How to Become a Data Scientist
Is Data Science Hard to Learn?
Because of the often technical requirements for Data Science jobs, it can be more challenging to learn than other fields in technology. Getting a firm handle on such a wide variety of languages and applications does present a rather steep learning curve. Of course, this is one of the reasons for the current global shortage of data science professionals—and why they’re in such high demand.
What Programming Languages Should Data Scientists Learn?
One of the biggest challenges in working data science is the number of different languages and applications you’ll need to learn. Unlike some fields of tech, where it has been possible to focus on one or two platforms, the interdisciplinary nature of data science means you’ll need to learn at least a half-dozen languages—and use all of them in combination.
A must-have, but one with a manageable learning curve. Python is the top programming language of choice for many Data Scientists, who appreciate its accessibility, ease of use, and versatility. BrainStation’s 2019 Digital Skills Survey found that Python was the most frequently used tool for Data Scientists overall.
Because it’s purpose-built for data analytics, R tends to be quite different from other platforms, giving it a reputation for being more difficult to learn than other analytics software. Even with ample experience using other data science tools, you may find R quite foreign at first. It’s worth the effort, however: it boasts nearly every statistical and data visualization application a Data Scientist might need, including neural networks, non-linear regression, advanced plotting and more.
Another must-have. Fortunately, SQL is relatively easy to pick up, quite readable, and intuitive. Because its commands are limited to queries, it usually takes only two or three weeks for beginners, and far less for experienced programmers. Once you have an understanding of SQL, you’ll be able to update, query, edit, manipulate, and extract information from structured sets of data, especially large databases.
Although easier to learn than its forerunner, C++, Java is still a bit more challenging than Python, thanks to its lengthy syntax. Some experts suggest that it takes nearly a month to learn the basic concepts of Java, and another week or two to begin applying those ideas in a practical way. Java is a good tool for weaving data science production code directly into an existing database; the popular statistical analysis utility Hadoop runs on the Java Virtual Machine.
User-friendly and flexible, Scala is the ideal programming language when dealing with great volumes of data. Applications written on Scala can run anywhere that Java runs, making it useful for complex algorithms or large-scale machine learning. Scala does feature a steeper learning curve than some other programming languages, typically taking several weeks to get a handle on, but its massive user base is a testament to its usefulness.
A much newer programming language than the others on this list, Julia has quickly made an impression thanks to its lightning-fast performance, simplicity, and readability, especially for numerical analysis and computational science. That’s not to say you can learn it overnight; while it’s relatively easy to jump into and begin experimenting right away, expect it to take a few months to master Julia. But once you have, it’s a great tool for solving complex mathematical operations—one reason it’s a fixture in the financial industry.
A popular statistical analysis tool, this numerical computing language is useful for high-level mathematical needs like Fourier transforms, signal processing, image processing, and matrix algebra, contributing to its widespread use in academia and industry. If you have a strong mathematical background, you might learn MATLAB in as little as two weeks.
While you won’t likely use all these programs every day, you’ll want to at least be familiar with each of them and their capabilities.
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