Programming Languages Required for Data Science

05-Jul-2022

Learning multiple programming languages for data science has become essential for those planning to build a career in data science. This necessity is prompted by the fact that one language cannot address all of the difficulties that arise. Your skill set will be lacking if you don't grasp the specific ones that are regularly employed in data science.

Along with the development of data science, demand for programming languages for data science field, such as Python, began to soar in the 2010s. In fact, a survey by Indeed indicated that between 2014 to 2019, Python and data science abilities emerged as the essential components for securing a strong foundation in an IT job in 2022 and the coming years.

Many of these requests can be directly linked to a group of thriving technologies that are currently becoming more and more popular. The need for some languages is being driven by the impetus from cloud machine learning (ML), artificial intelligence (AI), artificial reality (AR), virtual reality (VR),  and deep learning. Additionally, particular languages support various data science job positions, such as business analyst, data engineer, data architect, or engineers in machine learning (ML).

You will eventually choose to specialize in a particular programming language for data science based on your data science environment, platform framework, interests, company, and career path. To keep up with the most recent advancements and trends in this quickly changing business, data scientists must be eager to learn more.

Pointers for learning Data science programming languages

Programming languages for data science is thus an integral part of the career journey. The more compelling factor is now to understand what elements should be taken into account while selecting the best programming languages for data science career. 

To make your career plan more smooth, you must prioritize these few points while choosing data science programming languages:

• Types of data science tasks you will be required to complete

• The company's data science application  and usage

• Goals of the business and organizations

• Interests in your career

• Your familiarity with coding languages

• Level of difficulty you can manage

• Your Educational Goals

What are the most popular and leading programming languages for data science?

In light of this, we put together a concise list of significant data science programming languages with a brief description of the language. This brief overview should help anyone trying to get a grasp of the data science programming languages landscape.

PYTHON

Python has significantly soared in recent years. Despite being around for 30 years, the language really started to gain wider acceptance in the years following the  introduction of the heavily Python-reliant Dropbox in 2007, which offered considerable real-world proof of concept for its advantages. But Python's appeal is nowhere more apparent than in the data science community, where it's frequently regarded as the standard.

You can succeed in the field if you are proficient in Python and have a high aptitude for mathematical thinking and experimental analysis.

Python's versatility is one of the things that sets it apart from other programming languages. You can create solutions for a variety of use cases if Python is part of your toolkit. Python is currently most frequently used for

• Use modules like NumPy and SciPy for data mining

• Using the Django and Flask frameworks, develop web services.

• Sort, classify, and categorize data.

• Create machine learning algorithms like decision trees and random forests.

R 

R has quickly surpassed a number of other programming languages to rank among the most popular languages in data research. R makes it possible to create a wide variety of statistical models. 

Nearly 8,000 networks have submitted packages to the public R package archive. 

It is used by statisticians to carry out regression-related tasks. Additionally, R provides data visualization with support for many chart types. Smart applications are made using Gmodels, RODBC, TM, and Class in machine learning. R is regarded as appropriate for reports and research articles.

JAVA

Java has remained a preferred choice for desktop, web, and mobile developers for the past three decades. It is supported by a highly advanced environment called JVM (Java Virtual Machine).

Because of Java's high level of scalability compared to other contemporary languages, businesses employ it extensively. Once a project is started in Java, scaling can be done without sacrificing performance. Consequently, building massive machine learning systems is thought to be a common decision.

A few of the Java Libraries ideal for Machine Learning are DL4J, Java ML, ADAMS, Neuroph, and Stanford CoreNLP.

JavaScript

By the 2000s, front-end development—the process of creating dynamic web pages—was the main use for JavaScript, an object-oriented language. But with the introduction of ReactJS, AngularJS, NodeJS, VueJS, and numerous more frameworks in the 2010s, it underwent a substantial evolution. As a result, building a website's front end and back end, frequently using the MEAN and MERN stacks, has gained recognition.

Aspiring data scientists can access models and algorithms via the web browser, which makes JavaScript simple to utilize. On a web-based dashboard, users can create interactive data visualizations using datasets.

SAS (Statistical Analysis System)

In fields including data management, business intelligence, multivariate analytics, and predictive analytics, statistical modeling is frequently done using the SAS software package. SAS has established itself as the leading brand in the analytics sector since its initial release in 1976. SAS allows you to manage and modify data in a variety of formats, split and merge datasets, and carry out statistical techniques for data analysis.

C/C++

Although they are scarcely essential skills for broad data science, the venerable general-purpose language C and its object-oriented cousin, C++, occasionally appear in job listings for machine learning engineering posts. Even though most practitioners use both with Python, the main ML libraries PyTorch and Tensorflow were both primarily created in the low-level C++ language. 

SCALA

One of the most widely used functional languages is Scala. JVM powers it. If you frequently need to work with large amounts of data sets, this is the best choice. It is simple to utilize with Java in data science because of its JVM roots. Keep in mind that Apache Spark, a well-known cluster computing framework, was written in Scala. Scala is a smart choice if Spark will be the center of your data science projects.

MATLAB

This programming language shares a close connection with academics and laboratories for scientific study even after 40 years of its introduction. It is used in several commercial applications, particularly in data science areas that deal with robotics, automotive, and aerospace industries. MATLAB functions more like a language and workspace that offers space for creating and testing fresh algorithms. It offers essential elements like toolboxes or libraries of application-specific functions. 

Learning the right programming language is essential to developing a successful career as a data scientist. Try and indulge enough time in analyzing which programming language you're enthusiastic about and want to specialize in, which will help in achieving a fulfilling career growth in the data science field.

For those who are already working, learning data science programming languages that best suit your company in terms of benefits and growth is ideal. Continue to develop your coding abilities and make it a point to learn what sectors and businesses are searching for in data scientists. You can just search prominent employment boards' job postings to do this.

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