why you should learn Python for data science

Peter Maxwell was aiming for a career as a data scientist. So, it was an obvious choice for him to pursue his college degree in data science. Once he joined college, Peter realised the endless possibilities that data science has to offer. He also learnt that it’s important to be well-versed with a few programming help languages to work with and manage volumes of data. 

Speaking of programming languages, most data scientists prefer to learn Python because of its convenience and flexibility. The language makes data analysis and visualisation a whole lot simpler. 

So, if you’re thinking of pursuing data science, here are some reasons why you should learn Python for data science

  1. Python is simple and easier to learn

One of the advantages of learning Python for data science is that it’s straightforward and intuitive, and that’s what makes it so like for anyone who wants to get a result rather than lost in code.

The language is readable and easy to learn. This indicates a shallow entry barrier as opposed to other programming languages like Java, R,  or C++. It requires a proper environment to be set up to do anything apart from running a trivial HelloWorld program.

And, If you’re already convinced that Python is the best programming language for Data Science and preparing an online course that teaches you Python from a Data Science point of view. 

  1. Extensive tools and libraries

One of the preliminary jobs of data scientists is to assess the data. Now, real-world data comes in all shapes. They are often raw and not applicable to run any type of analytics. Thus, data wrangling is utilised for that. It’s a complex process of cleaning and transforming the data so that you can assess and model it to develop insights.

This is where Python assists data scientists. It comes with so many open-source libraries that carry out all these tasks for them. These are the libraries that are frequently updated like MatPlotLib, NumPy, Pandas, etc. All you need to do is utilise them in your Python scripts; you have the perfect tools for data analysis and visualisation.

You don’t need to learn how Pandas works or how NumPy works; as long as you can get your data clean, apply some mathematical formulas you are comfortable with.

All you need to learn is the steps to import a Python module, and you’re sorted. If you’re eager to know which Python module to use for which job, that’s simply a Google search away. You will find your answers immediately.

  1. Improved scalability

Unlike many prominent programming languages, like R, Python is extraordinary when it comes to scalability. It’s also faster than languages like Stata and Matlab.  It facilitates scale as it allows data scientists the flexibility and multiple ways to approach different problems. This is primarily why YouTube migrated to the language. 

You can find the optimum application of Python across multiple industries, ensuring the rapid development of applications for all types of services. 

  1. Helps maintain flexibility

The cool options don’t just stop there. Flexibility is another reason why Python is really a fantastic sill to learn for data scientists. The programming language is known for its hyper flexibility, making it highly requested among data scientists and analysts. 

When you learn the language, you can create build data models, create ML-powered algorithms, systematise data sets, web services. You can also apply java homework help for data mining to accomplish different tasks in a short period of time. Such an advantage makes Python an ideal choice if you want to excel in the domain of data science.

Python data science.jpg

Source: Pixabay

  1. Supportive Python community

The popularity of Python is a direct result of its ever-growing community of developers. As the data science community continues to rely on this skill, more users are encouraged to create additional data science libraries. This, in turn, is ensuring the creation of the most modern tools and advanced processing techniques available today. This is why most of the learners are choosing Python for data science.  

The community is a close-knit one, and looking for a solution to a challenging problem has never been more convenient. A proper internet search is all you need, and you can easily search for the answer to any queries or connect with other people who may be able to provide help. Programmers can connect with their peers on Stack Overflow and Codementor. 

  1. It’s open-source

Python is open-source, which means it’s free and employs a community-based model for development. Python is developed to run on Linux and Windows operating systems. Also, it can be ported to multiple platforms easily. 

There are many open-source Python libraries dedicated to data manipulation and visualisation, statistics, mathematics, machine learning, natural language processing, and so on.

  1. Accessibility to graphics and visualisation tools

It’s no secret that visual information is a lot easier to operate, understand, and remember. 

Python comes with a pack of diverse visualisation options. This makes the programming language a tool not only for data analysis but for the entire data science. 

You can make the data more easier-to-use and accessible through creating various graphics and charts and web-ready interactive plots. Python offers you the capacity to get a good sense of data.

  1. Extensive pack of analytics tools available

Right after you collect volumes of data, you’re required to manage it. Python helps with this process exceedingly well. So, when you’re seeking the perfect tool for complex data processing or self-service analytics, you need to check out Python’s built-in data analytics tools. Scores of data mining companies all over the world have adopted Python to reduce the load of data. 

Python also possesses the ability to penetrate patterns easily and also correlate information in large sets. It provides better insights alongside other critical matrices in assessing the performance.

Parting thoughts, 

Like Peter, if you too dream of a career in the field of data science, you have good reasons to put your time and energy into learning Python. With concrete knowledge in this programming language, you’ll find promising prospects in your career ahead. 

Author bio: Fred Walters is a data analyst for a distinguished corporate firm in Australia. Walters has earned his MS in Computer science homework help from Federation University. He loves learning about new technologies. He’s also an academic expert for MyAssignmenthelp.com and offers online assignment help to students. 

By Anurag Rathod

Anurag Rathod is an Editor of Appclonescript.com, who is passionate for app-based startup solutions and on-demand business ideas. He believes in spreading tech trends. He is an avid reader and loves thinking out of the box to promote new technologies.