Scala is an excellent option for big data, particularly when complemented with Apache Spark, due to its handling of strong types and functional programming and scalability.
Go (Golang) is optimized for concurrent execution and is most suitable for real-time data processing and streaming with performance and simplicity.
SAS remains a workhorse of regulated markets by its enterprise statistics and data capacity, and has extensive support and documentation.
Though Python still reigns supreme in many aspects of data science, the ever-changing environment means we must also consider other programming languages for the purpose of data science analytics. These alternatives provide different strengths but meet various needs for data analysis, machine learning, and big data processing.
Considering other options may help improve flexibility and efficiency in solving a range of data science challenges.
Julia is optimized for high-performance numerical and scientific computing. Julia is a dynamic language and benefits from the ease of use one expects with a dynamic language while performing at the level of compiled languages like C. Julia's syntax is natural for people who understand math, allowing easy use for data scientists and researcher collaborators.
Speed of execution is provided by Julia's Just-in-Time (JIT) compilation and allows for parallel and distributed computing capabilities as needed. Julia's expanding ecosystem features packages like DataFrames.jl for domain-specific data processing and Flux.jl for machine learning.
Scala is a statically typed language that combines object-oriented and functional programming paradigms. It's best suited for big data processing, particularly when combined with Apache Spark. Scala's syntax is compact, and it has a strong type system, which makes it easy to develop solid data pipelines.
The libraries, Breeze for numerical operations and Smile for machine learning, add to the strength of Scala in data science.
Go, or Golang, is described as a succinct and very fast concurrent language built by Google. One of Go's nice features is that the language is so straightforward to build a scalable system that can perform large-scale data handling. With Go's built-in goroutines and channels for ease of concurrency, it truly is useful for real-time data analysis and streaming systems. Libraries, Gonum and Gorgonia, will allow you to achieve numerical computation and machine learning in Go.
Rust is a systems programming language that focuses on security and efficiency. Its zero-cost abstractions and memory safety guarantees provide it with the best approach for performance-critical data science applications.
Its expanding ecosystem also supports libraries such as ndarray for n-dimensional arrays and RustML for machine learning purposes. Its interoperability with other languages also supports integration into current data science processes.
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SAS (Statistical Analysis System) is a collection of computer software solutions for advanced analytics, business intelligence, and data management that works in any industry (e.g., health care, finance, government). SAS offers a wide variety of data manipulation, statistical analysis, and predictive models. SAS is a business-class viable alternative for data science initiatives due to its thorough support and documentation.
Mastering other languages like Julia, Scala, Go, Rust, and SAS as a data scientist would complement Python and also empower the professional toolset. It would make a data scientist more versatile. Each of these languages has advantages and applicability in specialized domains, such as numerical computing, scalable big data, concurrent programming, memory safety, and enterprise analytics.
Each language would increase the ability to develop scalable, optimized, and specialized data solutions.
The data science domain is rapidly evolving, and new challenges arise every day. Being able to push programming skills beyond Python will allow data scientists to think about solving more complicated problems in innovative and creative ways. As businesses look for more and more customized solutions for different data workloads, the ability to master many programming languages can only be beneficial. The use of less common languages will allow one to stay competitive and grow in a profession like data science.
Understanding and using data science programming languages other than Python will become more critical as the field continues to become more granular and information-heavy. Julia, Scala, Go, Rust, and SAS all provide specific capabilities to solve unique data-centric problems, whether that means high-performance computing or enterprise analytics.
Being able to program across a variety of different programming languages in this context brings more flexibility and creativity, and less constraint, to data professionals and leads them well into the future of the industry.