Fear of mathematics, or a sense of not having ‘a mathematical mind’, are among the most common feelings that can hold back an aspiring tech student. For some fields, like web development, these fears are largely groundless – it is possible to learn them even with very little preparation and/or aptitude for numbers.
When it comes to data science, however, things are a little different. Some mathematics is indeed necessary, but the crucial question is how much? Is it more or less what we all did in school, or does one need a postgraduate specialization of some kind?
In this blog, we will attempt to outline what sort of mathematics is necessary or simply useful in order to learn data science. While this will not be an exhaustive list, it should, at the very least, give you a general feel for the topic.
Calculus and Algebra
To begin with, then, you probably already know that data scientists frequently work with charts, graphs and other similar methods of visualizing data. There are two branches of mathematics involved in using these objects and methods, and they are calculus and algebra. (Algebra, and more precisely linear algebra, also has other uses in data science, but we will not go into that level of detail here).
While both calculus and algebra are topics that can get very complicated in academia, the level necessary to start learning data science is fortunately not especially high. As importantly, the type of understanding required is more conceptual than practical. You should know and understand what a function is, but it’s not necessary for you to find the differential coefficient of sec(tan 1-x). You can let the computer do that for you.
Calculus and algebra are both taught in high school, and at the level necessary to start learning data science, they certainly do not require an outstanding mathematical mind. You’ll just have to brush up on some of the notation they come packaged in: understanding something like sigma notation, which is simpler than you probably think, will be the difference between understanding a piece of code and feeling totally overwhelmed by it. You may have a job that requires you to write in Python and make some slides, but you’ll also want to read stuff that is written in the language of calculus.
Statistics and Entry Levels
The above two branches aside, surely the field of mathematics that is most relevant to data science is statistics. This is the one case in which simply brushing up on the general concepts won’t cut it – statistics is really relevant to a great deal of what is done in data science.
Don’t let this intimidate you though, especially as the concepts involved in statistics are generally simpler and more intuitive than those in the other two fields. But if you haven’t studied statistics at all, then you should definitely set aside some time and become familiar with things like standard deviation and variance before you start on your first data science lesson.
The above topics, researched more or less to the level you’d encounter in high school (including statistics), are sufficient to start picking up the rudiments of data science. Of course, if you want to work in the forefront of the field and discover innovative methods and principles, you’ll need more sophisticated mathematics. But it’s important to remember that not all data science is strictly speaking science, in the sense of discovery-based experiments taking place in universities. Much if not most of it takes place within business contexts, where the requirements are substantially simpler (it is in fact more common for beginners to complain that the tasks they are given in the early part of their careers are too simple for their qualifications).
That a minimum level of mathematics is required to learn data science is something which should not be obscured: if a student really can’t stand working with numbers in any way, shape or form, then for their own good they should probably consider a field other than data science.
On the other hand, if someone would like to try, but feels that they don’t have ‘the mathematical mind’ for it, they should definitely look into reopening some of their old high school textbooks. There is a minimum maths bar to access the world of data science, that is true. But many people would be surprised at how low that bar really is.
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