How Do You Use Python in Data Science 101
Tags: datascience, programming
Published: 2022-02-23 13:12:17

Python is a programming language that is used for a variety of purposes such as web development, mathematics, system scripting and software development. 
It is a language used to create web applications. It can also create workflows alongside the software. It connects to the Database and helps in modifying files by reading them. In addition, it is able to perform complex calculations and handle big data

If you wish to pursue a career in data science, understanding Python is very crucial. It is a valuable skill that will help your career to a great extent. A good data science course like [Scaler's data science course](https://www.scaler.com/data-science-course/) emphasises Python's use. 

## Why Python? 

Python works efficiently on platforms like Windows, Linux, Mac, Raspberry Pi and others. The syntax layout of the program is similar to the English language making it very easy to use. You can write programs with fewer lines compared to other languages. Since Python runs on an interpreter system, the code written can be executed right away. This makes prototyping very efficient. All these qualities make Python one of the best programming languages, so every data science course stresses it. 

## Learn Python for Data Science in 5 Simple Steps

While a data science course will definitely teach you to integrate Python more meticulously, you can go about learning the basics or even more in the following steps. These steps are easy, and if you are a little focused, you will be able to master them quickly. 

## Learn the Fundamentals 

Learning the fundamentals is the most important and the first step to learning anything at all. And it is no different in the case of Python. Start with the programming basics and data science. Get your hands on a tool that offers Python libraries. You can either join a community or a data science course to escalate your learning. This will place you among like-minded people and open up more opportunities for employment. 

## Practice Mini Projects 

Practice is the key to success in the data science field. Hands-on learning makes you far better than just reading books about a subject. With the right kind of guidance, you will be prepared to work on mini Python projects pretty soon. 
You can use surveys to analyse data. This will train you to drill and mine answers. Some data courses also offer guided interactive projects that will enhance your skills. These projects use real data and help you to apply skills in an innovative manner. 

This could just be the start, and while you are getting the hang of it, you can try your hands on more complex projects that involve more critical and elusive data.

## Reading

While you are practising, do not give up reading. You must have a few books that you can refer to when programming. Practice must go hand in hand to achieve the best results. If you have enrolled in a data science course, you will be suggested some books on Python programming. Make sure you read them throughout your journey of learning Python. 

## Learn Python Data Science Libraries 

There are several Python libraries for data science that can help you gather the most useful information. It helps you explore regions and play with data which in turn makes it easier for you to wrap your head around the working of the program. 

Python has a community of experts who can help you learn the program. A good [data science course](https://www.interviewbit.com/blog/best-data-science-courses/) works great for people who wish to explore and learn all the tenets of Python. The educators will share their knowledge and expertise of the field and enrich your learning. Such courses provide you ample opportunity to practice and get better with each session. 

## Build a Data Science Portfolio 

Aspiring data scientists must have a strong portfolio. You can record your various works with different datasets, including insights and what you have learned while on these projects. Some kinds of projects you can try are data cleaning projects, machine learning projects and [data visualisation](https://en.wikipedia.org/wiki/Data_visualization) projects. 

These are not very difficult but can make a great difference when looking for a job. Not only will it help you learn more about the program, but it will also serve as your experience when applying for a job. However, make sure that your analysis is not fluff and presented clearly. Find an ideal format that would help experts and beginners alike to read and understand your input. Your portfolio can either be diverse or pertaining to a certain niche as long as it is insightful, clear and right; you have a chance of grabbing a job before your competitor does. 

Learning Python from scratch is not an easy task. It might take you anywhere between 4 months to a year, depending on the kind of practice you put in and the mentoring. However, with insistent practice and endeavour, you will learn Python like the back of your hand.