Learn Python for Data Science | Easy Work Net

1. Steps to Learn Python for Data Science When it comes to learning Python, then you have to choose first which course is best to grab all nuts and bolts of Python. Different courses are available and they are designed according to the person’s demand and of the various period. These courses are designed to provide knowledge that will pave the way to become a data scientist. In addition to this, it also equips you with soft skills along with technical abilities. Here are some steps to learn Python and these are mentioned below. Step 1: Learn Python Fundamentals Everyone needs to start somewhere. The first and foremost step is to learn the fundamentals of Python programming by clearing all the basics of data science too. The important thing that you need to do is begin using the Jupyter Notebook that will help you out to learn Python for data science. Step 2: Practice with Python Projects The hand-on practice is important to implement all the concepts and basics related to Python and data science in real-time. So, you need to build up a small Python project and try to understand the practical scenarios of this field. You can give a try to programming things such as calculators for games or a program that shows the weather from Google in your city. Building up of these small projects helps you to get a better understanding and also get to know that you will start buildup your projects soon. You ought to begin the experience with APIs and start web scrapping. Web scraping will help you to learn Python and also collect data later. Step 3: Learn Python Data Science Libraries Not similar to other programming languages, there is a way to do something in Python. The three pillars of Python libraries for data science are NumPy, Pandas, and Matplotib. NumPy and Pandas are the best way of exploring as well as playing with the data. Further, Matplotlib is a data visualization library that makes graphs such as searching for Excel or Google Sheets. You can use the tool named Git that is very beneficial to track the changes that have been made in coding. It becomes easier through this tool to correct mistakes, experiment, and work together with other people. Step 4: Build a Data Science Portfolio A portfolio is a must for aspiring data scientists. These projects include different datasets and ought to leave readers with surprising insights that you possess. There is no need for a

2. particular theme in the portfolio and find out the datasets in which you have an immense interest. Step 5: Apply an advanced level of data science techniques Always aim to sharpen your skills and the study of data science is full of regular learning but get into the advanced level courses is fruitful. It helps you to cover everything in depth.