Every model you will train later is built on a small set of ideas from math, and the people who get the most out of data science are the ones who understand those ideas rather than just calling a library. This module covers the essentials you will lean on again and again: linear algebra, probability, and statistical inference.
You'll start with vectors and matrices, the language that data and neural networks are written in. Then you'll move to the normal distribution and the central limit theorem, which explain why so many real-world measurements behave the way they do, followed by Bayes' theorem for updating beliefs as new evidence arrives, and p-values for deciding whether a result is signal or just noise.
With the math in place, you'll be ready to put it to work in code in Python for Data Science, the next module in this course.
A visual introduction to vectors, from arrows in space to lists of numbers, and why that dual view is the foundation of linear algebra and machine learning.
What the bell curve is, what its mean and standard deviation tell you, and how to use it to reason about how likely a value is.
A visual walk through why sums and averages of random values tend toward a normal distribution, and why that matters for statistics.
A visual way to think about Bayes' theorem: how a prior belief is updated by new evidence to give a posterior belief.
What a p-value actually measures, how significance thresholds like 0.05 work, and the limits of reading too much into a single number.