The skills actually used in software engineering. When people start looking into software engineering as a career path, a typical first question asked is “I’m not very good at math.
When people start looking into software engineering as a career path, a typical first question asked is “I’m not very good at math. Do I need to know it for software engineering?” The short answer is “Probably not.”
I will make a distinction here between software engineering and computer science. Computer science — the degree — requires a lot of math. It gives a well-rounded theoretical overview of the field that requires a lot of math (with courses like Discrete Structures being very proof-heavy).
Software engineering — the career — usually requires much less math. It’s focused not on the theoretical structures but on practical problem-solving with code. Now, if you work in a quantitative or algorithmic field, this truth will not hold. A quantitative or algorithmic software engineer will use copious amounts of math in their daily jobs. The standard application or web developer will probably not use much at all.
The logical thought process behind math is very similar to the thought processes in software engineering, so in that respect, being good at math should translate very well into being good at software engineering. A lot of software engineering is about problem-solving and being creative in your approach. This is very similar to math in that to solve a problem, you have to pick a specific technique and be creative in how you use it.
This isn’t to say that knowledge of specific topics like calculus will be directly applicable — rather that if you can understand more advanced mathematical concepts, you should have no trouble understanding more complicated topics in software engineering.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
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