Caltech Bootcamp / Blog / /

Why Use Python for Data Science?

Why Python for Data Science

When performing data science tasks, you can use many methods, approaches, and tools. Still, today, we are focusing on one element exclusively: why you should be using Python for data science.

This article tells you why data scientists rely on Python for data analysis and data science. It lists the primary reasons why Python is preferred, the benefits of using Python, and how Python is used for data science. It also includes a data science bootcamp you can take to learn how to use Python for data science.

But before we get too deep into the case for Python and data science, let’s explain what we mean by the Python programming language.

A Brief Python Overview

Python is a general-purpose, high-level programming language supporting structured, object-oriented, and functional programming paradigms. It was created in the late 1980s by the Dutch programmer Guido van Rossum, who wanted to make a programming language that descended from the ABC programming language but would also appeal to Unix/C hackers. The name is a call back to the famous British comedy troupe Monty Python’s Flying Circus.”

Now on its third version, Python is among the top five most-used programming languages today, sharing popularity with JavaScript, HTML/CSS, SQL, and TypeScript.

So why, of those top five programming languages, should you learn Python for data science? You will find that the reasons for using Python for data analysis and data science are the same reasons you should learn the language, even if your position doesn’t involve data science.

Also Read: A Beginner’s Guide to the Data Science Process

Data Analysis vs. Data Science

Let’s clarify these two terms and establish what makes them distinct. Data scientists extract meaningful insights from data, while data analysts use data to resolve the day-to-day questions posed while running a business. The data analyst is more about the present, while data scientists use the available data to predict how something will fare in the future. So, data scientists should have solid business acumen, while data analysts must know their way around spreadsheet tools.

Additionally, there are many situations where the distinction between these professions could be more explicit, resulting in people sometimes needing clarification on the two concepts. However, due to the overlapping nature of data analytics and data science, Python is essential in both cases. Thus, whether you’re a data analyst or a data scientist, it’s crucial to understand Python.

Why Use Python for Data Science and Data Analysis?

  • It’s easy to learn and understand. Python has a relatively low learning curve, emphasizing readability and simplicity, making it perfect for programmers of all skill levels. Tasks in Python require fewer lines of code, so you spend more time programming and less time wrestling with code. Python’s simplicity makes it an excellent choice for programming newbies to learn data science.
  • It’s open source. In terms of price, you can’t beat “free.” Python is free to use and distribute, and its supporting libraries are also free.
  • It’s highly scalable. Python is all about versatility and scalability. One person or five thousand people can work on the same project using Python. The language’s flexibility lets it be used in many projects and for countless purposes. It runs on almost every platform and operating system and interfaces with most API-powered services and major libraries. Python is handy when data analysis functions and tasks must be integrated with web apps and cloud computing platforms or as part of a more significant, complex project. Additionally, Python is compatible with Hadoop, the most vital and popular open-source big data platform.
  • It enjoys an extensive library and community support. Speaking of libraries, the number of libraries has exploded over the years, many of them facilitating data science tasks such as data analysis and data visualization. Python has a vast, active community that has designed many of the language’s more valuable libraries. The library number exceeds 137,000, including NumPy, Pillow, Scrapy, Requests, Tkinter, Six, Pandas, SciPy, Scikit-Learn, and PyMySQL. It comes down to this: Python’s sheer popularity means a more extensive selection of free libraries to choose from, which in turn makes Python a versatile tool and the most logical choice for data scientists and others, including professionals in industrial and academic circles.
  • It excels in the fields of machine learning and algorithms. Python is an ideal resource for machine learning projects because of the plethora of machine learning libraries available. Google’s machine learning library, TensorFlow, was developed using Python and libraries like PyBrain and scikit-learn factor in developing regression, classification, and clustering algorithms. Also, Python’s very nature makes it easy for machine learning engineers to “do the math,” a critical factor in implementing algorithms.
  • It’s platform-agnostic. Python lets developers run their code on Windows, Linux, Mac OS X, and UNIX. With almost any operating system, you can run Python anywhere, on any machine.
  • It’s a great skill to have on your resume. Many data science, software engineering, machine learning, and artificial intelligence positions require at least a rudimentary knowledge of Python. Having such a widely used programming language on your resume or CV doesn’t hurt.

Also Read: What Is Data Mining? A Beginner’s Guide

Are There Any Downsides to Using Python for Data Science?

For example, it’s unsuitable for making mobile apps, which could prove problematic considering the vast popularity of mobile apps. Additionally, Python runs slower than compiled languages, has a high memory consumption, and its garbage collection could lead to high memory losses.

How is Python Used for Data Science?

Python is commonly used in data analysis, automation or scripting, machine learning, web development, software testing and prototyping. Additionally, Python is used in the gaming industry to create prototypes, quantitative and qualitative financial analysis, natural language processing (NLP), and search engine optimization (SEO). Many high-profile companies use Python for data science, including Facebook, Google, YouTube, Intel, IBM, Instagram, Netflix, and Spotify.

Specifically, data scientists routinely use Python for applications such as:

  • Computer Vision. Computer vision is a fusion of artificial intelligence and machine learning that involves learning algorithms and specialized methods to interpret what a computer sees and decide on a course of action.
  • Data Analysis. Data analysis involves collecting, transforming, and organizing data to create informed, data-driven decisions and future predictions. Additionally, data analysis helps find possible solutions for business problems.
  • Data Visualizations. Data visualization presents data graphically or in pictorial format, helping the organization easily understand large quantities of data. This advantage allows decision-makers to identify trends and patterns efficiently and make better decisions.
  • Deep Learning. Deep learning is classified as a subset of machine learning that employs artificial neural networks to model and solve difficult, complex problems.
  • Image processing. Image processing deals with using computer algorithms to modify and analyze digital images. Image processing enhances the visual quality of images, extracts helpful information, and renders images suitable for additional analysis or interpretation.
  • Machine Learning. Machine learning (ML) is a subset of artificial intelligence (AI), focusing on creating systems that learn or improve performance based on the data they consume. The discipline of machine learning allows machines to learn without human intervention.
  • Natural Language Processing (NLP). Natural Language Processing is an AI field involving computer and human language interaction. NLP analyzes, comprehends, and generates natural language text and speech. NLP aims to allow computers to understand and interpret human language, like how humans process language.

Python is used extensively for data science, so most data science positions require at least a basic understanding of the programming language. Python is an inherently object-oriented, general-purpose, and dynamic language supporting multiple paradigms, from functional to structured and procedural programming.

Do You Want to Learn More About Data Science?

Data science is a very popular, fast-growing field, with more job openings being created every day. If this field piques your interest, consider this online post graduate program in data science. This comprehensive 44-week course covers all the main things you need to know, including how to use Python for data science. You’ll also gain generative AI skills by working with tools like DALL-E, ChatGPT, and Midjourney.

According to Indeed.com, data scientists can earn an annual average of $123,645. So, if you’re looking for a secure career and excellent compensation, start the process by signing up for this online bootcamp and squaring away the necessary data science skills.

You might also like to read:

Data Collection Methods: A Comprehensive View

What Is Data Processing? Definition, Examples, Trends

Differences Between Data Scientist and Data Analyst: Complete Explanation

What Is Data Collection? A Guide for Aspiring Data Scientists

What Is Data? A Beginner’s Guide

Data Science Bootcamp

Leave a Comment

Your email address will not be published.

What is A B testing in data science

What is A/B Testing in Data Science?

This article explores A/B testing in data science, including defining the term, its importance, when to use it, how it works, and how to conduct it.

Data Science Bootcamp

Duration

6 months

Learning Format

Online Bootcamp

Program Benefits