In short, very. Machine Learning (ML) is at the height of Gartner’s hype cycle, and we see the corresponding boom in the job market. AI will bring the next industrial revolution, claimed at TED. Others claim there is a ML startup for every job that doesn’t have ML. Tall and yet unsubstantiated claims. Broadly speaking, data science, machine learning and AI can be seen synonymous. Let’s talk concretely and look at a snapshot of the job opportunities of data scientist, and compare it to data analyst. Quora defines the latter as someone who mainly looks at the known, i.e. historical data, from new perspectives such as writing custom queries for business. In contrast, data scientist looks at estimating the unknown from known data such as a recommendation system, fraud/anomaly detection, store sales prediction etc. We compare the opportunities at three different job boards in the GTA as plotted in the bar chart.
The boards vary in the number of jobs and proportion. The proportion of data science to analyst is opposite comparing Indeed and LinkedIn, and we are not sure why. However, we see the interest in data science has doubled in last 12 months if we look at Google Trends globally. Meanwhile, it has stayed stagnant for data analyst.
Around 18% of Canadian population lives in GTA, but 45% of data scientist jobs for Canadians are in GTA. Data science work is also different from big data space or Hadoop ecosystem, which is useful in its own right but has had a focus on data ingestion and presentation to SQL layer. Unlike typical IT tasks, it is hard to program your way out of ML problem. You need to have solid understanding of the underlying theory and algorithms. This makes ML niche for Highly Qualified Professionals, and places Datalaya in a favorable position with a team of PhDs.
Authors: Drs. Rizz, Sif