What are useful data structures with Python in your work as a data analyst/scientist?
Data Science in Python is less to do with Data Structures and more with In-built Statistical Libraries. But data structures like stacks, lists, dictionaries and tuples are often used in Data Science. These are not the core but are the means to the more important Data Science and ML topics, which are:
- Descriptive Statistics in Python
- Linear and Logistic Regression in Python
- Decision trees and Support vector machines in Python
- Neural Networks – RNN & CNN
- Deep Learning (Tensor flow and python)
- Spark and Python for Big data
So you need to focus on libraries more and need to have knowledge about the implementation of data structures. Let’s have a look on libraries available in python.
Important libraries for Data Science and ML in Python are:
- Basic Python Data Science libraries : Numpy, Scipy, Pandas , Matplotlib
- Machine learning libraries : Scikit-learn, Tensorflow, Theano