As a bioinformatics student, you will focus your studies on mathematics, computer science, and biology.
But since you handle large amounts of complex data, you may wonder if you can become a data scientist.
You will be happy to learn that the transition from bioinformatics to data science can be easy.
However, the ease may depend on a few factors.
Follow along to learn how to become a data scientist and if it is worth the switch.
Data scientists are experts in analyzing data to find trends, discrepancies, or significant insights. They can work in any field as long as they have domain knowledge.
And Bioinformatics is essentially data science, only applied to biology. So, bioinformatics experts usually have no problem working as generalist data scientists.
Depending on the field, they may need to learn subsequent specialized applications to work as a data scientist.
However, if they wish to work in generalized domains which require minor specialization, they may be able to become a data scientist immediately.
In particular, bioinformatics specialists may have the easiest time becoming data scientists.
Bioinformatics specialists analyze and interpret the biological data stored in software systems.
On the other hand, bioinformaticians may understand the biological data, but they concentrate on building software systems to support it rather than providing scientific insights from the information itself.
Therefore, bioinformatics specialists already understand data systems and the analytical mindset necessary to become successful data scientists.
Learn more: Can Bioinformatics Do Biomedical Engineering?
Bioinformatics specialists seek to provide scientific insights rather than consumer or business conclusions.
They apply data science to the bioinformatics domain using two main types of data:
The first type is genomics, the structures, functions, and changes of complete sets of DNA or RNA.
The second type is proteomics, protein structures and functions under specific conditions.
Storing, organizing, analyzing, and interpreting this data significantly benefits advances in image processing, healthcare, disease management, and pharmaceuticals.
Most data science academic programs teach you general systems and computer science skills, business-oriented analysis, and specialized domain knowledge.
Bioinformatics programs teach you field-specific systems and biological and medical-oriented analysis.
Therefore, data science within bioinformatics does not apply in the same way as other fields.
Since bioinformatics and data science are similar fields, it can be challenging to know which is the best choice.
Academically, data science will be a more manageable program as it focuses more on general mathematics, computer science, and data analysis.
Bioinformatics is a more complicated program, requiring extensive study in math, biology, computer science, and software engineering.
Career-wise, job outlook opportunities for both groups align with those for computer and information technology specialists.
Before 2030, the government expects a 22% growth in job opportunities.
Since career statistics are similar, the best choice depends on your particular passions.
Bioinformatics may be the perfect profession if you enjoy biology, medicine, or healthcare.
Alternatively, if you prefer business-oriented work, data science may be an excellent choice if you prefer business-oriented work.
Bioinformatics has many different job titles, including:
- Bioinformatician: The most popular type of job in bioinformatics is a bioinformatician. They are responsible for managing databases and developing systems.
- Bioinformatics Specialist: A bioinformatics specialist uses existing or new software programs to analyze data and extrapolate conclusions.
- Bioinformatics Engineer: A bioinformatics engineer designs and builds new algorithms, techniques, and software programs for storing, organizing, and processing data.
- Biostatistician: A biostatistician analyzes data and prepares easy-to-read reports.
Data science can lead to different job titles, such as:
- Data Scientist: A data scientist collects, analyzes, and makes conclusions from specific data sets.
- Data Architect: A data architect designs and builds databases and data processes.
- Database Administrator: A database administrator oversees and manages databases.
- Data Engineer: A data engineer designs and builds software systems that store, organize, and retrieve data.
How To Become A Data Scientist
Prospective data scientists can follow a relatively clear-cut academic path to fulfill their goals.
They can earn an undergraduate degree in data science, ensuring they acquire substantial mathematics and computer science knowledge.
Important content includes programming languages, machine learning, statistics, software engineering, data mining, data warehousing, data analysis, and risk analysis.
During their undergraduate degree, data scientists can specialize in a domain or pursue their specialization during a graduate degree.
Finance, manufacturing and sales, media and communications, and healthcare are all popular fields.
It is important to complete a Master’s degree in data science or data science as applied to your domain, as most companies require or value a higher academic degree.
Data science can be challenging at times. That’s why you need excellent resources at your disposal. A few of the most helpful online tools include:
- Coding: Learn some of the most important programming languages for Data Science.
- Linear Algebra for Beginners: Grasp foundational math theories and principles before diving deeper into mathematics for data science.
- Data Science Specialization: Gather all the elements you need to create a solid foundation in data science.
- Harvard Data Science: Take an online professional certificate in data science.
- A Complete Tutorial to learn Data Science in R from Scratch: Learn data science in R language, covering topics such as predictive modeling, data exploration, and machine learning.
- IBM Data Science: Earn a professional certificate from IBM with this course on data science and machine learning.
- Data Science Math Skills: Tackle the mathematics portion of data science with ease, courtesy of this course from Duke University.
Whether you pursue bioinformatics or data science, you will perform work oriented around data analysis.
Choosing an academic and career path that you find interesting is best, as both fields have challenging work.
In most cases, if you choose bioinformatics and later wish to transition to data science or vice versa, you will find it easy to accomplish this.