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Bioinformatics vs Data Science

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Basil

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The worlds of Bioinformatics and Data Science share a lot of commonalities. Although one focuses more on biological sciences than the other (Bioinformatics), they still use a lot of the same programming languages, software, and general principles. In this article, we go over exactly the differences and similarities between Bioinformatics vs Data Science and show you which path is right for you!

What is Bioinformatics? What is Data Science?

In a broad sense, Bioinformatics is the field involving the use of tools, software, and programming languages to understand and interpret biological data. Data Science is the field involving the use of similar tools and programs, but to understand data in general.

In terms of programming languages, some examples of what Data Scientists and Bioinformaticians use could include Python, PERL, or Java. For software and tools, some examples are R, SAS, Pandas, Apache spark, and Tableau.  

Bioinformatics VS Data Science

A generalized image to give an overview of Data Science vs Bioinformatics

 

Two Fields, One Common Goal

Although Bioinformatics and Data Science have many differences, there’s still somewhat of a same underlying goal; using algorithms, tools, and programs to understand and process data. Now if you are a Bioinformatician, that might mean using instruments to help you understand biological data, whereas a Data Scientist may be using similar tools to understand business or marketing data. Does this mean only a Bioinformatician can analyze biological data? No! Both Data Scientists and Bioinformaticians can handle all types of data, but Bioinformaticians have more of a focus on biology than Data Scientists do. 

Which Should You Major or Focus In?

Up until the last couple of years, there was no such thing as a Data Science degree or major. That has changed with the popularity of the field growing at astronomical levels. The answer to whether or not you should major in Bioinformatics, Computational Biology or Data Science lies on what type of career you’d like to pursue. If you want to focus more on the biological science side of things, pursue Bioinformatics or Computational Biology, which gives you a firm grasp on the sciences needed to handle large biological data. If you want to focus purely on managing data for all disciplinaries, and have no interest in broadening your biological skill-set, err on the side of a Data Science degree.

Once again, we're not saying that Bioinformatics or Computational Data majors/degrees do not give you ample knowledge or handling all types of data. However, a lot of your time in these programs is spent going over biological and chemical systems, so you need to have a passion in these fields, or else you’ll not be enjoying yourself.

Just to elaborate on this point, my first year as a Bioinformatics Masters student included challenging courses on human genetics, molecular and cellular biology. and biological research methods. Someone without any passion in these subjects would have had a torturous time! 

At the End of the Day, It's Not Your Degree; It's Your Skills

The biggest takeaway message we have is that it ultimately doesn’t matter what degree you chose, but the skill sets you gain from these majors. Are there jobs that have the requirement of a particular type of degree? Absolutely. These jobs are typically the exception. Instead, most jobs want a set of skills, which anybody can develop regardless if your major is in Data Science or Bioinformatics. Learn as much as you can and hone your skills, and you’ll find that you can make it in all sorts of data-oriented jobs.

Recommended Resource: 

If you51qCDtgoiYL._SX380_BO1,204,203,200_.jpg're looking for a great book that can help you bridge the gap between Data Science and Bioinformatics, I highly recommend O'Reilly's Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools.  This a really versatile book, that goes over a lot of important topics. Everything from the handling of sequencing data, to working with actual pipelines. Even if you err towards of Data Science, I still think this book can be an extremely valuable resource. If you do end up checking out the book, please let me know what you think of it in the comment section below! 

 

 

About the Author

24775259_10209011730644482_3671056967541215844_n.jpgBasil Khuder is the director and founder of YDSOA. He started YDSOA in 2015, hoping to create an online community for those new to the fields of Data Science and Informatics. When he's not running the organization, he's busy with his research and studies as a Doctoral Bioinformatics student at Iowa State University. You can follow Basil through any of his social media accounts.

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Thanks a million times for these lucid explanation, I have been in a dilemma over which to chose

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I would also say that Data Science has become heavily enriched in Artificial Intelligence, more so than I have seen in Bioinformatics or Computational Biology. 

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My actual line of thought is whether I can get a straight data job with a bioinformatics degree, which I'm imagining is easier to come by.  What I'm actually exploring are programs that are in Analytics / Data Science, but with a concentration or electives pulled from Bioinformatics / Computational Biology.  My background is in Environmental Science, so getting a business background is helpful, but my passion is along biology / chemistry, and I have an interest in social science / academic applications as well.  Thoughts?  Anyone else?

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On 10/29/2017 at 12:37 PM, Freddy said:

I would also say that Data Science has become heavily enriched in Artificial Intelligence, more so than I have seen in Bioinformatics or Computational Biology. 

I would like to see the data behind this statement.  I do not find this statement necessarily true, specially coming from the bioinformatics side. 

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