It's time to start looking at some basic mathematics principles that are handy when dealing with data science. The word math tends to strike fear in the hearts of many, but I aim to make this as enjoyable as possible. In this chapter, we will go over the basics of the following topics:
- Basic symbols/terminology
- Logarithms/exponents
- Set theory
- Calculus
- Matrix (linear) algebra
We will also cover other fields of mathematics. Moreover, we will see how to apply each of these to various aspects of data science, as well as other scientific endeavors.
Recall that, in a previous chapter, we identified math as being one of the three key components of data science. In this chapter, I will introduce concepts that will become important later on in this book – when looking at probabilistic and statistical models – and we will also be looking at concepts that will be useful in this chapter. Regardless of this, all of the concepts in this chapter should be considered fundamental to your quest to become a data scientist.
Mathematics, as a science, is one of the oldest known forms of logical thinking. Since ancient Mesopotamia (3,000 BCE), and probably before, humans have been relying on arithmetic and more challenging forms of math to answer life's biggest questions.
Today, we rely on math for most aspects of our daily lives; yes, I know that sounds like a cliche, but I mean it. Whether you are watering your plants or feeding your dog, your internal mathematical engine is constantly working—calculating how much water the plant had per day over the last week and predicting the next time your dog will be hungry given that it is eating right now. Whether or not you are consciously using the principles of math, the concepts live deep inside everyone's brains. It's my job as a math teacher to get you to realize it.