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.
Being data-driven is a must for companies these days. If you think differently, you might as well pack it in now. Here’s why. Companies are always trying to outdistance each other, winning more clients and gaining the most profit. They burn a lot of time in building strategies and working on tactics, but their actions may not bring the expected results. Why not? Because they don’t start with data.
Heat maps are a great way to visualize patterns in data, but some businesses avoid them because creating them seems challenging and time consuming. Well, it’s not. Do you know what the most popular programming language currently is? According to the PYPL Index, it’s—you guessed it—Python. And our serpentine friend was also crowned the best programming language in 2018 by Linux Journal readers. Why all the buzz? Because Python is simple and easy to learn.
Typical business users make decisions based on gut feelings, but this can’t get them so far. In this article, we’ll look at how learning to write basic SQL queries helps your company become a data-driven organization. Businesses face many decisions. Do we increase our advertising budget in one region or the other? Are certain products selling quickly enough? What we should do if they aren’t? Most of these decisions are driven by intuition, but organizations that make the most business impact use data-driven decision-making.