There are currently no frequently asked questions associated with this exercise – that’s where you come in! You can contribute to this section by offering your own questions, answers, or clarifications on this exercise. Ask or answer a question by clicking reply () below.
If you’ve had an “aha” moment about the concepts, formatting, syntax, or anything else with this exercise, consider sharing those insights! Teaching others and answering their questions is one of the best ways to learn and stay sharp.
Join the Discussion. Help a fellow learner on their journey.
Ask or answer a question about this exercise by clicking reply () below!
You can also find further discussion and get answers to your questions over in Language Help.
Agree with a comment or answer? Like () to up-vote the contribution!
This lesson block is pretty difficult to understand. I thought that having completed the Data Science Analyst course would be kind of a decent indicator that I’d be able to handle the ML course. I don’t have formal education in Calculus, so this is becoming challenging fairly quickly. I think I need some courses outside before moving on.
When we’re learning programming, there is no initial prerequisite math or science background necessary. We can learn programming concepts from scratch, even as a grade schooler, with the right teacher. Eventually we will want to apply a program to some task we do repeatedly and often in the form of a tool, or a toolbox.
If those tasks relate to science, then it follows we already have a grasp of the mathematical or physical concepts relating to our discipline. This is what empowers us since the underlying logic, algorithms and identities are already known. We only have to apply the language in such as way as to replicate/emulate them.
Programming for web development seldom involves a lot of math, though it can. but programming apps that act as scientific tools is not something we learn in a programming class. Sometimes it comes down to getting the math and science education we need.
Bottom line, we don’t come to a programming class to learn science. We can teach everything there is to know about a language without ever encroaching on a science discipline, at least not conceptually. If we need to get better at math, learning programming is not going to help. Learn the math, then apply programming to it, not the other way around.
Add to this that there are several areas of Maths that are incorporated into programming algorithm theories: Calculus (differential and integral), Linear Algebra, Analytical Geometry, Statistics, Combinatorics, Conics, Probability, Sets, Matrices, etc., to name but a few. We really need to match our skill level to the type of programs we are writing, and not expect to intuit what we need.
) below.
) to up-vote the contribution!