Using Python for data science: What you need to know

Today, the amount of data that we have at our disposal is overwhelming. Which means the role of data science is more critical than ever. 

“Data science is the art of making sense of data, in an increasingly data-saturated world,” says Michael Berthold, CEO and co-founder of data science company, KNIME. It can also be a rewarding career path, with a high pay floor and plenty of opportunity for growth. 

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If you’re considering a data science career, one programming language you may want to learn is Python. Its combination of powerful libraries, ease of use, community support, and adaptability make Python an indispensable tool for data scientists. Interested in learning more? Here’s what you need to know about using Python for data science.

What is data science?

Data science is a multidisciplinary field that attempts to garner meaningful and actionable learning from complex data. It combines aspects of statistics, computer science, AI, and more. “Data scientists use scientific methods, algorithms, processes, and systems to extract insights and knowledge from data,” explains Justice Erolin, CTO at software development company, BairesDev. Often, the goal of data science is to inform decision-making and strategic planning for businesses.

Pursuing a career in data science can be quite lucrative. The average data scientist salary in the U.S. is $145,257, according to Salary.com, with the salary range typically falling between $130,842 and $159,732. Of course, actual salaries for data scientist positions can vary quite a bit depending on factors such as your level of education, additional skills or certifications you may have, the number of years you have spent in your profession, etc.

The outlook for this career field is also promising. The U.S. Bureau of Labor Statistics (BLS) doesn’t specifically track data scientists, but does include them in a broader category of “Computer and Information Research Scientists.” This category was projected to grow by 23% from 2022 to 2032, much faster than the average for all occupations. Given the increasing importance of big data, the demand for data scientists is expected to be at the higher end of this growth spectrum.

“What makes data science super valuable right now as a career path is that it’s the foundation of artificial intelligence and machine learning,” Erolin says. “Depending on the role, you’re able to make a real impact on the business or in other people’s lives.” 

What is Python?

Python is a high-level, interpreted programming language known for its simplicity, readability, and versatility. First released in 1991, Python has become one of the most popular programming languages in the world. 

“Python started off as an easy-to-use and learn programming language for quick prototyping,” Berthold explains. “In past years, a lot of functionality for working with data has been added and it has become one of the two primary languages next to R, which is still the preferred choice for statistical work.”

Berthold added that most new tools in the artificial intelligence/machine learning space have been developed in Python, so you need to know Python in order to work directly with those libraries. “It is worth noting that many aspects of data science require other languages as well, most notably, SQL for data engineering, but also Javascript for visualization,” he says. 

Using Python for data science 

Learning a programming language is easier than ever if you have the desire to practice, according to Erolin. He noted there are plenty of free and paid online resources, from YouTube to Udemy. “There are also communities on Reddit and Stack Overflow that can help you solve specific problems you run into,” he added. However, Erolin said the best learning is often done on the job. 

“Regardless of where you get your information and how you practice, aspiring data scientists should learn the fundamentals such as mastering the basic Python syntax, control structures, functions, etc.,” Erolin said. “You would then move on to familiarizing yourself with popular data science libraries such as Pandas and Seaborn, which provide tools for data manipulation, analysis, and visualization.” From there, he explained, you can dive into data science concepts such as statistics and machine learning and marry them together.

The takeaway 

If you’re looking for a rewarding and challenging STEM career, data science is a great route to pursue. “The career path is in-demand and requires a healthy dose of creativity since the job differs from project to project, and tools and techniques are constantly evolving,” Berthold says.

There are many skills and tools involved in data science, but you can set yourself up for success by learning Python, which is often integral to the work. Whether you want to take a DIY approach to learning Python with free online courses or enroll in a Python bootcamp, there are plenty of resources available to help you become an expert in this programming language and apply it to data science.



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