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Data and analytics have become buzzwords in recent years in the global business community. From large multinational conglomerates to small businesses, data science, and big data have made great impacts in driving them into being data-driven organizations. But recently small data has also made big strides in the global industrial sectors. Both big data and small data are extensively used in businesses to reveal patterns, trends, and customer habits to enhance revenues. When it comes to data science, programming languages are required in every direction. Data specialists use top programming languages for various purposes. In this article, we have listed ten programming languages that will efficiently handle small data in 2022.
Python is one of the most popular programming languages used in global businesses. Learning Python not only opens unprecedented doors to handle small and big data but also for web and software development. It is an open-source, object-oriented programming language that is extensively used by programmers all over the world in a number of tasks it allows to solve.
R is one of the most popular programming languages that is used in data science for handling big and small data. R is not just a programming language; it is an entire environment for statistical analysis and calculations. It allows the users to perform operations like data processing, mathematical modelling and work with graphics at the same time.
JavaScript is one of the most popular programming languages to learn in 2022. It is widely used for web development purposes due to its enhanced capabilities of building rich and interactive web pages. It is a general-purpose programming language and is a primary choice for data scientists for its good selection of packages and efficient web integration.
Scala is a programming language that is strongly associated with data engineering. It enables high-performance frameworks for warehouse data, which is perfect for entry-level data science professionals. Scala also supports concurrent and synchronized processing.
MATLAB is one of the best programming languages when it comes to profound mathematical calculations. This technology is powerful for data analysis, image processing, and mathematical modelling. Besides, it is a general-purpose programming language, which makes it highly capable of using it in big data operations as well.
Java is a high-performance programming language and is widely used for writing machine learning algorithms, and can be seamlessly integrated with advanced data science tools. Due to wide applications, Java is the most frequently used programming language that is good for small and big data and IoT applications as well.
Julia is another specialized programming language that is designed for computations and numerical analysis. It provides versatility and can support distributed computing. Julia also offers fast performance which is ideal for numerical analysis, data visualization and deep learning.
SQL is vital for manipulating structured data. It is ideal for handling large datasets, which makes it even more efficient for handling small data. It is a querying language, which allows the users to adjust, locate and check datasets efficiently. It is a domain-specific language and is convenient to manage relational datasets.
SAS is widely known as a data analysis programming language, which provides flexible possibilities of working with data analytics and statistics. Even though it is one of the oldest languages, developers can use its unique functions for predictive modelling, data and business analytics.
Learning C/C++ will offer excellent capabilities for building statistical and data tools. It can seamlessly compile data and build highly functional tools. Programmers with less experience can use C and C++ for scalable projects.
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