Top 10 Programming Languages for Data Science Projects

If you are a Data Science enthusiast and want to learn top programming languages for data science projects then in this article I’ll let you know the programming languages assisting data science projects. Data science is an art of extracting insights from Data. At the core of every data science project lies a programming language. In my earlier article, I had explored for you the machine learning tools, data science tools, & top 10 programming languages for machine learning projects which assist in the respective technologies.

Through the use of machine learning and data science, developers can accelerate the development process of apps and they can integrate these machine learning algorithms into apps too. For this article, I will look at the following programming languages that are being used by data science professionals. I shall evaluate their strong sides, weak sides, and the areas where they are being applied.

Maybe the epoch of big data is a time when data science probably makes the business decisions, research in science, and development of technology a driving force. The important element in data science is the language selection. It is a technology that can be used to program. In this article, I attempted to look for the best programming Languages that give data scientists the ability of knocking down the barriers and enable them extract meaningful information from the big complicated dataset.

1. Python Programming Languages for Data Science

Python is an undoubted winner if there is a bout between data science and its opponent. Its easy-to-understand, neatness and a broad reference library and framework are the reasons why it’s used by the majority of data scientists around the world. Among the libraries such as NumPy, pandas, Matplotlib, and Seaborn, there are data manipulation, analysis, and visualization sub-tasks carried out. In addition to that, Python has a bunch of machine learning libraries that simple and convenient to use. These libraries include Scikit-learn and TensorFlow respectively.

2. R Programming Languages for Data Science

R, a language of statistics was created by statisticians for statisticians’ community. The immense spectrum of packages including ggplot2, dplyr, and tidyr, are the prime easy-go tools for tackling complex data tasks. This is possible because the packages in the collection are quite powerful and simple in nature allowing for the quick and accurate task processing. R’s rich statistical libraries and its ability to create nice visualization make it irreplaceable tool for researchers and analysts.

3. SQL

Oracle is the best known language for DBMS (relational data), while SQL (Structured Query Language) is the leading language for relational databases. SQL proficiency constitutes a major part of a data scientist’s job because this skill helps them get, modify, and work on data stored in relational database management systems, or RDBMS. SQL is one of the core skills needed for data engineering and analytics.

4. Java

In dealing with big data, Java’s versatility also prevails in the area of data science, for instance, in the processing of big data. Apache Hadoop which is Java based framework is typically used for reaching distributed data storage and processing. Java is the language of choice for large-scale data and analytics capabilities due to its unquestionable typing ease and unparalleled speed.

5. SAS

SAS (Statistical Analysis System) is software which provides solutions for advanced analytics, business intelligence & data management. It includes the variety of weapons in the form of data manipulation and processing, statistical analysis, and the predictive modeling. SAS is widely utilized in those industries where data precision and accuracy, data security, and data safekeeping as well as compliance are key issues.

6. Julia Programming Languages for Data Science

Julia, which is currently developing as a language for numerical and scientific computing. It shares these two worlds, Python’s ease of use and C++ performance. Julia’s JIT (just-in-time) compilation and parallel processing capacities render it a suitable candidate for data heavy tasks.

7. Scala Programming Languages for Data Science

Through its support for both objects and functions, Scala is ideally positioned to data-oriented operations, like analysis and engineering. Apache Spark, a distributed data processing platform designed to be used alongside Scala offers flexibility and capabilities for data scientist to do more with big data analysis. For instance, in Scala, libraries such as Breeze and Smile provide machine learning ability while at the same time they take advantage of Scala’s conciseness and functional programming features. Scala’s interoperability with Java results in easy integration by developers with Java based frameworks of Apache Spark.

8. Haskell Programming Languages for Data Science

The strong type system and functional programming features of Haskell are what make it an interesting possibility for data scientists who are seeking to explore the analysis process in a completely functional way. While, it is not as usual as Python and R, but Haskell’s expressiveness comes in handy when there are data science projects that require particular solutions.

9. C++

Among the advantages of C++, there is its speed and efficiency, which make this language to become a tool very useful for data science tasks. Libraries such as Armadillo and mlpack enable the machine learning and data analytical functions within the software. C++ is among the languages which serve best for systems development and applications where real time processing and resource optimization is important.

10. GNU Octave Programming Languages for Data Science

GNU Octave is used for cases when only a small number of data are taken into consideration but accurate arithmetic calculations remain the primary task. It is a high level programming language, which is next only to machine language, and consists of capabilities like scientific computing and numeric calculations.

Conclusion

The right decision in a programming language is the key factor for a successful data science project. While Python is complex in functionality, R is more oriented towards statistics, SQL for database querying, and the other languages like Java, SAS, Julia, Scala, Haskell, and C++ all possess characteristics that fill different gaps and shows unique strengths. Whether to use Java or Python at the end of the day is determined by the distinctive project needs, the libraries in the available package, and the data analyst’s competency in the language. The use of language and technology in data science has become a phenomenon beyond dispute. And this is because the latter has undoubtedly realized the potential hidden in programming for turning the raw data into actionable insights.

Image credit- Canva