling the analysis of large datasets such as those generated from proteomics.

Both Roche and Greentech have previously demonstrated the use of R for analyzing clinical trial data.

SQL for data management

If you’re dealing with databases, you’re more likely to use SQL (sometimes pronounced “sequel”).

Structured Query Language (SQL) is a programming language designed for managing and communicating with databases. It helps in updating, retrieving, and manipulating data from large databases.

SQL is an old programming language with worldwide adoption in almost every industry that uses data in relational databases. In the life sciences industry, SQL can be used for laboratory information management and other situations involving large databases.

MATLAB for clinical research

Although MATLAB is usually thought of as an engineer’s only language, it also holds its place in many other scientific fields. MATLAB is a programming language that allows you to implement algorithms, plot data, and supports parallel computing.

In the life sciences, MATLAB can be used to simulate pharmacokinetics and pharmacodynamics using its platform, SimBiology. MATLAB is also useful in pharmaceutical manufacturing and helps optimize yields during pharmaceutical manufacturing.

MATLAB can also contribute to the analysis of data from wet labs. Using this language, you can analyze entire slide data and perform cell classification and radiomics analysis.

MathWorks, the company that owns MATLAB, provides an introductory guide for life sciences.


JavaScript is an object-oriented programming language widely used in web pages, but its ubiquity continues to grow. It helps you control multimedia, animate images, and create interactive components on your website.

It’s not as popular as other programming languages ​​in the industry, but its popularity is growing. Some industry professionals are capitalizing on the utility of JavaScript by creating BioJS, an open-source JavaScript framework for visualizing biological data.

JavaScript is also useful when using applications such as Qualtrics, survey creation software. Using JavaScript, you can create dynamic, moving components in your survey.

Other software to consider

Programming is popular, but it’s not all industry analytics. Instead, companies may choose to use software that streamlines the experience and, in some cases, is easier to use with a visual user interface.

A popular choice for data analysis and management is SAS (Statistical Analysis Software), a suite of statistical software. SAS has many applications, including data management, predictive analytics, and advanced analytics.

SAS promotes the Life Science Analytics Framework, a computing environment designed specifically for clinical research. This framework accelerates time to market, incorporates regulatory compliance and controls, and improves efficiency.

how to choose

Not all programming languages ​​are created equal and it can be hard to know where to start. If you’re interested in learning programming in general, or if you’re a new learner, you should start with Python or JavaScript. These languages ​​are used in a wide variety of applications and are easy to learn.

If you’re interested in learning to program just for statistical analysis, you should start with R. In general, if you’re interested in learning for a particular job, check the language companies typically use in their scientific publications and presentations.

There are many resources available for new learners. For example, you might consider using an IDE or integrated development environment. These software applications can take the complexity out of learning, debugging code, and building software. They are designed to provide a visual component to the coding process and give users built-in tools to make coding easier.

Additionally, many programs are free and open source. There are plenty of tutorials online, and if you get stuck, sites such as his StackOverflow where experienced programmers can provide answers and help with programming problems.

Learning a new language isn’t always easy, so start with a skill or project in mind. You can also find coding buddies to help bounce ideas off when you’re stuck with abstract concepts or syntax.


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