Math is Still a Hurdle for Data Science Beginners
9 resources to help you build basic math background for data science
Since I started writing blogs in data science in 2018, several individuals have contacted me to inquire about the math background required for data science. One of the top questions from beginners new to data science is: Can I succeed in data science without a strong math background?
The simple answer to this question is that it may be quite challenging to succeed in data science with limited math background. If you are interested in the field of data science, you may need to first build some background in foundational concepts in math before starting your data science journey. Data science is heavy in math and to better implement any data science algorithm, you need to understand the inner workings, otherwise you’d be using the algorithm as a blackbox.
Over the years, I have written several blogs on the math concepts needed for data science. These resources will provide the reader with some basic introductory resources to demonstrate how math concepts are used in data science and machine learning.
1. Math for Data Science Beginners: Functions
Most of basic data science is focused on finding the relationship between features (predictor variables) and a target variable (outcome). The predictor variables are also referred to as the independent variables, while the target variable is the dependent variable. The importance of functions is that they can be used for predictive purposes. If one can find the function that describes the relationship between X and y, that is y = f (X), then for any new value of X, one could then predict the corresponding value for y. This article introduces the role of functions in data science.