The PhD in data science. It seems like the Holy Grail.
But do you really need one to be a good data scientist?
Today’s post sheds light on when it’s ok to not have a PhD and when it is almost required.
I will help you decide if you should go for the PhD or if you are better off without one.
First of all, let me say this: Any degree, Masters or PhD, is more or less only a certification of your skills. It attests that you are able to deep dive into a topic in a scientific way.
What makes the PhD special is the level of scientific working and theoretical research required. The main difference between a MSc thesis and a PhD dissertation is how much it contributes to the research in a field.
A PhD dissertation has to contains your original ideas and findings.
This original content needs to result in totally new scientific findings. Findings that help advance the scientific knowledge in this field.
Also, although you might do some hands on research during the dissertation it is mainly theoretical in nature.
A masters thesis on the other hand, is mostly showing the current state of science. It contributes to scientific research only in a small way.
The thesis is more about showing that you are skilled in a field. Showing that you have some original ideas and that you can implement these ideas in a real world setting.
Applying vs Researching
There is a big difference between applying and researching. Take machine learning for example.
Applying an algorithm to solve a business case requires totally different skills then researching new algorithms.
A researcher is usually not going to realise the finished solution. Researchers know every aspect of the field they work in.
He knows the theory behind the algorithms in detail. He is able to modify existing algorithms to come op with with totally new and original ones.
Very often researchers are going to write papers on their findings to advance the global scientific knowledge. Check out Google research papers how this process looks https://research.google.com
Appliers on the other hand need mostly different skills. Although they need to understand how something works the main focus is to implement a solution for a specific use case.
Applying an algorithm requires to do a lot of work around the actual core machine learning algorithm:
How is data coming in? How does the data need to be formatted? How do I need to preprocess it that the algorithm can use it? How do I need to post-process the data so that the users can make sense of it?
How can I make all the above work in an automated way to provide a solution for the problem.
You don’t need a PhD!
Ask yourself the one question: “Do I want to apply or do I want to do research?”
You can be an amazing data scientist by applying existing methods to solve problems with data science.
That’s why I say a clear No.
You don’t need a PhD to be a good “applying” data scientist.
Research on the other hand is different. You almost definitely need a PhD to be a amazing Researcher.
Doing research, creating new algorithms and writing scientific papers. That requires not only far deeper knowledge of a field like for instance deep learning.
Research requires skills for scientific working that you learn while doing a PhD.
I for instance don’t have a PhD and I never wanted to do one. I like building solutions that solve problems for customers.
I would be a horrible researcher! And that is totally ok.
How about you?
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PS: A critical thought.
I have the feeling that a lot of people see the PhD as their path to money. When in fact the idea behind the PhD is a noble one:
Advance the knowledge of the scientific community. Help make the world a better place.
What is your take on PhD and MSc or BSc? Share it with me and the community in the comments section below!
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