Deciding between data science and bioinformatics can take time and effort.
They are both highly fascinating fields, and without any career experience, it can be hard to decide between them.
In this 8-minute read, we will compare and contrast data science and bioinformatics and give a ton of information so that you can make an informed decision that works best for you.
After this quick read, here are the things you’ll have learned:
- What Bioinformatics Is
- What Data Science Is
- How They’re Similiar
- How They’re Different
- Types of Data Science
- Types of Bioinformatics
- Schooling Needed For Both
- Coding Languages For Both
- Salary Comparison
- Future Outlook
- Bioinformatics as a career
- Should You Study Bioinformatics?
- Switching to Data Science From Bioinformatics
What is Bioinformatics?
Bioinformatics is a field of science that combines biology, computer science, statistics, and information technology to analyze and understand biological data.
It is used to study the structure and function of genes, proteins, and genomes in humans and other biological organisms – like mice.
How similar do you think we are to mice?
The answer may shock you.
Believe it or not, humans share 85% identical genomes, as there is the belief that we shared a common ancestor 85 million years ago.
Without bioinformatics, we would never know this!
While bioinformatics is great at analyzing the past, they’re also great at predicting the future.
With the help of data visualization and machine learning, bioinformatics can predict the effects of mutations on a protein structure, design new drugs or diagnostics tests, and analyze evolutionary relationships between species.
A big part of bioinformatics is data storage. These massive centralized databases enable researchers to quickly analyze large amounts of biological data, such as genetic sequences or gene expression patterns from their labs and others.
Bioinformatics is also concerned with developing methods for understanding the molecular cause of diseases and identifying potential targets for drug discovery. (This is what they use the mice for)
Bioinformatics is enormous – it plays a vital role in helping us gain insights into life at a molecular level and accelerate the discovery of new cures for diseases – saving lives.
Reference:
https://www.genome.gov/10001345/importance-of-mouse-genome
What Is Data Science?
Data science is a fascinating field that combines aspects of computer science, statistics, and mathematics to interpret any type of data.
Using analytical, business, and critical thinking skills, data scientists employ machine learning and other SOTA techniques to identify patterns in large datasets to make predictions about real-world events – usually to improve business KPIs.
For example, with a large enough dataset of consumer spending data, a data scientist could analyze and predict what products will be popular this season or whether interest rates on consumer loans should be dropped.
Data science can also be used for more broad and abstract tasks, such as deeply understanding social trends, stock market fluctuations, and upcoming marketing strategies.
Ultimately, the goal of any data scientist is to take information from any part of the business realm, analyze it and then turn it into invaluable insights that can help shape positive business decisions.
How is Bioinformatics Similar to Data Science?
While bioinformatics and data science don’t seem similar, they share many of the same things in practice.
Both data science and bioinformatics involve working with ample, complex data storage – usually in the same type of storage locations.
For example, most genome data for analysis sits in a database, and most business data for analysis sits in a database.
Even once the data is retrieved, these two specializations share many of the same techniques when working with their newly extracted data.
Both require understanding machine learning algorithms, statistics, and mathematics to try and make sense of the massive amount of data they’ve ingested – whether it’s genomic or marketing studies.
Both of these careers will have hands-on keyboard, needing strong coding abilities in various computer languages (we will discuss this later) to write code.
Finally, both disciplines need a general knowledge of software development tools for working in teams, such as git, big data processing, and other general data architecture designs.
While these two disciplines are very similar – some differences we go over in our next section can help you differentiate between the two.
How is Bioinformatics Different from Data Science?
As we’ve seen above – we know there are a LOT of similarities between bioinformatics and data science.
However, the differences between the two may help you make the best decision for your career.
Data science is a much broader field as it covers all data analysis and manipulation types, while bioinformatics is a subset of data science.
Since it’s a subset, bioinformatics will inherit many techniques but will be much more specialized in the biology and medical science community.
Another considerable difference many people leave out is that bioinformatics typically involves laboratory work.
This could include DNA sequencing, gene expression analysis, and building up databases that others in the community can use.
In my career (as an ex-data scientist), I never worked in a lab and focused mostly on writing code and building software.
The goals of the two careers ultimately drive this.
Data science is usually driven by some business KPI (profit or efficiency).
Bioinformatics is driven by saving lives through technological advancement; having a healthier relationship with academia and other parties within their specialized community.
What Are The Different Types Of Bioinformatics?
So, you’re more interested in bioinformatics?
Great, let’s talk about some options.
Structural Bioinformatics
Structural Bioinformatics focuses on the structure of biological macromolecules.
And while this probably seems evident from the name, it’s based on 3D renderings of these macromolecules, like proteins, nucleic acids, carbohydrates, and others, to drive innovation through visual analysis.
Most of their work will be in the Protein Data Bank (PBD) database.
Comparative Genomics
Comparative Genomics is another type of bioinformatics that examines whole genomes across multiple species.
Comparative Genomics is how we came to the conclusion above, where we found out that mice and humans are 85% similar.
Finding hidden commonalities amongst species allows us to unlock the “how” when it comes to life and a deeper look into our evolutionary past.
Functional Genomics
Functional genomics studies gene expression patterns and functional elements within the genome to understand the lineage of different biological processes.
For example, if you are interested in how cancer affects the genome of the whole biological system – a functional genomics specialization could be a great career choice.
Phylogenetics
Without studying the past, how can you understand the future?
Phylogenetics concerns family trees and how species have changed over time.
Taking a much more holistic approach to DNA sequencing, we can identify where family trees have deviated, common ancestors, and hidden biological keys that could unlock something from our past.
Phylogenetics focuses on much more than just humans, so if you are a plant and animal lover, phylogenetics may be an excellent place for you!
Biostatistics
Biostatics is bioinformatics without the emphasis on coding.
Biostatics is a good place if everything above has attracted you to bioinformatics, but you’re worried about the coding side.
What Are The Different Types Of Data Science?
Data science as a field is HUGE and can be broken down into THOUSANDS of different sub-components depending on what business domain you’re currently focusing on.
We’re not going to do that.
Instead, we’re going to keep it simple.
Data science is focused on data, and the two main categories of data are structured and unstructured.
Structured data is organized in a specific format, such as numerical or tabular, so it can be quickly processed and used for analysis.
This data has a “target,” meaning we tell our machine learning algorithm what to look for.
Unstructured data does not have a set structure or format, meaning the goal is usually loosely defined.
Unsupervised learning has no target; our machine learning algorithms are designed to find insights in data that the human eye can’t see.
This type of data comes from everywhere, including social media posts, emails, genome data, and billions of other sources.
How Much Schooling Is Needed To Become A Data Scientist or A Bioinformatics Professional
While you can become a data scientist or a bioinformatics professional with only a 4-year degree, many employers prefer a master’s degree.
Both roles are highly technical, and employers want to ensure you’re fully equipped with the education you need to handle their highly abstract and challenging problems.
Do Both Data Scientists and Bioinformaticians use Python?
Python is an essential programming language for data scientists and bioinformaticians – probably the most crucial skill in these careers.
Python is the king of machine learning, and both data scientists and bioinformaticians rely on Python to build their data pipelines.
While Python is the king of machine learning, it’s also the king of data analytics.
Even without machine learning, Python will be instrumental to both careers to analyze biological data such as genomic sequences and proteins or build a visual for a business KPI.
While Python is essential, both professions often require using SQL to extract data from their respective databases.
Therefore, having a solid understanding of Python and SQL is critical for anyone wanting to pursue a career in either field.
Data Science Vs. Bioinformatician Salary
While I’m used to reporting that data science has a much higher salary than its competitor – this time is different.
According to glassdoor, a data scientist can expect to bring home around $125,000 a year, while bioinformaticians bring home a whopping $140,000 yearly.
Both careers will provide an excellent salary!
References:
https://www.glassdoor.com/Salaries/bioinformatics-salary-SRCH_KO0,14.htm
https://www.glassdoor.com/Salaries/data-scientist-salary-SRCH_KO0,14.htm
Other This Or That Articles
We’ve written a couple of other articles that are very similar to this one; check out:
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- Data Science vs. Economics
- Machine Learning vs. Human Learning
- Heuristic Algorithm vs. Machine Learning
- Machine Learning vs. Programming
- Data Science vs. Operations Research
Frequently Asked Questions
Which Has A Stronger Future, Data Science Or Bioinformatics?
Data science and bioinformatics have both seen tremendous growth in recent years. As organizations strive to become increasingly data-driven, the demand for data scientists and bioinformaticians continues to increase. Since bioinformatics is a subset of data science, and data science is broader, there will always be more opportunities in data science (a bigger field) than bioinformatics.
Is bioinformatics a good career choice?
Bioinformatics is an incredible career choice for those interested in the intersection of biology, statistics, and computer science. With the growing demand for medical research, bioinformatics offers a unique opportunity to help advance medical knowledge and shape our future. It also provides higher salaries than data science, making it an attractive option for those who want to specialize.
Are bioinformatics and computational biology the same thing?
No, bioinformatics and computational biology are not the same things. The most significant difference between these two is the level at which they play. Computational biology is much broader and more abstract, dealing with more theoretical concepts within biology. Bioinformatics deals purely in reality – focusing on historical data and technological techniques to derive insights.
Can I switch to data science from bioinformatics?
Since bioinformatics is a subset of data science, switching from data science to bioinformatics is very straightforward. Instead of learning new technologies, data scientists will have the advantage of only learning the biological and medical side of things. While this isn’t an easy task, it does give you an edge over newcomers to the field.
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Bioinformatics is data science, unlike biotechnology, bioinformatics is purely dry lab work which predominately focuses on developing algorithms and codes to process, analyze, and assemble biological data. Bioinformaticians uses already available tools and terminal(shell) to execute data but have minimal understanding and do not code. Biostatistics on the other hand requires coding in the R language as well as SAS and ANOVA at the advance level so yes programming is used in R. A Bioinformaticists is a programmer that utilizes Python, R, Perl, Java, ML, ANN, database systems, etc. to visualize and manipulate biological, chemical, and pharmaceutical data. To truly master bioinformatics or biostatistics you need to be proficient in a programming language. My advice to anyone getting into Bioinformatics is to master R, Python, and ML because they are very few positions for just Bioinformaticians and typically most employers want you to have a master’s in bioinformatics but if you master the primary programming languages you should be able to find work in data science fairly easy.