Knowing Python is the most valuable skill to start a data scientist career. Although there are other languages to use for data tasks (R, Java, SQL, MATLAB, TensorFlow, and others), there are some reasons why specialists choose Python. It has some benefits, such as:
- it is powerful but simple to learn
- it is high-level, so the code looks like it was written in English
- it is compatible with a variety of platforms including Windows, Mac, Linux
- it is an interpreted language — it runs the code line by line
- it offers libraries for data gathering, cleansing, transformation, visualization, modeling, and audio/image recognition
- you can do complex computations using a simple syntax
That’s why Python and data science became some kind of synonyms. Vanilla Python gives you all the opportunities to work with data, but the libraries listed below make data tasks easier.
NumPy
NumPy is a general-purpose library for working with large arrays and matrices. Along with scientific abilities, NumPy can be…
