Today, everyone is talking about data and data science. The exponential growth in data management requires specific programming languages and technical expertise in data analysis. For a person to enter the field of data science, he or she must know some of the top programming languages for data science. In this thriving data-driven arena, the number of aspiring data scientists and professionals is increasing day by day.
Now, the question arises: what is data science?
Data science uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. Its multidisciplinary approach consists of statistics, machine learning, data analysis, and programming to analyze and monitor data sets and make decisions and predictions.
If you are a seasoned programmer or curious about the data science domain (as I am also in this field), you have to understand the flexibility of Python, statistical powers with R, SQL efficiency, and trends in Julia, which will all help you learn machine learning and visualization of complex data.
List of famous programming languages for Data Science
For a carefully curated and comprehensive understanding of data analysis, statistical modeling, machine learning, and data visualization, mastering the languages listed below is essential. If you gain commands in the languages below, you can become a professional and entry-level data scientist within six months of a practical approach.
Due to its capacity for statistical analysis, data modeling, and easy-to-earn approach, Python is at the top of the data science field. Secondly, its huge library supports multiple approaches to data science and analytics. Many functions, modules, tools, and methods are available in Python libraries. All of the libraries focus on specific tasks related to managing images, textual data mining, neural networks, data visualization, etc. Data analysts and scientists use Pandas for data handling, NumPy for numerical computing, SciPy for scientific computing, and Matplotlib for data visualization. Python’s vast community makes it very famous for data scientists worldwide.
The second famous programming language developed by statisticians is R. It is very popular in the recent data science community and consists of cutting-edge, useful libraries like ggplot2. This language is used in exploratory data analysis and creating insightful visualization. Many of the libraries have unique functions that manage image and textual data, data manipulation, data visualization, web crawling, machine learning, and so on, such as dplyr, a famous manipulation library. It handles the real projects of statistical data analysis.
- SQL
People specifically use Structured Query Language (SQL) to manipulate data in relational database systems (RDBMS). Data scientists use this often for data manipulation, such as extracting and managing datasets efficiently. Data professionals use it to retrieve data, handle large datasets, and run complex queries to obtain structured results. Many flavours of SQL databases that data scientists use nowadays are SQLite, MySQL, Postgres, Oracledb, and Microsoft SQL Server. BigQuery, in particular, is a data warehouse that can manage data analysis in petabytes of size and can perform super-fast SQL queries.
Julia is an emerging programming language for data scientists, offering syntax very familiar to Python and R users. It even works faster than Python, R, MATLAB, and JavaScript. It uses just-in-time (JIT) compilation that enhances speed, rapid numerical analysis, and scientific computing. Overall, Julia has 1900 packages and is able to integrate with other programming languages such as R, Python, MATLAB, C, C++, Java, Fortran, etc., either directly or using packages.
5. Java
As an older language, Java plays a key role in the data science domain. Tools like Apache, Hadoop, and Apache Spark extend its functionality, making it a robust choice for big data processing and analytics. Hadoop runs on a Java virtual machine (JVM), so it is important to understand Java before using Hadoop. Many other libraries, like Weka, MLlib, Java-ML, and Deeplearning4j, are famous data science libraries. Java’s platform independence allows developers to use it for large-scale applications and solve complex tasks in distributed environments.
A popular programming language for numerical computing and data analysis is MATLAB, widely used by industry and academia for research purposes and data science. As data science mostly deals with math work, MATLAB allows mathematical modeling, image processing, and data analytics. It consists of many built-in mathematical functions, mainly used in data science, linear algebra, statistics, optimization, Fourier analysis, filtering, numerical integration, differential equations, etc. Many built-in graphics are also part of MATLAB and are used for visualization purposes. Overall, it provides researchers, professionals, and scientists with valuable tools in various domains.
Golang, famous for its Go name, is a popular language for data science in terms of simplicity, concurrency support, and overall performance. For developing scalable and concurrent data processing applications, Go is the best. Due to parallelism for multitasking applications, datasets, and distributed systems, Go makes all these possible. Due to its recent popularity, Go has emerged as a language with promising potential for data-driven applications and systems.
This famous but not extensively used language focuses on light processing due to lightweight arrays. It is similar in syntax to Python and a useful language for data science. Interestingly, Perl 6 is thought to be the ‘big data lite’, used by big companies like Boeing, Siemens, etc. Some other quantitative fields, like finance, bioinformatics, and statistical analysis, use Perl in their applications.
Originally an extension of Java and built on the Java Virtual Machine (JVM), Scala enables programmers to integrate with Java and can be used in data science. It can be used with Apache Spark to control a large amount of data. Many applications that use Hadoop on top also use Scala or Java. The disadvantage of Scala is that it is difficult to learn, and the online community is very limited.
A complete integrated system developed by the SAS Institute enables professionals to perform information retrieval and data management in the fields of healthcare, finance, and so on. Data cleaning and exploration of advanced statistical modeling are possible with SAS. Industry-wide presence makes it a trusted choice for organizations with stringent data analysis requirements.
Conclusion:
The ever-evolving nature of growth in data science deals with the choice of programmer for any languages discussed above. Ranging from Python to R, the scalability of Java and so on brings unique features with them; thus, language is not just a requirement, but complete strategy and deep knowledge are the basis for becoming a professional in data science. Whichever language is your choice for data science, many of the above are used in parallel to become an experienced data scientist in this domain.