In the last few years, data science has changed drastically and seen some real growth as organizations across all sectors are adopting a culture of data-driven decision-making. Data scientists rely on a wide range of data science and analytics tools and statistical methods to collect, analyze, visualize, and interpret data to gain insights and assist their organizations grow. The programming language is the core of these tools and techniques and plays the most important role in a data science workflow.
What are Programming Languages?
Just like how people communicate with each other in their language, programming languages help humans communicate with computer systems. Though they are widely used in data science, they also serve an important role in computer science, engineering, software development, machine learning, and other new emerging applications.
Most data science roles, including data science, data engineer, or data analyst, require knowledge of at least one of the programming languages like Python, R, SQL, Julia, or others. These languages have their own strengths and weaknesses. Therefore, a lot of data science professionals want to learn multiple languages to expand their capabilities.
Let us dive deeper and understand which are the most popular programming languages in data science, where they are used, and what their strengths are.
Overview of data science programming languages
Data scientist uses different types of programming languages for various purposes, such as:
- Data storage – Languages like SQL help professionals build access and extract information from a database
- Data engineering – Data engineers use languages like Python to pre-process data and make them suitable for an analysis
- Data modeling – Python is again used to create data science models that can solve real-world business problems
- Data visualization – Popular libraries like Matplotlib and Tableau are used to create visually appealing data visualizations to transform complex insights into easy-to-understand charts and graphs.
Programming languages are used for different types of applications, from web development to statistical computing, and have their own strengths for particular applications. Therefore, professionals need to choose the right language to master their expertise and push their capabilities further.
Top Programming Languages in Data Science
So, here are the most widely used data science programming languages that you must master to use in a variety of projects.
1. Python
Python is undisputedly the most popular programming language, not just for data science and statistical modeling, but for web development, software engineering, and other applications as well. Around 90.6% of data science professionals use Python for the ease of use and flexibility it provides (source: data science skills survey).
Python offers simple syntax and rules, and therefore, it is easy to learn and use. It can be used for projects of all sizes. It also offers a huge collection of libraries, such as NumPy, Matplotlib, and pandas, which makes data science work easier. So, whether it is data manipulation or data processing, you can use the pre-written codes in these libraries and work on your projects without needing to work from scratch.
2. R
The R programming language is known for its statistical capabilities. Around 40% of professionals use R for their data science workflows. It is an open-source programming language and offers a huge community of experienced professionals who provide technical assistance and resources for new users.
As of now, R has over 14,000 packages (resources of pre-written codes) that professionals can use for various functions.
3. SQL
SQL stands for Structured Query Language, and it is used by data science professionals to build relational databases by organizing information into tables. It is the second most popular language after Python, with 53% of professionals using it.
Data engineers use it for data manipulation by aggregating and organizing depending upon specific questions. SQL is also used for querying and retrieving specific data from databases for analysis.
4. Java
Java is a powerful object-oriented programming language in data science, popular for its portability and performance. Data science professionals use it for building large-scale data processing applications and integrating with big data tools like Hadoop. Java’s scalability and high speed makes it a perfect choice for real time data analytics and enterprise level data science solutions.
5. Scala
Scala is a functional as well as object-oriented programming language, highly suitable for parallel data processing. It can run on both JVM and can be integrated seamlessly with Apache Spark (a popular big data framework). Scala is known for its ability to handle large data sets efficiently and write concise code for distributed computing.
6. Julia
Julia is another popular and high-performance data science programming language designed for numerical and scientific computing. It offers the Speed of C with the simplicity of Python. Thus, it has lately become a preferred choice in data science, machine learning, and large-scale linear algebra.
Julia is a great data science tool that provides excellent computational capabilities and hence is widely used in academia and research-heavy industries.
Conclusion
Data science is a vast domain and consists of a variety of tasks, from data collection to storage, and analysis to visualization. So, a single programming language is not sufficient for all your data science tasks. This is why these different programming languages with unique strengths and applications come in handy. With careful selection, you can enhance your expertise and make your projects more efficient.