Learning Python, R, and SQL is a good place to start. With these three languages, you’ll have the basic skills needed to start your career in Data Science.
What Are the Best Programming Languages to Learn for Data Science?
Ready to start your career in Data Science? In addition to a solid knowledge of statistics, you’ll first need to learn a variety of programming languages to help you analyze data and present your findings to others in your organization. However, if you’re just getting started, a comprehensive list of every programming language used in Data Science can seem overwhelming. That’s why we’ve compiled a concise list of some of the best programming languages for Data Science and which ones you should learn first.
Luckily for the budding data scientist, one of the best programming languages for Data Science is also one of the easiest to learn! Often referred to as “the Swiss Army knife of programming,” Python is a general-purpose language that can be used for almost anything with the help of frameworks. Due to its myriad of uses, and the ease of reading and writing the code, Python has risen in popularity over the last decade. For the same reason, the Data Science community has gravitated toward Python and has developed many useful frameworks for the field.
TensorFlow, one of the leading frameworks for Artificial Intelligence, is compatible with Python. This makes learning Python especially important if you want to work in AI or Machine Learning. There are just so many helpful libraries and frameworks for Python that you’ll use it regularly for Data Science.
Arguably the most popular programming language in statistical computing, R is a must-have tool for Data Scientists. R can be used for creating statistical models, graphing data sets, and solving complex mathematical equations. It can even handle matrix algebra. Instead of using a graphing calculator as you did in college, you’ll graduate to using R as a Data Scientist. Unlike Python, R is not a general-purpose language; it is almost exclusively used for statistics and not much else. Additionally, R is much more difficult to learn and has a high learning curve when compared with Python.
Since you’ll be working with data, you’ll need to know how to query and edit information in databases. Therefore, knowing SQL (Structured Query Language) is nearly a prerequisite for any Data Science job. Technically, SQL isn’t a programming language at all, but it’s one of the languages you’ll use most often on the job. Fortunately, SQL is relatively easy to learn when compared with other languages. And, once you learn SQL, you will have an easy time picking up other database management languages. Like most human languages, 90% of your daily usage of SQL will only involve about 10% of the language. It takes almost a lifetime to master the language, but with a working knowledge of SQL, you’ll be able to perform the basic tasks you encounter every day.
Java is commonly used in the back-end of many enterprise systems, making it one of the best programming languages for Data Science. Additionally, most frameworks and tools for Big Data are all written in Java, including Spark, Hive, Flink, and Hadoop.
As its name may suggest, it’s compatible with the Java Virtual Machine and is used for tons of cross-platform software. The JVM (Java Virtual Machine) is a program that lets you write Java code for lots of different platforms. Without such a tool, you’d have to write separate code for each platform. Other programming languages like Scala are based on Java and use the Java Virtual Machine.
The name Scala comes from a combination of the words, “Scalable” and “Language.” In this context, “scalable” refers to shorter lines of code and shorter execution times, which allow larger programs to run better. Scala is extremely concise and eliminates lots of unnecessary code. For example, something that may take 10 lines to perform in Java can be accomplished with just one line of code in Scala.
Knowledge of this programming language is important for the Hadoop and Spark frameworks, which are used for big data. Scala is also interchangeable with Java code. For example, you can write part of a program in Scala and the rest in Java—and it will run! So, if you already know Java and you want to get into Big Data, learn Scala and how to use the Spark and Hadoop frameworks.
Most of the programming languages we use today date back to the 1990s or earlier, with Java as the exception to the rule. Julia, however, was released just 5 years ago and has surged in popularity ever since. Despite being a relatively young programming language, Julia is now being used by tech giants like Apple, Amazon, Facebook, Google, Microsoft, IBM, and many more. How exactly did this happen?
Julia gives users the best of both worlds by combining the ease-of-use provided by Python and the quick execution of C and C++. A great tool for Data Science, Julia is extremely useful for AI, Machine Learning, and Risk Analysis. Julia is currently one of the fastest-growing programming languages and will play a growing role in Data Science in the years to come.
Still not sure what the best programming languages for Data Science are? Learning Python, R, and SQL is a good place to start. With these three languages, you’ll have the basic skills needed to start your career in Data Science. Down the road, you’ll want to learn additional programming languages like Java, Julia, and Scala. However, these aren’t quite as urgent and can wait for a later point in your Data Science career.
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