The choice between learning C or C++ for data science will depend on your specific needs and goals. Both C and C++ are general-purpose programming languages that are widely used and have a lot of capabilities, but they have some differences that might make one a better choice for you over the other.
C is a low-level programming language that is known for its efficiency and speed. It’s often used for system programming, developing operating systems, and embedded systems. If you need to work with low-level systems or need to optimize performance, C might be a good choice for you.
C++ is an extension of the C programming language that adds object-oriented features and other modern programming constructs. C++ is widely used in many areas, including data science, because it provides a high level of control and flexibility while still maintaining efficiency. Many popular data science libraries, such as TensorFlow and scikit-learn, are implemented in C++ or have C++ bindings, making C++ a good choice if you want to work with these libraries.
In general, both C and C++ can be used for data science, but Python is a more commonly used programming language for this field. Python is a high-level, dynamically typed programming language that is well-suited for data analysis and visualization. It has a large and active community and a vast collection of libraries and tools specifically designed for data science, making it a great choice for many data science projects.
Ultimately, the choice between C, C++, or Python for data science will depend on your specific needs and goals, as well as your existing programming experience and skills.