WHILE THERE SEEMS TO BE ADVOCATES OF BOTH FORMAL AND INFORMAL EDUCATION IN THE DATA SCIENCE COMMUNITY, WHAT DOES THE GENERAL HIRING LANDSCAPE SUGGEST?
We may be living unique lives, but there are a few common experiences that tie most of us together. While the instinctive response seems to be what the Covid-19 pandemic has left in its trail, a less grim example would be the chain of events that mark a few decades of our lives – school -> college -> job
While most of these remain unchanged, there has been a growing clamour to rethink education systems as we know it; to one, that both shields learners from the negative effects of new technologies in the workplace, as well as reskills them to prepare for new cross-functional roles that will inevitably be created as a result.
While one way of addressing this talent and labour market issue would be to enrol and improve the quality of education, upskilling in silo would be a more efficient way of achieving this without burdening the formal education system across the world.
Tied to lifelong learning, upskilling through relevant certification is increasingly becoming an accepted norm. But the field of data science is faced with a critical question – can its vast universe be merely acquired through self-study?
While there seems to be advocates of both formal and informal education in the data science community, what does the general hiring landscape suggest?
While IT services company CSS Corp places an ‘inestimable value’ on a formal academic background, LinkedIn admittedly hires candidates based on skill sets alone, and not necessarily on their educational background.