Python is one of the most popular and user-friendly programming languages used today. Developers, analysts, and data scientists alike use Python not only to “automate the boring stuff,” but also to clean, query, and analyze data, create web applications, and integrate systems more effectively. So, what’s so special about this general-purpose scripting language?
We’re glad you asked.
Today we will dive into why learning Python is a great starting point for any aspiring data scientist and why you will want Python in your toolbox as you level up your career.
First released in 1989, creator Guido van Rossum named Python after Monty Python’s Flying Circus in order to give the language a unique and mysterious handle –but don’t let that scare you! Python is as simple and easy to read as they come.
Python is known by many in the field as the “Swiss army knife” for programmers – and for good reason. Python is easy to learn and useful for scalable back-end web and mobile app development, desktop app and software development, big data processing, and for writing system scripts. So, what makes Python so popular among all types of programmers?
Python’s beginner-friendly, easy-to-use syntax and ability to develop models and visualize results makes it a powerful tool and has led to an exponentially large increase in popularity in the international developer community over the last decade. In fact, Python is one of the most-learned programming languages to date in large part due to the simplicity of its structure and the speed with which programmers can code. Further, Python is readable and much easier to learn than other programming languages such as R, C++, and Java, making it the perfect choice for those new to programming.
Not only is Python an interpreted language (rather than a compiled one), its simplicity allows for free and open-sourced language and high-level programming that is intuitive and straightforward, meaning you will get results rather than getting lost in your code. And since it typically requires fewer lines of code to complete the same operation in Python than in other languages, it’s faster and easier to complete scripts than any other language.
Compared to other languages, Python scales fast. This means Python has the capacity to grow with the times - taking on more requests and expanding the network of functions as large as a company or individual may need. This is especially relevant for industry growth as companies expand from mom-and-pop operations to billion-dollar companies and the need for competent developers and data scientists grows. Thus, a Python-based application can maintain its syntax and can grow with any company.
Finally, Python is constantly updating to newer versions, cutting down on completion times and making it truly one of the most versatile and evolving languages out there. Who doesn’t love languages that evolve with the time?
Data science is the study of information, and the tech industry today uses data science to help make decisions that directly impact performance, results, growth, and revenue. If you are considering a career in data science, you know that data scientists most commonly clean and prepare data to be analyzed using a script. Thus, knowing what programming language to write the script in can be a huge time saver and can set you up for success. With so many helpful programming languages out there, why learn Python?
Python is widely used in popular applications and is the foundation for many social media platforms with significant staying power such as Facebook, Instagram, YouTube, and Pinterest. It’s also a significant language for Google, who released their own Python Style Guide. Google engineers even developed their own open source machine learning libraries like TensorFlow to benefit the Google community and beyond.
Python is used in just about every corner of tech, from niche tech spaces like scripting and automation, to 2D imaging and 3D animation packages like Autodesk, scientific and computational applications like FreeCAD, and video games like Disney’s Toontown.
In the last five years in particular, Python’s popularity has grown exponentially, with ratings increasing each year faster than any other coding language. Today, Python consistently sits in the top three of all coding languages world wide, with TIOBE’s July 2021 rating showing a steady increase each year and the popularity of other top languages falling in response. If this trend continues, Python may surpass the popularity of Java and C in 2022 as it does in other current user ratings.
Python’s utility makes it a no-brainer for data scientists looking for career stability. Consider this – due to its versatility and ability to work across platforms, Python is used in a variety of data science roles including Python developer, product manager, financial advisor, educator, and even data journalist. Additionally, Python data engineers make an average annual salary of $108,417 and according to HackerRank, Python is the second most in-demand programming language for hiring managers.
Industries with the highest need for Python include those who are modernizing and beginning to collect data to increase revenue and improve outcomes such as insurance, banking, aerospace, finance, business services, hardware, consulting, info-tech, and software development. As these needs continue to increase and diversify, data scientists will be more important than ever in managing and analyzing this data. Having a base in Python, then, can give any data scientist or machine learning enthusiast the flexibility needed to work in a variety of secure and profitable roles.
Python has been a programming staple since the 90’s and has demonstrated arguably the fastest exponential growth of all programming languages for the last decade. Currently, 84% of data scientists use Python as their primary programming language, with 66% of data scientists using Python on a daily basis. With this popularity, Python support modules and community resources have grown as well, meaning problems are easy to solve with the extensive network of open-source Python libraries, not to mention books, online tutorials, podcasts, apps, and videos. The more popular Python becomes, the more these repositories of information will grow to meet coding needs. It’s no wonder, then, that Python is considered the best language to enter the world of data science.
Now that you know the benefits, you may be wondering how do I learn to code with Python? The best way to learn Python is at Lambda School, where students are immersed in Python from day one. Lambda School’s rigorous Data Science Program helps students pursue a career in applied statistics and machine learning with an immersive coding curriculum focused on Python and other data science essentials such as SQL, data visualization, machine learning, linear algebra, databases, statistics and modeling, natural language processing, and more.
How long does it take to learn Python? Lambda School offers 6-month full-time data science courses that will prepare you for a career in tech with no upfront cost. The program includes job preparation, supportive mentorship, and peer-to-peer support to help you stay motivated. Additionally, Lambda School offers Computer Science coursework and group labs projects to highlight your skills when you enter the field.