Data science is a new interdisciplinary field of algorithms for data, systems, and processes for data, scientific methodologies for data and to extract out knowledge or insight from data in diverse forms - both structured and unstructured.
Data analysis is the technique of analyzing and inspecting as well as cleansing and transforming of data to retrieve useful information or suggest a solution and this process helps in making decisions for business or other processes. In this tutorial, you will be learning about the various types of data analysis and their uses.
IPython can be said as an interactive CLI (Command-line Interface) for Python. It provides the Jupyter notebook which is a web application that provides an interactive environment for computing and analyzing the data.
Python is known for its neatness and clean data readability and handling feature. There are various techniques for handling data in Python such as using Dictionaries, Tuples, Matrices, etc. In this tutorial, you will be learning about the matrices and its functionalities.
NumPy is a linear algebra library for Python, and it is so famous and commonly used because most of the libraries in PyData's environment rely on Numpy as one of their main building blocks. Moreover, it is fast and reliable.