An increasing number of fintech companies are using Python for data analysis. But what makes Python so special? And why is it a better language for data analysis compared to traditional software?

Python is quickly becoming the most popular coding language in the world. Currently, it’s perching comfortably in the fourth spot after Java, C, and C++ on the Tiobe Index of Language Popularity. And the Popularity of Programming Language Index ranks Python as the most popular programming language in the world in October 2018.

In this article, we’ll take a look at the benefits of learning Python and why financial experts should consider it, even if they have no prior programming experience.

Why You Should Learn Python

Not convinced that Python is the right language for you? Well, it’s time to change your mind.

1. Ease of use for beginners

First and foremost, Python is one of the easiest programming languages to learn. You don’t need to have any programming experience to start performing data analysis in Python. Unlike R and MATLAB, two other popular languages in science and engineering, Python has very simple syntax and coding rules, making it the perfect language for beginners. And it’s also very easy to set it up and jump right in.

2. Quick application development time

Fintech and traditional finance areas prefer Python to other languages because of its quick application development time. Owing to the myriad of open-source data analysis libraries, developing fintech applications in Python doesn’t take nearly as much time as it does with data analysis tools such as Microsoft Excel and R because you don’t have to waste time writing code from scratch.

Trying to decide between R and Python? Check out this article to learn more about these two competing languages.

3. Plenty of online learning resources

The biggest challenge for beginner programmers is finding useful tutorials and resources. Fortunately, the official Python documentation unpacks everything you need to know about the language—and since Python is already simple enough as it is, picking up the language is fairly straightforward. But if you’re looking for more hands-on experience and guidance, you can also look into taking some affordable introductory Python courses from data experts.

4. Extensive data visualization support

The R programming language, Python’s biggest competitor in data science, is credited with providing excellent data visualization libraries. But Python is quickly catching up—with data science packages like plotly, ggplot, and pandas, you can create professional plots and other forms of data display.

5. Open-source libraries

Python has plenty of open-source libraries that extend the core language’s functionality. And installing them is as simple as running the following command from a terminal:

pip install [libraryNameHere]

From simple GUI application development to support for machine learning, networking, and powerful data analysis, there are Python libraries for pretty much anything you can think of.

Some of the top Python data science libraries include:

• NumPy: A full-fledged scientific computing library for performing linear algebra and high-level math in Python, allowing you to work with matrices and other data structures. If you’re familiar with MATLAB, you’ll feel right at home with NumPy.
• SciPy: Building on top of NumPy, SciPy is an excellent library for data scientists and engineers that allows you to work with N-dimensional arrays and perform a variety of optimization and linear algebra operations.
• scikit-learn: If you’re into machine learning, this is the library for you. scikit-learn implements a variety of popular machine learning algorithms. And if you’ve already picked up NumPy and SciPy, you’ll be happy to know that scikit-learn was designed to work with these two packages!
• Matplotlib: Need to create histograms, pie charts, line graphs, or other professional data visualizations for your work? No problem. There’s really no limit to what you can do with matplotlib. You can alo export all your graphics to popular formats for publication.
• pandas: No, not the animal. pandas is an excellent open-source library for data manipulation and analysis. If you’re familiar with data frames in R and the syntax of SQL, you’ll find that pandas combines the best of both of these worlds in a nice little Python library.

If you’re looking to review your knowledge of the pandas and Matplotlib libraries, check out this Introduction to Python for Data Science course.

6. Leading companies are using Python

Octave and MATLAB, step aside. Python isn’t just for casual programming—it’s being used by leading companies in a variety of fintech fields. For example, Bank of America’s Quartz and J.P. Morgan’s Athena platforms both use Python, and big companies like Google, Facebook, Instagram, and Spotify also use Python in their development. Many other companies like Citigroup also now require their data analysts to master Python and take training classes for the language.

Conclusion

What more is there to say? Python is a simple, flexible, and powerful programming language with applications in data science and beyond. And if you’re new to programming, it’s really the perfect place to start.

Learn Python today to put yourself ahead of your competition and get more work done.