All industries have rapidly adopted data-driven techniques into their daily operations, therefore creating abundant job opportunities for candidates who possess skillset in mathematics, statistics and programming. Having these unique skillsets will open avenues to enter just about every industry, and that includes biotechnology.
Data science in biotechnology has been popularly discussed in recent times. The application of data science in biotechnology is not limited to mining big data sets. The emergence of bioinformatics has pushed data scientists to not only answer the questions posed by scientists and clinicians, but also define those questions. Data science in biotechnology is now a new field to research by itself.
So whether you’re striving for a career in biotechnology or eager to learn some new data science skills, we’ve got the basics covered for you. Read on to find out more.
1) What is biotechnology?
Before we dive deep into the crux of bioinformatics and study the applications of data science in biotechnology, let’s first understand the meaning of biotechnology.
In scientific terms, biotechnology is, “any technological application that uses biological systems, living organisms, or derivatives thereof, to make or modify products or processes for specific use.” Our biological knowledge has significantly improved over the years as we learn more about molecular interactions at genomic level and with simultaneous advancements in technology has provided new avenues for scientists to use predictive models in determining the results of manipulating cellular realms.
Here are the branches of biotechnology with a few examples of their application:
- Medical – stem cell research, gene therapy, vaccines
- Agriculture – genetically modified crops, elimination of chemical pesticides
- Industrial – textiles, bio-fuels, detergents
- Environmental – biodegradation of organic matter, detoxification of wastewaters
- Marine – exploration and harnessing of marine materials for various uses
- Animal – creation of transgenic animals, development of microorganisms.
2) The role of data science in the field of biotechnology
Essentially, biotechnology professionals use advanced statistical analysis to study molecular biology. They are typically data scientists within a specific sector. Both biotechnology professionals and data science professionals are required to have a strong understating of different research designs, mathematics, statistics and programming.
Biotechnology, like any other field, is getting overly inundated by vast amounts of data at its disposal. Researchers in the field of biotechnology are constantly under time pressures to derive results which usually can go on for years. Data science when applied to clinical trials and experiments, speeds up the process of identifying the source of errors with ease. Data science helps researchers build predictive models and provide information that will aid them in achieving desired results from an experiment. An underlying knowledge of collection methodologies, storage and algorithm analysis of data is an essential skill set for a modern-day biotech professional to possess in order to advance your career in the field.
3) Will getting trained in data science skills help me perform better as a bioscience professional?
As discussed above, data science has marked a strong presence in the field of biotechnology. Biotechnologists face the daunting task of going through vast amounts of data on a daily basis. The molecular world and larger environments are dynamic in nature with each containing massive volumes of quantitative data and identifying which factors are more likely to have a particular effect needs a lot of computational effort.
Biotechnologists in today’s world need to have a foundational knowledge of data science that will make their day-to-day tasks easier, efficient and quicker. With the biomedical research space developing, biologists have realised the importance of adopting various tools and techniques from areas like Machine Learning, computational chemistry, mathematics, statistics and Physics. Knowledge of programming tools such as Python, R and C++ and databases such as SQL are just a few of the must-have skills for every biotech professional to excel in their career.
4) Understanding the applications of data science tools and techniques in the field of bioscience
The biotechnology field has seen rapid adoption of data science tools and techniques to carry out different types of research – from sequencing genomes, discovering new diagnoses to genetically modifying seeds.
Let’s look at a few applications of data science in the biotechnology space:
- Genomics: As a field, genomics has benefitted a lot in terms of reducing cost and time to sequence genomes after adopting big data. Previously, it took scientists 13 years and close to 3 billion dollars to sequence the first human genome. Since then, the cost and time needed to sequence a genome has dropped drastically. In 2016, a genome was processed for just $1,000 and with the technology advancing at the speed of light, this process is expected to happen within a matter of few hours.
- Pharmaceutical research: Present-day pharmaceutical research processes depend heavily on molecular data modelling systems that are based on libraries containing millions of chemical compounds. This model has helped save millions of dollars in the cost of development and speed trials to get approvals and distribute lifesaving drugs to the market at a much faster pace.
- Healthcare: Data science tools help in securely storing volumes of data associated with electronic medical records. Additionally, that data is also used to improve predictive diagnosis and identify effective courses of treatment.
- Environmental Science: Data science skills play a crucial role in environmental studies by supporting the design of monitoring systems, and working towards integrating and comparing data against historical observations. In addition, predictive analysis is helping bioscientists to understand factors directly or indirectly related to climate change.
5) If I have a background in biotechnology, can I switch to data science?
Transitioning from bioscience to data science might sound really daunting at first. But if you have a closer look at the roles and responsibilities of a biotechnologist and a data scientist, you will see there are plenty of skills these professions have in common, providing you with a range of potential career options.
Firstly, let’s explore the skillset required in biotechnology.
The tasks and responsibilities of a biotech professional might vary across the board, but they generally include:
- Data processing and quality checking specific to the domain
- Data transformation and filtering
- Data visualisation, data integration and reporting skills
- Basic knowledge of programming languages like Python and R
- Ability to articulate data-driven insights into simple presentations.
- Scientific curiosity and drive to solve problems related to medicine and biology
Whilst a data science skillset includes things like:
- Advanced programming skills
- Machine learning tools and techniques
- Statistics capabilities
- Data wrangling, data processing, data formatting and transformation
- Visualisation and communication of data insights
If you remove the domain knowledge from the bioscience skillset you will be left with a data science skillset. Professionals looking to move from biotechnology to data science will see a smoother transition since they already possess a number of critical skills that a data scientist requires.
If you’re considering this type of career transition, you will most likely need to upskill and adapt to the data organisation and distribution process of employers of a different domain. Additional skills in data science tools and technologies and domain-specific knowledge will help you smoothly transition career paths.