Math and Data Science: What Do You Need to Know?

Math and Data Science: Types

Below are some of the most common types of math that you will use in your data science career. 

Linear Algebra

Knowing how to build linear equations is a critical component of machine learning algorithm development. You will use these to examine and observe data sets. For machine learning, linear algebra is used in loss functions, regularization, covariance matrices, and support vector machine classification.

Calculus

Multivariate calculus is used for gradient descent and in algorithm training. You will study derivatives, curvature, divergence, and quadratic approximations.  

Statistics

This is essential in machine learning when working with classifications such as logistic regression, discrimination analysis—and hypothesis testing and distributions. 

Probability

This is critical for hypothesis testing and distributions such as Gaussian distribution and probability density function. 

After having looked at the types in math and data science, let us next look at the applications.

Popular Applications of Mathematics in Data Science

Businesses across all industries need data scientists to help them function and be successful on a daily basis. Understanding how you can use math in practical scenarios can help you understand why businesses need data scientists and how mathematics comes into play. 

Let’s look at some practical uses of mathematics in popular data science and machine learning applications and technologies being utilized by leading organizations today:

Natural Language Processing (NLP)

Linear algebra is used in NLP for word embeddings, and unsupervised learning techniques like topic modeling and predictive analytics. Examples of uses of NLP include chatbots, language translation, speech recognition, and sentiment analysis. 

Computer Vision

Linear algebra is also used for computer vision such as image representation and image processing. When people think about computer vision, companies like Tesla come to mind for their self-driving cars. Computer vision is also frequently used in industries like agriculture to improve yields, or healthcare to classify illnesses and improve diagnoses. 

Marketing and Sales

Statistics is useful for testing the effectiveness of marketing campaigns such as hypothesis testing. It’s also used to understand consumer behavior, such as why consumers are purchasing from a specific brand, in techniques like causal effect analysis or survey design, and personalization recommendations via predictive modeling or clustering. 

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Pursue Your Math and Data Science Education

Math is a core educational pillar for data scientists, regardless of your future industry career path. It ensures you can help an organization solve problems and innovate more quickly, optimize model performance, and effectively apply complex data towards business challenges.

Ensure that you’re building the right skill sets and mathematical capabilities through a leading online bootcamp provider like Simplilearn. We offer Caltech Post Graduate Program In Data Science that guide you through everything you need to know in pursuit of your data science career—including courses dedicated to mathematics.