Is Data Science and Python a Good Combination?

Just a few short years ago, a sharp mind might have predicted that data science using Python was on the brink of becoming a major trend. Today, that same person would confidently tell you, “It’s already a big deal.” Acclaimed historian and author Yuval Noah Harari, whose insights have captured the attention of tech giants like Bill Gates and Mark Zuckerberg, has boldly declared data as the new divine. Without diving too deep, the central message is clear: data is invaluable, making Data Science – and its companion tool, Python – equally crucial. Python is a preferred programming language for data science, and with an abundance of resources and foundational courses available, it’s time to consider if this is a skill worth acquiring.

In this article, we’ll thoroughly examine this pressing question.

The Importance of Data Science

Data, in this sense, refers to a massive collection of information – think of the billions of letters paired together in a DNA helix. Data Science is the process of employing scientific methods, especially computer programs, to glean insights or knowledge from this vast expanse of data, whether it’s structured or unstructured. To put it in context, decoding the human genome from the DNA helix data is an example of Data Science in action.

Let’s break down the roles and responsibilities of data science and data scientists:

  1. Problem Identification: Recognizing and defining a business problem that can be solved using data science skills.
  2. Data Collection: Amassing large datasets from diverse sources, like a company’s email usage statistics or a city’s demographic data. This information can be sourced from databases, web servers, and more.
  3. Data Mining: Discovering significant patterns in the collected data – for instance, noticing a frequent link between the 10-14 age group and mentions of a specific video game.
  4. Data Pre-processing: Cleaning the data to remove duplicates or rectify inconsistencies that might skew the results.
  5. Data Analysis and Modeling: Selecting the right variables and finding the optimal model for data modeling to extract meaningful knowledge.
  6. Visualization: Creating graphs, charts, or animations to visually represent the data, enhancing the understanding of the insights gained through the previous steps.

Understanding data science is more crucial than ever, and with Python as a tool, we’re well-equipped to navigate this data-rich world.

Look at the following image. It is compiled from large amounts of data that made no sense before, but after proper analysis and visualisation, it gives meaningful information within a few seconds of looking at it.

Data science and Python image for article
(Image source: Towards Data Science)

Understanding Python’s Role in Data Science

Now, let’s dive into where Python fits into this intricate world of data science, and what exactly it is.

Picture this: you’ve got a list of a million people along with their favorite colors, but the data is all over the place. Your task? Figure out the most and least favorite colors. Sure, you could go the manual route or use basic tools like MS Excel, but that would be incredibly time-consuming—and as we all know, time is money. Enter Python.

With Python, instead of tediously sorting through data yourself, you could write a piece of code in a matter of minutes (or hours, depending on your skill level) to sort the data and deliver the answers you need. When you apply this to more complex scenarios, the importance of a powerful yet user-friendly programming language becomes crystal clear. And that’s precisely where Python shines.

Python is a high-level, object-oriented programming language that’s easy to learn—a point emphasized by its developers. Even though it’s been around for over two decades, it continues to be extremely popular. Here are some of the reasons why Python stands out as a top choice for data science:

  • Ease of Learning: Python features a syntax designed for readability, making it accessible not just to programmers but to professionals from various fields, such as scientists, accountants, and more. This is crucial because being a data scientist doesn’t require you to be a computer engineer.
  • Efficiency: Python allows you to do more with less code, saving valuable time.
  • Memory Management: As a high-level language, Python frees you from worrying about memory management issues that can arise in languages like C++.
  • Cross-Platform Compatibility: Python works on various operating systems, including Windows, Mac, and Linux. Plus, it’s open-source and free to use.

So, What About Career Prospects in Data Science with Python?

Absolutely, the prospects are promising! As Thomas Davenport and D.J. Patil highlighted in their October 2012 Harvard Business Review article, they’ve dubbed the role of Data Scientist as the “sexiest job of the 21st century.” If you’re considering diving into Python and data science, you’re on the right track to a thrilling and rewarding career.