If you’ve been digging around our website or researching tech tools, you may have heard of Python. Python is a programming language that can be used by software developers, accountants, mathematicians, and especially, data scientists. Python is actually the dominant programming language in data science, often used for data wrangling, analysis, visualization, and machine learning.
What is Python Used For?
Python is an object-oriented language, meaning that the focus is on data and procedures rather than functions and logic. By writing out procedures, you can actually modify the data you have. It can help you automate mundane, repetitive tasks, such as downloading websites, changing the format of those websites, renaming those files, and uploading them to servers. With Python, you can program all of these tasks to be done automatically every time you download a website. This automation saves data scientists a lot of time.
Is it Good for Beginners?
Yes! One of the key features of Python is the short, easy to understand syntax in which you write your code. Its minimalistic nature allows you to focus on solving your problem rather than solving the code itself. It reads and feels more like English as compared to other common programming languages. Sometimes, what would take you 4 lines of code in Java can be done with 1 line of code in Python. Because it is a high-level language, meaning it is largely independent of your computer specifications, you can get started using Python on whatever device you have – even phones, tablets, or PlayStation!
How is it Used in Data Science?
Python can be used for data wrangling, data analysis, data visualizations, and applying machine learning algorithms. Data wrangling involves preparing raw data for use by parsing it, meaning to convert each data point to a standard format, and by cleaning it, which means to separate useful data from erroneous or missing data points. This is relatively easy if you have only a few data points, but parsing Big Data by hand, like a thousands-of-terabytes large list of every transaction a retail store has ever had, is out of the question. So, Python is used to automate these tasks. It’s also used for data analysis – helping you find trends, correlations, variations, and outliers; data visualizations – helping you chart or graph your findings to show to others; and machine learning, as the complex algorithms and workflows that it’s capable of make it great for Artificial Intelligence.
While this all sounds quite complex, remember, every data scientist started out knowing nothing about Python. Want to try it out? Check out our events page to see when our next free, beginner-friendly, Python Workshop is! If you don’t see one listed, check again soon – we usually have one every other month.