These programming languages for data scientists are extremely high-level and critical to learn
Even though the significance of no-code and low-code platforms is on the rise, programming and writing codes manually is still quite important. This is especially important for data science professionals. However, for data scientists, there is a learning curve. Along with coding, they will also need to unlearn and relearn mathematics and business, and several other thought leadership elements that they have never considered before. Besides these, data scientists have to choose the latest programming languages that are widely accepted by cutting-edge technologies and provide massive amounts of facilities. Programming languages for data science are high-level languages and those who aspire to enter this burgeoning field must ace these languages to gain an edge over their competitors. In this article, we have enlisted the top 10 programming languages for data scientists that they should learn in 2023.
Python has the highest amount of popularity among data scientists, mainly because of its wide range of applications in the domain. Various tasks based on deep learning, machine learning, artificial intelligence, and other popular forms of technology can be easily handled through Python. The language’s libraries like Keras, Scikit-Learn, and TensorFlow offer massive opportunities for advancement.
Java is an open-source, object-oriented, and one of the most popular programming languages in the world that are used for data science purposes. Due to its first-class performance and efficiency, Java has emerged as one of the popular programming languages in the data science industry.
SQL is great for data management and handling. Even though the language is not exclusively used for data science operations, the knowledge of SQL tables and queries can help data scientists deal with database management systems. The language is specifically extremely domain-oriented and is extremely convenient for storing, manipulating, and retrieving data in relational databases.
Scala is a modern programming language that gained major prominence in the data science industry in recent years. Its application range from web programming to machine learning. The language is highly scalable and effective for handling big datasets. Modern organizations that use Scala are facilitated with object-oriented and functional programming as well as concurrent and synchronized processing.
Julia is a data science programming language that is purpose-developed for the fast numerical analysis and high performance of computational science. It can quickly implement mathematical concepts like linear algebra and is excellent to deal with matrices.
Go is a new programming language that has gained major prominence in the data science industry. It is a language that addresses critical issues in Python. Go is excellent in data reading and data manipulation and is widely used by experienced data scientists all over the world.
Kotlin can be used to build data pipelines to produce machine-learning models. The language is concise, readable, and easy to learn. Being a JVM language, Kotlin gives demonstrates great performance and the ability to leverage an entire ecosystem of tried and true Java libraries.
R is not highly popular among data scientists as much as Python, but it definitely is a top option for aspiring data scientists trying to learn critical programming languages. R is an open-source, domain-specific language, explicitly designed for data manipulation, processing, and visualization.
MATLAB is a language mainly designed for numerical computing. The language massively adopted in academia and scientific research since its launch and provides powerful tools to carry out advanced mathematical and statistical operations, making it a great choice for data science.
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