DailyPost 2019

Data Science though in vogue all around us for a variety of reasons, one important certainly being fancy, is understood and used differently. The learning also is in wide and varied modes. How much one has understood and whether that would serve the purpose in the professional world, also remains unknown. What has this trade delivered in which area also remains unknown. How can we find the level of expertise of a person claiming to be a data scientist? And conversely, what is the right method to gain expertise in data science? Nobody till today has ventured to provide clarity to the path ending up in becoming a data scientist.

If you intend to unravel this journey; a map for learning data science has recently been provided by Syngenta’s Serg Masis. The post has already become viral. It seems as if the world was waiting for it. Being a data scientist, he posted a data science map on LinkedIn, and that was it. The clarity so far missing has since then dawned on all enthusiasts of data science. It has certainly been a dampener for those who were picking up data science with short certification courses and the like. He has clarified that data science that is not a everybody’s ball game. The finesse in mapping of knowledge, skills and expertise has left everybody who has gone through that post awestruck.

The explanation makes super sense, even to those uninitiated in data science. The description and methodology is fascinating not so say anything about the superlative map. ”Data science is a vast body of knowledge encompassing science, business and engineering, and there’s lot to explore, like the oceans in the age of discovery.” This map is the north star in the sea of data science. He continues with this analogy, that he first ”encourages people to practice sailing around the Sea of Probability & Statistics.” Only then can Computer Science be the next port of call. The few areas which need practice here are practicing around programming, data wrangling and MLOps. MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems.

He recommends new sea types to move into deeper and stormier seas after getting proficient in managing the calmer and shallower parts. ”Deep Learning Point is dangerous for beginners, despite how accessible it seems.” He talks of the other exciting seas as well, typically not treated as a part of data science. These are Actuarial Science, Econometrics, Financial Quantitative Analysis, Biostatistics and Operations Research. Skilled data sailors with these expertise might find exploring areas of finance, insurance, logistics, economics, biotech etc extremely rewarding. This is the way a professional data scientist would be born. Now that the methodology is known, more would venture to take this extremely arduous journey. A seafarer indeed.

Sanjay Sahay

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