Top Programming Languages For Data Scientists To Master

According to the Data Science Skills Survey 2022, Python is one of the best programming languages for data scientists, with 90.6% of professionals using it for data science tasks and statistical modeling. On the other hand, about 38% of professionals use R, which offers around 14,000 packages—bundles of code that assist with data manipulation, statistical analysis, and other processes. Structured Query Language (SQL), used by roughly 53% of professionals, ranks as the second most popular language after Python. SQL is commonly employed to create relational databases and retrieve data from them for analysis, making it one of the essential data science languages.

Different data science programming languages offer various functions and capabilities that make them suited to multiple projects, such as web development, statistical computing, functional programming, and machine learning. Given their essential role in the data science industry, tech professionals must understand the distinct features of data science programming languages. Let us look at some of the best programming languages for data scientists in today’s blog:

Role of Programming Languages for Data Science

Programming languages for data science are essential for productivity, enabling efficient data storage, manipulation, and analysis. Data science spans multiple domains, such as machine learning, geospatial analysis, and automation, all requiring robust programming skills to function effectively. From data extraction to statistical analysis, the ability to code is integral to executing operations across these fields. Different stages of data science demand specific coding skills:

●        Problem Statement Understanding: At the initial stage, no programming is required. Instead, the focus is on understanding the tools and software necessary for the task.

●        Data Acquisition: For data profiling, SQL and NoSQL are crucial programming languages for data science for extracting data from databases or Web forms.

●        Data Cleaning: Raw data cleaning involves the usage of programming languages for data analysis such as Python and R, in addition to the tools such as Trifacta Wrangler and OpenRefine.

●        Data Analysis: Besides Python being popular for data analysis, R and MATLAB also offer special libraries for statistical use.

●        Data Visualisation: Reporting and presenting results in any way that involves a graphical display constitutes an essential aspect of data science. Due to various tools and libraries in Python such as Seaborn, Prettyplotlib, and Pandas, good visualisations are constructed to understand and display data.

Top Rated Data Science Programming Languages

Data science relies heavily on programming to manipulate, analyse, and visualise large datasets. With various programming languages available, here are the best programming languages to learn for data scientists:

Python

Python is one of the best programming languages for data scientists due to its simplicity, readability, and versatility. Its extensive libraries (e.g., NumPy, Pandas, and TensorFlow) make it ideal for data analysis, machine learning, and deep learning. Python’s ease of use and large community support make it a great choice for both beginners and professionals.

SQL 

SQL is essential for working with relational databases and is widely used to extract and manage data. As one of the most in-demand data science programming languages, SQL allows data scientists to query and retrieve data for analysis, making it a critical skill.

R

Known for its statistical computing and data visualisation capabilities, R is another powerful language for data science. It excels in data manipulation and provides a wide range of packages for machine learning and statistical analysis, making it highly suitable for complex analytical tasks.

Julia

Julia is growing in popularity for its speed and efficiency, especially when working with large datasets. It is designed for high-performance computing and offers features similar to MATLAB and R, making it a valuable tool for data scientists handling large-scale data analysis.

JavaScript

Although primarily used for web development, JavaScript is increasingly being used in data science for visualisations and client-side applications. It is versatile and can integrate well with data science workflows for building interactive data dashboards.

Scala

Scala is a hybrid programming language that combines functional and object-oriented programming. It is widely used in data science for handling big data and AI applications, offering features like high-performance computing and concurrency, making it an attractive option for data scientists.

Java

Java’s cross-platform capabilities and object-oriented design make it a solid choice for data science projects, particularly those involving large-scale enterprise applications. It is commonly used in big data frameworks like Hadoop, making it a valuable skill for data scientists.

Key Takeaways

Data science programming languages are essential tools in data science since they allow a data science professional to manipulate, analyse and present large datasets easily. While technically Python and R are the same data science languages, they have their different place in the data science process. Fluency in those languages is critical to the job, as each language provides specific features for given assignments within the field. Thus, having analysed the described strengths and applications of these 10 top programming languages, data scientists will be able to choose the proper tools that will help them enhance their job performance and promote their profession as more and more organisations turn to data-driven decision-making.

FAQs

What is the best programming language for beginners in data science?

Python is considered one of the easiest programming languages because of its simple and readable syntax.

How long does it take to become proficient in data science?

To become an entry-level data scientist, it takes about 7 to 12 months of rigorous learning for anyone with no coding or mathematics background.

Can I learn data science on my own for free?

Of course, data science can be studied by yourself for free with the help of searching for the necessary materials on the internet. Lectures such as courses on Coursera, edX, and Udemy are available for free to enable learners to understand data science about topics including Python, R, Machine learning among others, and data visualisation. Websites like freeCodeCamp, Tech With Tim, and Sentdex offer free videos about data science ideas and examples of projects.

Can a non-technical person become a data scientist?

Indeed, anyone can become a data scientist because this profession does not require specialisation in computer programming but rather the acquisition of other fundamental tools like programming and statistics, data analysis among others. By focusing and applying oneself and with the help of the internet no job cannot be switched to data science.

Which programming language is best for big projects?

Java, C#, and Go are classified for scalability and this is important when handling huge projects.

Why is Coding Required in Data Science?

Coding plays a crucial role in data science because data manipulation, data analysis, and data visualisation depend on coding for which data scientists can get insights and automate procedures. This assists in the creation of more complex systems such as algorithms for machine learning and organising big data.

Why is Python popular for data science?

Python is preferred for data science because of its simple syntax of writing a program, availability of huge libraries such as pandas, and NumPy along with their easy installation and vast community support to learn from and deal with, as a beginner or a professional.

Which is better, R or Python for data science?

Python is one of the best languages that are most suitable for machine learning and other large-scale applications, especially in the area of data analysis in web-based programmes.

How many programming languages should I learn for data science?

Even when first starting, data science is more efficient if one masters at least two to three programming languages. Python and R are highly recommended due to the frequent usage of these languages and the great number of libraries that provide tools for the DA process. However, having knowledge of SQL language for the databases is important, having some sort of knowledge of languages such as Julia or Java may be useful for a certain project or complex analysis.

What skills are required for data science?

Some of the technical competencies that come under data science are coding skills, including Python and R language, Statistic knowledge, Data efficiency and cleaning proficiency, and Machine learning competency, Data visualisation tools. Also, good communication and interpersonal skills are vital in data interpretation and when presenting the data findings.