What Are the Advantages of Using Python for Data Science?

Python is not just the fastest-growing programming language in the world, but it’s also one of the most powerful. It’s used in a wide variety of fields and applications, and there are many data science-specific tools that work with Python to give you more power over your data. Here are ten ways that Python can be advantageous for your data science endeavors. In recent years, the Python programming language has become increasingly popular among data scientists and other tech pros, thanks to its numerous benefits over competitors like R and SAS. Many data scientists have come to rely on Python not only because of its flexibility and powerful libraries but also because it’s easy to use and user-friendly – even if you’re not an expert programmer. If you’re serious about data science, you probably already know that Python is one of the most popular languages in the field, and many of today’s most innovative companies including Facebook, Google, and Amazon have built their own frameworks on top of it. But did you know that Python has been used in the field of data science since its initial release in 1991? Or that it’s considered one of the best programming languages for beginners? In this article, we’ll look at ten specific advantages of using Python to perform data-intensive tasks as well as explore some interesting facts about the language and its history.

Cross-Platform Compatibility

Python is one of few languages that can be run across multiple operating systems. This makes it incredibly easy to share code with developers in your organization who are running different operating systems. Furthermore, it’s also possible to create a single program or script that will run across both Windows and Mac OS X, allowing you to work on a project together as a team, regardless of which machines they’re using. A unique advantage to Python over other programming languages is its user-friendly interface. It uses English keywords and its coding style looks more like natural language than most other programming languages do. That said, if you already know another coding language, there is an obvious learning curve when first writing in Python. However, once you get used to it, Python has a lot of advantages over other coding languages.

Easy Learning Curve

Unless you come from a programming background, learning to code in Python is easier than most other languages. In fact, we’ve seen people who know almost nothing about coding at all learn how to work with data in Python. The reason? It’s easy to learn and easy to read; anyone can pick it up quickly and start writing programs. Additionally, it doesn’t take long before you’re able to build complex software applications using just a few lines of code—and that means you can accomplish more with less effort. When time is money, that saves plenty of both. If you don’t have any experience coding, but are interested in getting started, check our online tutorials.

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An Open Source Language

Python is an open-source programming language, which means that it’s free to use and doesn’t come with licensing fees or other restrictions. Because it’s open-source, there are lots of tutorials, books, classes, and other resources available. It was created by Guido van Rossum in 1991 at Stichting Mathematisch Centrum. Its syntax is designed to be readable even if you don’t have a background in programming. Python has been used in many major companies like Google, Instagram, NASA, and Reddit. In addition to being used as a standalone programming language, it can also be used as part of another program (like C++). That makes it incredibly versatile and useful for data science projects.

Widely Used in Industry

One reason you should learn how to use Python for data science is that it’s been used in a wide range of industries. People often refer to it as the Google language because it’s used so widely by people who work at Google and was one of Google’s first-ever programming languages. Because there are so many people using Python in such different areas, there are plenty of tutorials out there that will help you quickly pick up your skills. You can also use that fact to your advantage; if you’ve learned one kind of code in another area, a lot of times you can adapt those skills. So if you know Java, for example, and now want to start learning how to program better with data science—you already have a good start! And even if you don’t know any other languages, that’s okay too: Python has its own specific way of doing things which means you won’t be tripping over yourself trying to figure out what everything does.

Excellent Libraries

Python boasts excellent libraries that are useful to data scientists. SciPy is a library that helps with many tasks, including computing and linear algebra, statistics, optimization and plotting; NumPy extends SciPy with tools designed specifically for numerical computation; Matplotlib allows you to create publication-quality figures quickly; Pandas adds table-like data structures as well as high-level statistical operations like group by, reorder, pivot tables and window functions. The Scikit-Learn library is ideal for machine learning and includes algorithms such as k nearest neighbors, logistic regression and support vector machines. Finally, Seaborn makes it easy to visualize complex data. There are plenty more but these will get you started! Learning any programming language takes time, so having great documentation and resources can really help out when you’re getting started. Luckily Python has its own beginner guide aimed at complete beginners who have no programming experience at all (although some basic knowledge would be helpful). It also has one of most active Q&A sites on Stack Overflow which means people are using it in industry right now and getting answers about how to use specific features or solve problems quickly.

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Fast Execution

When compared to other programming languages, Python has a high-level language that is an interpreted language. Because of its well-organized nature, it can be used to run as a part of major existing platforms and frameworks in companies. That makes it more efficient when compared to Java or C++. Less Expensive: There are no expensive compilers or other software used in order to write code using Python. It supports object oriented programming that ensures memory management. Hence, unlike other languages which use huge amounts of memory and processing power, you can use small applications with better results without any sort of unnecessary details like data classes or base classes etc. In addition, Python also provides flexibility in terms of development as it supports multiple platforms like Windows and Linux etc. Great Community Support: The developer community is large enough that one can find someone who would help them solve their problems whenever they get stuck somewhere. The entire community revolves around solving issues faced by people all over the world so they come up with some amazing solutions on a regular basis. Easy Learning Curve: Compared to other programming languages, python has an easy learning curve and there are many tutorials available online where one can learn from scratch very easily.

Huge Community

The size and diversity of data science communities using Python help developers find answers to their questions. If you’re stuck or need advice, your peers can usually lend a hand. There are also numerous online resources—including books, blogs, and websites—that cater to data scientists using Python. If you want to learn more about a particular subject, you don’t have to worry about not being able to find resources: There’s plenty out there! (Even if you aren’t using Python, these resources might be helpful.) And if that isn’t enough, there are even meetups for people who use Python for data science. You never know when your skills will come in handy; attending a meetup is an easy way to network with other professionals and make connections that could lead to future opportunities. Whether you’re new to data science or a seasoned pro, Python is sure to boost your career. It’s fast becoming one of the most popular languages used by individuals and businesses for coding applications in general and data science in particular.

Popularity with Programmers

There are plenty of programmers out there who have no experience in data science and can’t program in a language such as R. And as anyone who has worked in tech knows, it’s harder to get anything done without help. In many cases, you need someone else to help develop algorithms or scrape websites. With Python, you have access to thousands upon thousands of developers—most companies will even use their own in-house devs if they don’t have any data scientists on staff (but better companies will use freelancers). With other languages, like R, your options may be limited if you want an algorithm developed from scratch or a specific problem tackled. Python is more widely used than R, so it makes sense that you would have more people to work with. It also helps that Python has been around longer than R, which means there are already loads of tutorials and examples available online. This not only saves time when learning how to code in Python but also when tackling problems at work! When working with big datasets, speed is essential—especially when analyzing big chunks of data. With Python’s ability to run operations quickly on large datasets, you can move forward with confidence knowing your company isn’t wasting time waiting for answers.

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Cost Effective

Compared to R, Python is a lot more cost effective. According to Quantopian, a trading platform, a basic R development environment can set you back up to $5,000—that’s almost enough for three decent laptops! The good news is that Python requires nothing more than what you already have in your office and on your computer. Given that both data science and analytics are expected to be areas with high growth over upcoming years (one study suggests as much as 28% annual growth through 2020), it’s no surprise that firms are looking at their options when it comes to hiring talent. Luckily, Python has done an amazing job at opening up opportunities in data science by making it available on virtually any device—all you need is an internet connection. This makes Python a great option for companies looking to hire new talent without breaking their budgets. It also means that individuals interested in entering into data science don’t necessarily need to invest heavily into training or equipment if they want to get started. Since getting started is so easy, there are more people out there doing it.

Easy to Deploy Code on Web

Python code can be easily deployed on web servers via popular hosting services like Heroku and AWS Elastic Beanstalk or on infrastructure provided by providers like Google App Engine. Popular deployment tools include Fabric, Ansible, and Vagrant. Deploying code to a production server is an important part of any development process, allowing you to test your application in different environments. This flexibility makes it easy to integrate with deployment tools like Jenkins CI and other continuous integration software you might use for development. It also allows you to share your code with others, which is essential when working as part of a team.

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