The tech world is notable for successfully discovering and implementing innovative ideas which have been instrumental to the general development of humanity. Artificial Intelligence is one of the most prominent drivers of a new generation of tech.
It, therefore, goes without saying that Data Science is at the heart of the future of technology. As Hemant Sharma succinctly puts it, Data Science as a field of study is the effective use of algorithms and machine learning principles to uncover patterns from raw data. Basically, Data Science is the meticulous study of unearthed data secrets, suggesting and deciding the most economical ways to apply and appropriate these data.
Appropriation can be achieved through prediction (using predictive causal analytics) and then through prescription (with the help of prescriptive analytics).
Because of the apparent relevance of data science – especially in our ever-changing world – the importance of exploring the field of study that is data science cannot be overemphasized – well, at least studying it up to the basic BSc level. However, the recent trend in the tech academia world is the craze to get degrees, mainly Ph.D. degrees in Data Science.
What’s your purpose for getting a Ph.D.?
This is quite understandable; most people do this intending to use the degree to boost their chances at a better job with a data science team or research group. Sadly though, some other people desire a Ph.D. degree but without a structured plan or compelling reason.
Towing the latter path is risky and pointless, considering that much resourceful and beneficial work is already being done even with teams of data scientists who are not exactly armed with Ph.D. degrees. For those who fall in this category, their primary drive to study data science at a Ph.D. level is solely to solve world problems by finding answers to pre-existing problems and discovering suitable solutions to these problems – Data Science Ph.D. degree holder or not.
So many of these scientists were not professionals when they started their careers as data scientists/analysts. Some of them had to transfer from slightly variant fields of study like neuroscience and biology. Still, they were initially fueled by the longing to effect positive change in the AI and tech world, and there have been recorded successes for sure.
One could therefore insinuate that passion, skills, and experience supersede certifications when it comes to the field of data science, as a person with a BSc degree in any science-related field who is willing to learn and work towards change is more likely to add more value than a person who is driven to get a Ph.D. for unclear reasons.
These points beg the question; Is having a Ph.D. in Data Science even worth it anymore? Is it the pocket-friendly decision to make, bearing in mind the uncertainty of job security and career success even after bagging the Ph.D. degree?
Is it possible to balance the weight of pursuing a Ph.D. and the likely accompanying mental health challenges and complications usually recorded among Ph.D. degree students and graduates?
Mental Health of Ph.D. Students: A Disturbing Reality
Getting a degree is a lot of mental and physical work, with many financial burdens attached to it (although most people are consoled with the anticipation of the eventual benefits). Getting a Ph.D. is even more burdensome. Loans are one option to solve the financial part of the burden. Still, even loans do not make the process easier, considering that the thought of debt and inability to pay up may even endanger one’s mental health conditions, especially when the time for payment is fast approaching with no plan or solution to generate the funds.
Spending so much to get a Ph.D. for securing a job in Data Science could potentially be risky because, as earlier pointed out, skills and passion will most likely be chosen over a mere degree or certification, even when it is a Ph.D. So then, what happens to the person who has invested so much money into obtaining the Ph.D. but is unsure of a successful career in Data Science?
This dilemma could pose a threat to the mental health of such persons, coupled with the continued issue of depression in Ph.D. students, becoming very rampant in recent times. In 2017, a study carried out on 3500 students in Belgium showed that one in two Ph.D. students suffers from depression or other mental health problems. This may stem from overwhelming workload pressure, socio-educational-related stress, or some other challenge.
Regardless, getting a Ph.D. in data science does not guarantee anything concrete, as records show that even when a person successfully bags the Ph.D. without mental health issues, they are still at risk of having psychological problems later. This could initially be overlooked and ignored as unimportant. However, the ripple effect will become more and more evident in the increased number of subpar research work and results generated in the Data Science and AI industry, even with the concurrent increase rate in Ph.D. degree-holding ‘specialists’ plowed into the industry.
It becomes irrelevant to all the parties involved whether scientists assigned to specific projects are Ph.D. degree holders or not if the worth of their work is compromised due to unstable mental and psychological problems.
Ultimately, advanced educational knowledge of data science, especially at the Ph.D. level, is an admirable feat and would be instrumental for practical AI and tech industry changes. However, the misconception that a Ph.D. guarantees career success should be addressed and corrected, as it would save many potential aspirants wasted time, money, and effort. They must understand that the essential tool for any form of progressive change is the zeal to create the change.
Here is a good list of Ph.D. programs in Data Science: https://www.discoverdatascience.org/programs/data-science-phd