Why Data Science Recruiters Should Stop Looking For PhD Candidates
Data scientists are now the new assets for a company, and the candidates are required to have the right ability and skills to be rewarded with this lucrative career. But now, many data science job descriptions involve the requirement of a PhD degree to apply. To ensure an effective workflow in data science initiatives, organisations are seeking PhD aspirants, ignoring the challenge of the skill gap in the country. PhD candidates will have numerous advantages over other non-doctorate aspirants, in terms of knowledge and exposure; however, a PhD degree in the data science field will not guarantee results due to the ever-changing technology landscape. Since a decade ago, data science jobs didn’t exist, and barely anyone pursued a PhD in data science. Therefore there were only a handful of data scientists who used to have a doctorate in data science. Besides, until recently, most of the universities didn’t even offer data science courses, which, in turn, resulted in a shortage of skilled applicants for the jobs. Further, as per research, there are over 4000 jobs for data science in the US (a rise of 56% from the previous data), which provide ample opportunity for PhD candidates to choose from. Therefore, looking for a PhD applicant is a no brainer. According to the head data scientist and AI architect at ProVise Consulting, “Most of the problems that businesses deal with, do not require PhD candidates.” The Landscape Considering the rapid changes in the field of AI and data science landscapes, new technologies emerge, and many techniques get abandoned, a PhD in data science takes somewhere between five to seven years. For one, Jupyter Notebook, TensorFlow, Keras, PyTorch, among others have only become mainstream in the last three to four years. So, these tools wouldn’t be included in the data science courses in universities, and thereby, several aspirants enrolled for PhD in data science in 2014 or 2015, wouldn’t be proficient in new technologies that are relatively common today. Consequently, a PhD in data science doesn’t ensure a skilled candidate. However, in their defence, a doctorate scholar, over the years, acquires strong basics which empowers them to familiarise with new technologies quickly. Skills vs PhD In Data Science In the highly competitive data science landscape, skill is of paramount importance than certificates. For instance, on Kaggle, where developers from around the world compete to win the challenges, countless developers have become an expert without having a PhD in data science. Today, there are numerous platforms that various organisations can use to determine the proficiency of a data scientist instead of seeking for a PhD candidate. Unlike other development roles, data science developers are trained to solve real-world challenges on Kaggle, StackOverflow, GitHub, and other hackathons that are hosted across the world. Thus, their performance on these platforms speaks volume. Besides, data scientists are leveraging media platforms such as Medium, LinkedIn, Twitter, and more, to demonstrate their expertise in data science. “Writing is the most underrated skill in data science,” said Parul Pandey, a data science evangelist at H2O.ai. Recruiters can easily find ideal candidates by evaluating their work on different developers and media platforms. A proven track record is what recruiters should seek instead of a degree in data science for making sure their ability to solving business challenges when they get hired. Online Courses Serve The Purpose As per the latest trend, the industry is witnessing a massive rise of e-learning platforms and the students enrolling in them, which portrays the potential of filling the talent shortage gaps in the data science and AI marketplace. Analytics India Magazine covers data science journey of prominent data scientists, who describe their journey and their process of gaining proficiency in data science through best practices, which, majority of the times, includes enrolling in online courses. According to them, one can accomplish great things without a PhD in data science. Outlook So does a PhD degree have any relevance in data science? Yes, in academia, a PhD degree has significant relevance; in fact, it has increased over the years. While there is no proven statistics, it is believed that data science aspirants who focus on the research area have the edge over the ones who take online courses. Research in data science is paramount as it brings new techniques in the landscape, which makes the workflow easier. In 2019, we saw numerous breakthroughs in data science and AI technologies that researchers succeeded in shifting the landscape. Consequently, until an organisation is into intensive research to make breakthroughs, they should not seek a PhD in data science from applicants. This will not only help them in hiring the right candidates but also help aspirants to get in the right firm.
Data scientists are now the new assets for a company, and the candidates are required to have the right ability and skills to be rewarded with this lucrative career. But now, many data science job descriptions involve the requirement of a PhD degree to apply. To ensure an effective workflow in data science initiatives, organisations […]
PhD in data science has numerous advantages, but recruiters should stop seeking PhDs as it doesn't guarantee superior workflows.
["data analytics certificate"]
["data science", "Keras", "AI", "PyTorch", "data analytics certificate", "RAG", "Ray", "Jupyter", "analytics", "TensorFlow", "R"]
["AI", "data science", "analytics", "Ray", "TensorFlow", "PyTorch", "Keras", "Jupyter", "RAG", "R"]
Rohit is a technology journalist and technophile who likes to communicate the latest trends around cutting-edge technologies in a way that is straightforward to assimilate. In a nutshell, he is deciphering technology.
Email: rohit.yadav@analyticsindiamag.com