A map for learning data science

Serg Masís, a data scientist at Syngenta, has posted a data science map on LinkedIn that immediately went viral. Serg got data science is a new body of knowledge encompassing science, business, and engineering, and there’s still a lot yet to explore, like the oceans in the age of discovery. “Recently in a few events for entry-level folks, I have shared my thoughts on navigating it with this old-fashioned map that I made,” he said.

Serg encouraged people to practice sailing around the Sea of ​​Probability and statistics while also learning how to extract value connecting it to the business side. Then, drift to the Bay of Computer Science, practicing around programming, data wrangling, and MLOps to name a few.

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They got the uninitiated seafarer only ventures into deeper and stormier waters when they have mastered calmer and shallower ones. The area around Deep Learning Point is dangerous for beginners, despite how accessible it seems, he added.

They said areas such as Actuarial Science, Econometrics, Financial Quantitative Analysis, Biostatistics, and Operations Research are typically not seen as part of data science, but highly skilled data sailors have learned the ropes in those waters. It’s definitely worth exploring these areas should you care to work in finance, insurance, logistics, economics, biotech, and many other industries or disciplines that connect to them, they added.

Thom Ives, PhD, lead data scientist at AI Strategy Corporation praised Serg for making a comprehensive and fun map depicting the vastness of data science.

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The post started a whole discussion thread. Ehecatl Antonio del Rio Chanona, assistant professor at Imperial College London, asked: This is very nice! But what about the area of ​​(mathematical) optimization ?! Many of the algorithms used in DS use optimization as their workhorse.

To which Serg, responded: Indeed. Not only calculus and linear algebra are leveraged in DS. A prior version of the map had graph theory, game theory, information theory, mathematical simulations and Monte Carlo methods but it was too crowded so I decided to cut them out.

Jarrod Teo, a chief data scientist at Direct Sourcing Solutions, suggested the possibility of weather forecaster working together with a statistician too. He said: In my country when they say the chance to rain is pretty high + they even give a date range. Guess what? It happens.

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