Data science and applied mathematics are both interdisciplinary fields, but they have key differences in terms of academic coursework and career paths.
Academically, data science majors typically focus on learning the tools and techniques used to extract insights from large and complex data sets. These include tactics in areas such as statistics, machine learning, programming, and database management. Applied mathematics majors, on the other hand, focus more on the theoretical foundations of math, including topics such as calculus, linear algebra, and abstract algebra.
In terms of career paths, data science majors are often employed in fields such as business, finance, and technology, where they use their skills to analyze data and make predictions to inform decision making. Unsurprisingly, applied mathematics majors also work in a variety of fields, including finance, engineering, and research, but often use their skills in a more theoretical or research-oriented capacity.
Knowledge needed to succeed in data science include a strong foundation in data analysis, statistical modeling and machine learning, database management, and programming. The skills of applied mathematics majors will have a strong foundation in mathematical theory, proof-writing, and modeling.
Overall, while both fields are interdisciplinary and overlap in some areas, data science majors tend to focus on the practical application of math to solve real-world problems, while applied mathematics majors tend to focus more on the theoretical foundations of math.
Please note that this is not an exhaustive list. Remember that available positions and company availability may change depending on the country and the current economic situation.
