If someone is trying to get into programming then I think that python is one among the best and the easiest languages to learn. This is why because python has a lot of real-world practical applications apart from the regular programming skills. By learning Python, one can easily get into the world of artificial intelligence because AI is going to the next big thing in this industrial revolution. Python for data science course is the need of the hour now.
One has to start with the basics of python language like creating the hello world program, data type declaration and python installation. Then one will learn about the python lists and strings list is a data structure in Python that is a mutable, or changeable, ordered sequence of elements. Each element or value that is inside of a list is called an item. Just as strings are defined as characters between quotes. Then one has to learn about some of the libraries that are used in data science like NumPy library, Pandas library, Matplotlib, Scipy and many other libraries. Then enhance more your python coding skills by practicing more of it. Many Deep learning frameworks in Data Science are implemented by using python. Along with this, in the field of machine learning it is used for building fraud detection algorithms and network security algorithms for developers. Due to this immense application of python in data science, deep learning and machine learning, the requirement of the professionals with python skills have increased drastically over the past few years. This further creates the necessity of python for data science tutorial.
Python has massive libraries and can be extensively used for data manipulation because of its open source software feature. Therefore it provides a great approach to object oriented programming. The main use of python in data science is for crunching data, data visualization and weather forecast in companies like Forecast watch analysis.
Use of Python in each stage of Data Science and Data Analysis:
1 In the first stage the main application of data science is for understanding the type of data one needs to work upon. This stage consumes a lot of energy and time therefore the kind of python libraries used here to do the pararell processing are Pandas and NumPy.
2 While in the second stage the main function is to do the data scrapping by shortlisting the data according to the requirement.
3 In the third stage we need to get the graphical representation of the shortlisted data by means of python libraries like Matplotlib and Seaborn.
4 The next step involves the process of machine learning and complex computational mathematics like calculus, probability and matrices over lakhs of rows and coloumns.