Math Needed for Mastering Data Science

Key Takeaways

hello everyone can hear back with another video for you we got a lot of questions about the math and the computer science concepts that you need to know before you embark on the data science journey in this video I'm going to focus on the math concepts that are relevant for you to maximize your potential as a data scientist as usual if you enjoy this video please hit that like button and if you want to see more content at the intersection of data science and sports analytics please consider subscribing well I was researching for this video and thinking about my experience with math and data science I came across a great medium article by dr T Sarkar in this article he goes a bit more into depth about all of these concepts that I'm gonna go through and he also provides great links about where to learn about these things through online resources so after you watch this video I would definitely check out that article it's really good and it's a great foundation for this information without being said I think that the foundation of data science relating to math is in statistics statistics are what most data scientist use every day and they help us shape our decision making as well as a lot of the methods and tools that are used I believe that statistic starts with probability theory you have to understand distributions and understand randomness those are some of the most fundamental parts of algorithms and the way that we interpret results descriptive statistics are also very important for a data scientist you have to understand measures of central tendency as well as correlation these things help us determine what features are important to look at that might impact our end result hypothesis testing and inferential statistics are extremely valuable as well now we use these to understand what types of questions to ask and how to ask questions but we also use it to determine the significance of our findings finally we use a lot of linear regression and statistics and I think that that is something that you should really focus on linear regression are just fitting align two data points is it theme through a lot of data science and this in statistics that is where your fundamentals are built the next type of math that I would really or that I personally focused on was discrete mathematics while statistics is the foundation of most of the data science components discrete math is purely the foundation of computer science in this field you understand about proofs inductive and deductive reasoning you also get a grasp of graph theory recursion and understanding the complexity of problems when you're using algorithms when you're building out these models understanding how long you can expect them to take how difficult they are to create those are two factors that goes into evaluating what models you're going to choose so having an understanding of that founded in discrete mathematics I think can really propel your understanding of data science forward the next math discipline that I would turn to is linear algebra linear algebra is used in a lot of machine learning algorithms including you know deep learning as well as something like principal component analysis also the main Python modules that you use are focused on matrices and vectors and that is a huge component of linear algebra now with that you have to understand matrix multiplication again what the difference between matrices and vectors are as well as what Augen values and eigenvectors are that's very important again in PCA but also in linear regression the final map discipline that I believe is really important is calculus this is actually an area that I really struggled with when I was a sophomore in college believe it or not I got a D in calculus and I've been trying to recover from that ever since but to that point I have actually really focused on learning calculus and understanding these methods because I knew that this was important for the broader data science career that I met for calculus you obviously have to understand integrals and derivatives and these are really important in modeling different types of distributions and creating simulations in addition to that gradient descent is a loss function that's used in many different algorithms namely logistic regression and in neural nets so if you have a grasp of this through the lens of calculus you really help to push your data science understanding further I hope that this video has been a good summary of the types of math that are relevant for data science again please check out that article in the description below for a deeper dive into that area and for the learning materials that are recommended again thank you so much for watching and good luck on your data science journey