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.

In a word, yes. Data Scientists code. That is, most Data Scientists have to know how to code, even if it’s not a daily task. As the oft-repeated saying goes, “A Data Scientist is someone who’s better at statistics than any Software Engineer, and better at software engineering than any Statistician.”

The amount of programming (a.k.a. coding) they actually do, however, depends on their role and the tools they’re using. A few examples of the things Data Scientists can expect to program:

• Analysis scripts, usually in R or Python, with the intention of generating actionable insights.
• Prototypes of digital products. Using Python, the goal is generally to prove the efficacy of a new product or feature, which allows a Developer to then build it.
• Production code. In smaller companies, Data Scientists often have full responsibility for this, and may have to make use of Ruby on Rails or Java (in addition to the more commonly used data science languages) to achieve this.

Data Scientists use a variety of different programming languages in a variety of different ways in their day-to-day work, but there are some foundational programming languages that every Data Scientist needs to master. The most used programming languages for data science are:

With a manageable learning curve and an array of libraries that allow for nearly endless applications, Python is the top programming language of choice for the many Data Scientists who appreciate its accessibility, ease of use, and general-purpose versatility. In fact, BrainStation’s 2019 Digital Skills Survey found that Python was the most frequently used tool for Data Scientists overall. 

Since its introduction in 1991, Python has built up an ever-growing number of libraries dedicated to carrying out common tasks, including data preprocessing, analysis, predictions, visualization, and preservation. Meanwhile, Python libraries like Tensorflow, Pandas, and Scikit-learn allow for more advanced machine learning or deep learning applications. Asked about their preference for Python over R, Data Scientists cited Python’s tendency to be faster than R, and better for data manipulation.

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.

A free, open-source programming language that was released in 1995 as a descendant of the S programming language, R offers a top-notch range of quality domain-specific packages. Its visualization library ggplot2 is a powerful tool, and R’s static graphics can make it easier to produce graphs and mathematical symbols and formulae.

Yes, Python does have a speed advantage over R (and R does feature a steeper learning curve than the more approachable Python), but for specific statistical and data analysis purposes, R’s vast range of tailor-made packages gives it a slight edge. It’s worth noting that, unlike Python, R isn’t a general-purpose programming language—it’s intended to be used specifically for statistical analysis.

SQL, or “Structured Query Language,” has been at the core of storing and retrieving data for decades. SQL is a domain-specific language used for managing data in relational databases—and it’s a must-have skill for Data Scientists, who rely on SQL to update, query, edit, and manipulate databases and extract data. 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.

Though SQL is not as useful as an analytical tool, it’s highly efficient and crucial for data retrieval. This makes SQL a particularly helpful tool for managing structured data, especially within large databases.

In addition to the core data programming languages Python, SQL, and R, there are other data science languages that can potentially have more niche applications:

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. Java is also highly regarded for its performance, type safety, and portability between platforms.

User-friendly and flexible, Scala is the ideal programming language when dealing with big 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. However, because the language is relatively young, Julia lacks the variety of packages offered by R or Python—for now.

A numerical computing language, MATLAB is used for high-level mathematical needs like Fourier transforms, signal processing, image processing, and matrix algebra, contributing to its use in academia and industry. If you have a strong mathematical background, you might learn MATLAB in as little as two weeks. Like Julia, however, MATLAB hasn’t yet been widely adopted by data professionals.