Now I have structured this course much different than a traditional stat course that you would go through in high school or at the university level. I have a very clear and intentional goal that I want to achieve with this course.

I remember back to when I was in school and I went through Istat course and I also have been working in the machine learning space for a number of years and I couldn't really reconcile the fact that many of the concepts in that stat course I took in college were used at all in my machine learning and data science work. And so I don't want to waste your time with stat knowledge that you're not actually going to use in real life applications.

Instead, I took a very different approach and I actually worked backwards in designing this course and I also coordinated with a number of other data scientists that I've worked with to make sure that instead of giving you a set of guides that wasted your time. I only pick out very targeted mathematical and statistical principles. And so what we are going to go through when we get into the course material is a concept. Each section is going to have its own statistical concept and then I'm going to go through a real-life case study of how that concept can be applied inside of data science.

Before we start going into the course material I first want to address one specific question and it's a question that I've been asked a number of times from developers that are wondering about how important math is and the question is do I need to know math. As a developer and my answer would be I'm going to actually draw a diagram.

If you're wanting to get into the machine learning and data science space and if you're taking the machine learning course then you'll already have seen this diagram but when you are working in data science you were working in three fields of study. You need to be able to be a developer so you need to know coding you need to work with languages such as Python or R in order to implement your algorithms.

You also need to have domain expertise. And what that means is you need to understand the industry that you're building your mathematical formula and your different algorithms for now this third piece is very important in data science and that is the math category.

So a data scientist falls right in the middle of these three categories a data scientist has to be able to perform development. They have to have domain expertise but they also have to have an understanding of math. Now this does not mean that you have to become a math professor and you're going to have to know every single type of formula that is out there.

However one thing I will say is if you're missing this piece then you're going to have a very difficult time understanding the algorithms and what they're doing. You may have a high level view of what they do and what their behavior is. However, each one of those algorithms has a root in stats and that's the entire reason why I created this course.

Because if you go through any of my machine learning courses and you see all the algorithms that I'm building out each one of those algorithms is tied directly to some principle in stats and in mathematics. And my goal as an instructor is to give you a holistic knowledge of the machine learning and data science space. And if we're missing the math component then you're going to have a hard time understanding more complex algorithms and you're also going to have a difficult time in knowing which algorithm should be picked for which situation.

Now that you have a high-level understanding of the goal of this course and also why math is important for developers that are wanting to get into machine learning and data science in the next guide we're going to analyze the goal of statistical analysis.