There’s a reason why Python is frequently crowned the most popular programming language among professional developers and people learning to code. Python is an easy-to-read, versatile programming language that’s used in many different areas of software development.
For example, Python has a stellar reputation in data science, explains Ada Morse, Codecademy Curriculum Developer in Data Science. “Python’s the standard, so it’s a good one to know,” she says. Want to learn the ins and outs of using Python for data science? Our new free course Getting Started with Python for Data Science will teach you how to use Python to explore real-life datasets and answer questions using data.
The exercises in Getting Started with Python for Data Science are designed to mimic the work that you’d do as a Data Scientist or Data Analyst, so it’s a great way to test the waters and see if you like the field, Ada says. The course is also open to absolute beginners and anyone who wants to learn more about data. We’ll walk you through everything you need to know to use the main data science tools — Python, Pandas, and Jupyter Notebooks — and you’ll see firsthand what makes Python so awesome. Read on to learn more about why you should learn Python if you want to work with data.
Python has a simple, English-like syntax.
Data science can be intimidating for folks who aren’t super comfortable with numbers and math. With Python, rather than having to make sense of a jumble of complicated symbols and equations on a screen, the syntax looks like a natural (or spoken) language. Python is designed to be readable, which is one reason why it’s so approachable for coding beginners, Ada says.
Since Python is so easy to learn, you can start learning more complicated concepts with it quickly. Compared to other data science languages (like Julia, for example), you don’t need as much theoretical computer science knowledge to work with Python, Ada says. “Python handles some of the technical details for you,” she says.
There are lots of handy Python add-ons.
The neat thing about Python is that there are tons of libraries and frameworks that handle standard tasks in different areas of software development, from machine learning to data science. These prewritten code packages do a lot of grunt work for you, so you can write Python code faster and build apps that are pre-organized and structured.
For example, in Getting Started with Python for Data Science, you’ll get to use Pandas, a Python module that’s used for data manipulation. “Pandas is really helpful because instead of having to reinvent how to work with tables of data, a lot of the basic code has already been written,” Ada explains. “Now your job is just to apply that to the dataset that you want to work with.”
Some go-to Python libraries for data science include NumPy, MatPlotLib, and SciPy. Read this blog to learn more about the various Python libraries and tools that you can take advantage of while learning the language.
You can build other cool things with Python.
Python is not strictly a data science language; you can use it to create websites, test software, and build machine learning models. The course Getting Started with Python for Data Science is a great introduction to common coding principles that will come up again as you work on different coding projects or learn new languages altogether. Take a look at all of Codecademy’s Python courses to get a sense of how versatile the language is — you might be inspired to explore more in-depth Python topics, like the skill path Machine Learning Fundamentals or Build Python Web Apps with Flask.
You really can’t go wrong choosing Python as a first language whether you want to pursue data science or another specialty. And once you know one programming language, it’s typically easier to pick up other ones because there are so many overlapping concepts across languages.
Python is extremely popular.
As a beginner, you’ll probably find yourself searching lots of different coding questions on Stack Overflow or Google. Since so many people use Python, it’s easy to find reputable resources and documentation. “You’ll be able to find tutorials or courses or something in Python, whereas a less popular language might be harder to find those sorts of resources,” Ada says.
Speaking of, Codecademy has lots of resources that you can turn to while learning Python (or any other language), including articles and explainers, our community-driven code documentation called Docs, practice projects, plus courses and tutorials.
Python is the industry-standard programming language for data science. “The popularity of Python means that most Data Scientists ‘speak’ Python to a certain degree,” Ada says. If you’re interested in having a career in data science, knowing Python will help you stand out as a serious candidate — and enable you to jump right in working on projects once you get hired.
Even if you don’t aspire to become a professional Data Scientist, knowing how to work with data is a very important and marketable skill. “It’s hard to think of a job that wouldn’t have any sort of contact with data these days,” Ada says. Becoming the go-to Python and data person at your organization can boost your career potential in any field.
Ready to learn Python for data science?
In our free introductory course Getting Started with Python for Data Science, you’ll get hands-on practice working with real datasets in Python. We’ll teach you how to work with the trifecta of data science tools: Python, Pandas, and Jupyter Notebooks. By the end of the course, you’ll be able to explore and summarize a dataset, filter data to find specific categories, and format raw data so you can answer a data question, Ada says. This course is great for absolute beginners, and will set you up nicely to take another Codecademy’s data science course.
If you’re loving using Python to answer questions about data, maybe this could be the start of a new career for you? Be sure to check out the Codecademy career paths in data science to learn the skills you need to work in this exciting area of tech.