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Why I Chose a Master’s instead of a Ph.D. for AI and Data Science
The top 3 factors for choosing between Master’s and Ph.D degrees
In my previous post, I have described my experiences in getting an undergraduate degree in Computer Science. I also promised that I am going to let you guys know what I’m going to do next. So… [drumroll]… I will be heading to Carnegie Mellon to study for a Master’s in Computational Data Science!
For me, the decision to do a Master’s was not an easy one. In fact, I always thought I’m going to do a Ph.D. In this post, I hope to share the key differences between the two programs and what ultimately led me to apply to a Master’s program.
When I was first starting out, I had the idea that I wanted to be at the top of the AI & ML field. That means creating the best image-recognition model, the most interactive chatbot, or the most performant neural network for autonomous driving. In other words, I want to be able to learn a specific field deep enough so that I will be able to become an expert in it and come up with completely novel algorithms that can perform at the state-of-the-art. And I thought, a Ph.D. was the way to go because it involves research.
Well, I wasn’t wrong, because if you think about it, research is the process of coming up with new models or algorithms that either improve upon an existing work or deal with an interesting problem not yet been actively worked on by the global research community.
However, after nearly two years of exploring during my undergraduate, I realized that a Ph.D. might not be the ideal path for me. I considered 3 main factors when deciding between the two options.
Engineer vs Research
While a Ph.D. focuses on doing intensive research in a specific niche field, most (professional) Master’s programs emphasize equipping you with the practical skillsets to prepare you for the workplace. After conducting research on numerous projects on AI & ML, I gradually realized that I might not enjoy research as much as I do building products. The satisfaction I get from improving the state-of-the-art by 1% cannot be matched with the full-on excitement when seeing a useful and functional product come to life. Simply put, I think I just enjoy engineering more than research.
People vs Knowledge
In my opinion, I believe that building products have a greater contribution to people while conducting research contributes to the knowledge of the corresponding field. Although it can be argued that research also contributes to people and society, I think products and apps are a more direct way of providing assistance and convenience to daily consumers. Depending on whether you like impacting people or knowledge more, you can choose a Master’s or a Ph.D. appropriately. For me, I always had the dream of building a tech startup to solve real-world problems and help those in need. A Master’s seems like a better plan for me as I can learn the practical skills needed for building products that are both fast and scalable.
Broad vs Narrow
I think everyone would agree that doing a Ph.D. requires defining a specific research area to do research work about. On the other hand, if you complete your Master’s and begin working in the industry, you’ll notice that you will be able to work on a variety of different products, from designing data pipelines to building a recommender system to evaluating different classification approaches for spam detection. In general, you will be able to work in a more diverse array of areas rather than one specific research topic. Because I believe that there are many AI trends in the future, like natural language generation, conversational systems, multimodal deep learning, etc., I find it difficult to pinpoint exactly one of those that I am most interested in. I would definitely prefer to have the flexibility to work with all of those potentially if I could. Therefore, I believe a Master’s is a better option for me.
Here in this brief post, I listed three primary factors that you could think about if you are deciding between pursuing a Master’s or Ph.D. program in AI or Data Science.
- Engineer vs Research: Whether you enjoy building products or doing research more
- People vs Knowledge: Whether you care more about directly impacting people or contributing to knowledge
- Broad vs Narrow: Whether you like the flexibility of working on different things or just like focusing on one subject
That’s all, hope you took away something from this post. Please feel free to follow me to keep updated on my most recent articles, cheers!
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