How Much Math Do You Need in Data Science? Posted on : Nov 23 - 2022

Introduction

 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:

• Ggplot2
• Matplotlib
• Seaborn
• Scikit-learn
• Caret
• TensorFlow
• PyTorch
• Keras

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, and 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.

2. Case Study: Building A Multiple Regression Model

 Let’s suppose we are going to be building a multi-regression model. Before doing that, we need to ask ourselves the following questions: View More