Python is the No. 1 most popular programming language used by data analysts, data scientists, and software engineers to automate processes, build the functionality of applications, and delve into machine learning. Companies like Google, SpaceX, and Instagram use it to clean data, build predictive and artificial intelligence (AI) models and web apps, and more. It stands out for being simple to read and write, while offering extreme flexibility and having an active community of users and contributors. This makes it a great language for new programmers to learn for a broad range of applications in data science, web development, and beyond.
Python in Everyday Life: Real-World Examples
Here are some fascinating ways in which Python is shaping the world we live in:
- Artificial intelligence: Python is especially prevalent in the AI community, again for its ease of use and flexibility. For example, in just a few hours, a business could build a basic chatbot that answers some of the most common questions from its customers. To do this, programmers could use Python to scrape the contents of all of the email exchanges with the company’s customers, identify common themes in these exchanges with visualizations, and then build a predictive model that can be used by the chatbot application to give appropriate responses.
- File-sharing applications: When the file-storage platform Dropbox was created in 2007, it used Python to build the desktop applications and server infrastructure responsible for actually sharing the files. After more than a decade, Python is still powering the company’s desktop applications. In other words, Dropbox was able to write a single application for both Macs and PCs that still works today!
- Web applications: Python is used to run various parts of some of today’s most trafficked websites, including Pinterest, Instagram, Spotify, and YouTube. In fact, the visual bookmarking platform Pinterest has used Python in some form since it was founded (e.g., to power its web app, build and maintain data pipelines, and perform analyses).
- Hollywood special effects: Remember that summer blockbuster with the huge explosions? A lot of companies, including Lucasfilm’s Industrial Light & Magic (ILM), use Python to help program those awesome special effects. By using Python, companies like ILM have been able to develop standard toolkits that they can reuse across productions, while still retaining the flexibility to build custom effects in less time than ever before.
Simplicity in Code
Here’s a cool example of just how simple Python is. Below is code that tells the computer to print the words “Hello World”:
Yup, that’s really all it takes! For context, let’s compare that to another popular programming language, Java, which has a steeper learning curve (though is still a highly desirable skill set in the job market).
Clearly, Python requires much less code. This powerful language’s ease of use makes it relevant far beyond data — coders have adopted it to perform all sorts of functions that you encounter every day.
Python at Work
A wide variety of roles can benefit from using Python. Here are just a few:
- Data analyst: A data analyst could use Python to save time by automating tedious tasks or performing advanced calculations.
- Data engineer: A data engineer could use Python to build a data pipeline that takes data from one system, aggregates it or changes its shape, and moves it into another system.
- Software engineer/web developer: A software engineer or web developer could quickly use Python to build the next great web app.
Why You and Your Business Need to Understand Data Science
As the world becomes increasingly data-driven, learning to leverage key technologies like Python, SQL, and machine learning will create endless possibilities for your career and your organization. Now is a great time to dive in.
These skills have surprising uses beyond data, bringing delight, efficiency, and innovation to countless industries. They empower people to drive businesses forward with a speed and precision previously unknown.
Individuals can use data know-how to improve their problem-solving skills, become more cross-functional, build innovative technology, and more. For companies, leveraging these technologies means smarter use of data. This can lead to greater efficiency, employees who are empowered to use data in innovative ways, and business decisions that drive revenue and success.
In our paper A Beginner’s Guide to SQL, Python, and Machine Learning, we break down Python, SQL, and machine learning. The first two are programming languages used to gather, organize, and make sense of data. The last is a specific field in which data science experts and machine learning engineers, using Python and other technologies, enable computers to learn how to make predictions without needing to program every potential scenario.
Download the paper to learn more.
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