A programming language is a formal language comprising a set of instructions that produce various kinds of output. These languages are used in computer programmes to implement algorithms and have multiple applications. There are several programming languages for data science as well. Data scientists should learn and master at least one language as it is an essential tool to realize various data science functions.
Low-level and High-level Programming Languages
There are two types of programming languages – low-level and high-level. Low-level languages are relatively less advanced and the most understandable languages used by computers to perform different operations. These include assembly language and machine language.
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While assembly language deals with direct hardware manipulation and performance issues, machine language is basically binaries read and executed by a computer. An assembler software converts the assembly language into machine code. Low-level programming languages are faster and more memory efficient as compared to their high-level counterparts.
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The second type of programming language provides a stronger abstraction of details and programming concepts. Such high-level languages can create code that is independent of the computer type. Moreover, they are portable, closer to human language, and immensely useful for problem-solving instructions.
Therefore, many data scientists use high-level programming languages. Those aspiring to enter the field may consider specializing in a data science language to start their journey. Let us understand the features and advantages of some of these languages.
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Programming Languages for Data Science
Python is the most widely used data science programming language in the world today. It is an open-source, easy-to-use language that has been around since the year 1991. This general-purpose and dynamic language is inherently object-oriented. It also supports multiple paradigms, from functional to structured and procedural programming.
Therefore, it is one of the most popular languages for data science as well. With less than 1000 iterations, it is faster and a better option for data manipulations. Natural data processing and data learning become a cakewalk with the packages contained in Python. Moreover, Python makes it easier for programmers to read the data in a spreadsheet by creating a CSV output.
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This versatile language is capable of handling multiple tasks at once. It is also useful in embedding everything from electronics to desktop and web applications. Popular processing frameworks like Hadoop run on Java. And it is one of those data science languages that can be quickly and easily scaled up for large applications.
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This modern and elegant programming language was created way more recently, in 2003. Scala was initially designed to address issues with Java. Its applications range from web programming to machine learning. It is also a scalable and effective language for handling big data. In modern-day organizations, Scala supports object-oriented and functional programming as well as concurrent and synchronized processing.
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R is a high-level programming language built by statisticians. The open-source language and software are typically used for statistical computing and graphics. But, it has several applications in data science as well and R has multiple useful libraries for data science. R can come handy for exploring data sets and conducting ad hoc analysis. However, the loops have more than 1000 iterations, and it is more complex to learn than Python.
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Over the years, Structured Query Language or SQL has become a popular programming language for managing data. Although not exclusively used for data science operations, knowledge of SQL tables and queries can help data scientists while dealing with database management systems. This domain-specific language is extremely convenient for storing, manipulating, and retrieving data in relational databases.
Julia is a data science programming language that has been purpose-developed for speedy numerical analysis and high-performance computational science. It can quickly implement mathematical concepts like linear algebra. And it is an excellent language to deal with matrices. Julia can be used for both back-end and front-end programming, and its API can be embedded in programmes.
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In a nutshell
There are more than 250 programming languages in the world today. In this vast field, Python clearly emerges as a winner with over 70,000 libraries and about 8.2 million users worldwide. Python allows for integration with TensorFlow, SQL, among other data science and machine learning libraries. Basic knowledge of Python also helps in picking up computing frameworks such as Apache Spark, famous for its data engineering and big data analysis tasks.
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Before becoming an expert in data science, learning a programming language is a crucial requirement. Data scientists should weigh the pros and cons of the different types of programming languages for data science before making a decision.
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Why is Python considered to be the best fit for Data Science?
Although all of these languages are apt for data science, Python is considered to be the best data science language. The following are some of the reasons why Python is best among the best: Python is much more scalable than other languages like Scala and R. Its scalability lies in the flexibility that it provides to the programmers. It has a vast variety of data science libraries such as NumPy, Pandas, and Scikit-learn which gives it an upper hand over other languages. The large community of Python programmers constantly contributes to the language and helps the newbies to grow with Python.
State the data structures in R?
Data structures are the containers that store the data to use it efficiently. Primarily, R language has 4 data structures: Vector is a dynamically allocated data structure that acts as a container and stores the values with similar data types. Data values stored in a vector are known as components. A list can be considered as an R object that can store data values of multiple data types such as integers, strings, characters, or another list. The Matrix is a grid-like data structure that binds vectors of the same length. It is a 2-D data structure and all the elements within it must be of the same data type. A data frame is similar to a matrix except it is more generic. It can hold values with different data types such as integers, strings, and characters. It shows the combination of the characteristics of a list and a matrix.
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