Mathematics is an integral part of data science. Any practicing data scientist or person interested in building a career in data science will need to have a strong background in specific mathematical fields.
Depending on your career choice as a data scientist, you will need at least a B.A., M.A., or Ph.D. degree to qualify for hire at most organizations. A significant portion of your ability to translate your data science skills into real-world scenarios depends on your success and understanding of mathematics.
Data science careers require mathematical study because machine learning algorithms, and performing analyses and discovering insights from data require math. While math will not be the only requirement for your educational and career path in data science, but it’s often one of the most important. Identifying and understanding business challenges and translating them into mathematical ones is widely considered one of the most important steps in a data scientist’s workflow.
Will you be a data scientist, machine learning engineer, business intelligence developer, data architect, or another industry specialist? Maybe you don’t yet know the exact path you will take in your data science career. But take a look at the various types of mathematical requirements and what they are used for in data science. You will have a better understanding of your skills and interests and can ultimately better pursue your choice of mathematical education.
Let’s start by taking a look at the different types of math used in data science so that you have a better idea of what you really need to know when it comes to mathematics and your data science career.
Math and Data Science: Types
Below are some of the most common types of math that you will use in your data science career.
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.
Multivariate calculus is used for gradient descent and in algorithm training. You will study derivatives, curvature, divergence, and quadratic approximations.
This is essential in machine learning when working with classifications such as logistic regression, discrimination analysis—and hypothesis testing and distributions.
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.
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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