Programming language knowledge is only relevant for web developers and programmers, right? Well, not quite. In today’s world where data is king, knowledge of programming languages such as Python can help you build more impressive and effective data analysis models, providing you with better results. In 2022, Python usage increased 22.5% from the previous year, and there’s another fact we can’t ignore: the vast majority of data science and machine learning models are built using Python, meaning you’ll be setting yourself up for success if you choose to learn Python.
In this article, we’ll discuss why Python is the right choice for aspiring data scientists, in addition to providing helpful tips about learning Python.
The Basics of Python
With so many programming languages out there, how did Python become one of the most commonly used languages in data science? It’s easy to understand when you look at the details:
It’s flexible: with Python, there are very few limitations; you can create your own data models or simply play around and see if there’s some new insights you can uncover with Python. And as data becomes more valued in every industry, the need for innovative models will only continue to grow.
It’s versatile: from web development to data science, Python is used across the tech industry, meaning it’s a transferable skill that’s always valued.
It’s easy to learn: Python is based on principles of simplicity and readability, which translates well for those looking to learn it–especially those without any experience in coding. This ease of learning means you’ll spend less time learning and more time creating.
It’s open source: designed to run on a variety of platforms and free to use, Python is truly accessible to all. In addition, because it’s open source, there are tons of community libraries available to help you find inspiration or help if you get stuck.
Python as a data scientist
You now understand why Python is a popular programming language, but how does it intersect with data science? Through a few very valuable characteristics:
Data science is based on gathering unprocessed data and finding patterns or trends; Python libraries are specifically skilled in data retrieval, facilitating the process of finding valuable information.
Web scraping is another important step of data science and with the help of Python libraries, this process is much faster and valuable data is gathered in real-time, providing data scientists with the most recent and up-to-date information possible.
Data findings are useless if you can’t properly communicate your findings to the rest of the team, and Python has tools just for that; Python has libraries focused on visuals and graphic management, ensuring your results are clearly understandable.
Where to start in Python
As you’ve probably already gathered, Python is made up of libraries, modules, and frameworks that each have a specific purpose and to excel in data science, it’s important to familiarize yourself with the following:
Pandas: perfect for various data science needs, Pandas is a data analysis and visualization library used for data fill and cleaning, merging, statistical analysis, and more.
NumPy: this library is key for using Python for data science, and is required if you want to work with Pandas.
SciPy: if you’re looking to work with more complicated computing processes, SciPy is a good place to start; it can handle signal processing, optimization, and statistical analysis.
TensorFlow: this common library is used for large-scale machine learning and numerical computation processes, in addition to neural network projects.
Learning Python for Data Science
Now that we’ve convinced you that Python is the right choice for kickstarting your data science career, it’s time to get into the good stuff: how to learn Python, specifically for data science applications. As with most things you can learn in the tech sphere, this isn’t a one-and-done skill to check off your list; the more time you invest in learning Python, the more you’ll be able to do with it.
Take the time to figure out what’s best for you to learn for your aspirations and take it seriously: the more you put it into it, the more you’ll get out of it.
Breakdown your why of learning Python
As we mentioned earlier, Python can be used for a wide range of tasks, which is why it’s important to figure out how Python will be used to advance your career. Ask yourself these questions before you dive into Python to help refine your learning journey and make sure you’re making the most of your efforts:
What are your career goals?
What industry do you want to work in?
What are you interested in?
What experience do you have with Python or other programming languages?
Start with the basics
We totally understand that you want to dive right in and get your hands dirty, but it’s important to master the basics of coding, such as variables, data types, and operators before you advance too far. Creating a solid basis will help you significantly and it’s important to set yourself up for success by honoring the need for this foundation.
Get writing
With the basics under your belt, there’s nothing in your way: it’s time to start writing code and playing around with different libraries. The only way to get better at coding is to practice and there are tons of exercises and videos online that can help you start writing code. This is also a great way to deal with bugs and errors, fixing them as you go to enhance your knowledge.
Learn from others
As one of the most popular programming languages out there, there is a huge Python community–and this has two major benefits. You can get help when needed from more experienced coders and check out what other people are doing for inspiration and guidance if you’re not sure where to head next.
In conclusion, learning Python is not just a smart choice for aspiring data scientists—it's essential. Its versatility, ease of learning, and vast array of libraries make it the ideal programming language for analyzing data, building models, and creating visual representations of your findings.
By mastering Python, you'll not only gain a competitive edge in the rapidly growing field of data science, but you'll also open the door to endless opportunities in various industries. So, start your Python journey today and position yourself for success in the data-driven world of tomorrow.
About the Author:
Juliette Carreiro is a tech writer, with two years of experience writing in-depth articles for Ironhack. Covering everything from career advice and navigating the job ladder, to the future impact of AI in the global tech space, Juliette is the go-to for Ironhack’s community of aspiring tech professionals.
