Don’t let the name fool you: Python programming isn’t something to be feared. It’s a powerful tool that propels data-driven businesses today, and adding it to your skill set can help launch you on a lucrative new career path.
We’ve put together this primer on Python to help you learn exactly what sets it apart from other programming languages used for similar applications. Read on for an introduction to Python syntax, modules and libraries, and consider how becoming a Python programmer could help you in your current role or prepare you for a new professional challenge.
What Is Python?
According to the Python Software Foundation, an organization committed to supporting the community of Python programmers and open-source technologies related to the language, Python is “an interpreted, object-oriented, high-level programming language with dynamic semantics.”1 This means that its syntax is closer in appearance to how humans think and communicate than “lower-level” languages, and that it handles making a number of important decisions and establishing complex parameters when a program written in it is run (as opposed to other languages that require the programmer to make these decisions ahead of time).2
But what does this mean for you? It means that you don’t necessarily have to have a degree in computer science to be a successful Python programmer. Even if you’re just working your way through some beginner Python projects, or you’re new to programming entirely, you’ll find that it’s designed to flexibly accommodate demands across a wide range of uses. For this reason, Python has become particularly popular in recent years in the burgeoning fields of business analytics and data science.
Using Python for Data Science
Python’s significance in the field of data science is well-established: 87 percent of data scientists report using the language regularly in their current role, and 66 percent of data science job postings mention Python.3 But why is this the case? What does Python offer to data scientists that other programming languages do not?
Python is popular for data science, particularly in business applications, largely because of its productivity. It allows for complex tasks to be accomplished with fewer, more concise lines of code than many other languages, which makes it an invaluable asset in a fast-paced business environment.3
It is also an extremely versatile language, with potential applications across many functions of great importance to data scientists, such as data mining, data analysis, and machine learning. And its popularity is an asset as well: There is a broad, robust community of Python users and innovators who are typically more than happy to help each other devise clever and potentially reusable solutions to programming problems.
Python Libraries: The Best Bet for Data Science
Where Python really shines for data science and business applications, however, is in its ability to streamline work through the use of several devices to store and quickly recall large chunks of complex code: modules, packages, and most crucially, libraries.
Modules are files written in Python code; they are the basic building blocks of programs written in the language, and they can be stored in directories called packages. But Python is particularly efficient in its use of libraries, which are large compilations of modules and packages that can orchestrate common but complex functions without requiring the composition of new blocks of code.
Most common programming languages have standard libraries of code, but Python stands out for its especially robust set of useful libraries, including but not limited to its standard library.4 Data science professionals typically use one or more of several popular Python libraries in the course of completing their work:
- Pandas: An essential library for data science, Pandas strives to make working with large, structured data structures intuitive, easy and fast5
- Matplotlib: This library helps generate clear, legible, publication-quality visual plots from large, complex data sets,6 a particularly useful function for professionals who must interpret and impactfully communicate the value of data to a business audience
- Requests: This is a Python library that lets users easily send HTTP/1.1 requests by automating the creation of query strings for the transmission of data in a URL7
Python vs. Excel: Choosing the Right Tool
If you are just beginning your move into data science or business analytics, you may be more familiar with Excel as a tool for processing and visualizing data. Excel has risen to wide popularity as part of Microsoft’s Office suite of programs, and it is common for many professionals to become adept at using it either during their undergraduate education or early on in their careers.
But as you begin to work with the kind of data sets that are extremely valuable to business organizations today, you’ll likely come to find that Excel is not quite capable of handling the demands you might have for it. It struggles to handle very large data sets, and it is not particularly user-friendly when it comes to reproducing the steps and structure of previous analyses on new data sets.8
The benefits of Python described above tip the scale decisively in its favor if you’re debating which tool to use for data analysis. The replicability of processes enabled by its powerful array of libraries and packages can greatly reduce the amount of time and energy you need to sink into analyses that are similar in form but differ in content. And the intuitive nature of its syntax can allow users in any industry to easily translate their goals and thought processes into parameters that define a successful analytical program.
Sink Your Teeth Into Python at W&M
The Online Master of Science in Business Analytics can help you start building your competitive advantage for a data science role with training in Python business applications and other key analytics tools. And if you are just beginning your career pivot into a data-centric role, explore the Online Foundations of Business Analytics Certificate program which features an introductory Python programming course and can either lead you to your first analytics role or provide a leg up on admission to the Online MSBA.
1. Retrieved on January 2, 2020, from python.org/doc/essays/blurb2. Retrieved on January 2, 2020, from medium.com/@haydnjmorris/page-2-dynamically-typed-vs-statically-typed-languages-e507ac4634963. Retrieved on January 2, 2020, from techrepublic.com/article/why-python-is-the-real-language-of-data-science-not-r4. Retrieved on January 2, 2020, from medium.com/@trungluongquang/why-python-is-popular-despite-being-super-slow-83a8320412a95. Retrieved on January 2, 2020, from pypi.org/project/pandas6. Retrieved on January 2, 2020, from matplotlib.org7. Retrieved on January 2, 2020, from 2.python-requests.org//en/master/user/quickstart8. Retrieved on January 2, 2020, from qz.com/1063071/the-great-r-versus-python-for-data-science-debate