Do you need to be a math whiz to become a data scientist? The answer is no, but having a strong foundation in mathematics and statistics is essential for success in the field. Through classroom instruction, homework and a final project, students will study the mathematics needed for data management. However, these concepts are not prerequisites for a career in data science. With enough motivation and curiosity, you can master the fundamental concepts needed to thrive in the position, even if you don't consider yourself a “numbers” person.
For example, the influential statistician Hadley Wickham organizes most of his introductory textbook to data science around data analysis; when he dedicates himself to mathematics, it is to explain how to understand one of the analysis tasks that constitute the main topic of his book. That said, a background in mathematics is definitely not a requirement for pursuing a career in data science. Even if you intend to follow a more formal online training program for data scientists, reviewing your basic mathematical skills in preparation doesn't hurt. BloomTech's data science course was created in collaboration with industry experts and hiring managers to prepare students to successfully start a career in technology. If you're considering a career in data science, you'll need a basic understanding of principles and concepts in a variety of mathematical fields.
The initiatives of companies such as Google's attempts to use machine learning to create an autonomous car are based on data scientists being able to use complex mathematics to solve problems that no one else has been able to solve. Any practicing data scientist or person interested in developing a career in data science will need to have a strong background in specific mathematical fields. Most beginning data scientists and machine learning professionals will spend most of their time doing exploratory data analysis, doing basic predictive analysis, and so on. Beginner data scientists working in this field will mainly focus on tasks such as data preparation, cleaning, data visualization, and exploratory data analysis tasks that do not require high-level mathematical knowledge. Over the past few years, data scientists working in the business world have gone from being auxiliary providers of value to becoming key players in the configuration of their companies. In the academic world, people's careers progress through the production of novel research.
An academic data scientist specializing in biostatistics would obviously need a solid knowledge of statistics to be able to bring new knowledge to his profession. Companies in every industry need data scientists to help them function and succeed on a daily basis. Understanding mathematics for data science is completely at your fingertips. With enough motivation and curiosity, you can master the fundamental concepts needed to thrive in the position, even if you don't consider yourself a “numbers” person.