How Much Math do I need in Data Science?
If you are a data science aspirant, you no doubt have the following questions in mind:
Can I become a data scientist with little or no math background?
What essential math skills are important in data science?
There are so many good packages that can be used for building predictive models or for producing data visualizations. Some of the most common packages for descriptive and predictive analytics include:
Thanks to these packages, anyone can build a model or produce a data visualization. However, very solid background knowledge in mathematics is essential for fine-tuning your models to produce reliable models with optimal performance. It is one thing to build a model, it is another thing to interpret the model and draw out meaningful conclusions that can be used for data-driven decision making. It’s important that before using these packages, you have an understanding of the mathematical basis of each, that way you are not using these packages simply as black-box tools.
Let’s suppose we are going to be building a multi-regression model. Before doing that, we need to ask ourselves the following questions:
How big is my dataset?
What are my feature variables and target variable?
What predictor features correlate the most with the target variable?
How should my dataset be partitioned into training and testing sets?
Should I use PCA for removing redundant features?
How do I evaluate my model? Should I used R2 score, MSE, or MAE?
How can I improve the predictive power of the model?
What are the regression coefficients?
What is the intercept?
Should I use non-parametric regression models such as KNeighbors regression or support vector regression?
What are the hyperparameters in my model, and how can they be fine-tuned to obtain the model with optimal performance?
Without a sound math background, you wouldn’t be able to address the questions raised above. The bottom line is that in data science and machine learning, mathematical skills are as important as programming skills. As a data science aspirant, it is therefore essential that you invest time to study the theoretical and mathematical foundations of data science and machine learning. Your ability to build reliable and efficient models that can be applied to real-world problems depends on how good your mathematical skills are. To see how math skills are applied in building a machine learning regression model, please see this article: Machine Learning Process Tutorial.
