Best Programming Language for Data Science

Data science and programming go hand in hand. Every data scientist uses multiple algorithms to carry out their daily tasks and derives actionable insights from massive datasets. Data enthusiasts should get their hands set with programming language to perform well during advanced data science learning like Simplilearn online Bootcamp.

What are the suitable languages to become a qualified data scientist? The market is overwhelmed with numerous programming languages. So, it’s better to shortlist a few programming languages and become an expert in pursuing data science.

This article presents the top programming languages you can consider in your career. It’s better to learn and experience them before enrolling in a data science Bootcamp.

Are you ready? Let’s get started if it’s a yes!

Python – Programming Language

Data scientists utilize Python as one of the most popular programming languages for data research. Due to its numerous applications, including machine learning, deep learning, and artificial intelligence. Keras, scikit-Learn, matplotlib, and TensorFlow are among the Python data science from scratch libraries. The best application for this data science programming language is automation. For data science students, this is the ideal programming language.

JavaScript

Another most significant data science programming language to learn is JavaScript. It uses in web development since it creates interactive websites. It can be the most effective tool for developing and designing visualizations. Even if it is a fantastic language to learn, it is more beneficial for novices in data science than those who want to study primary data science programming languages.

Java

Java is also termed as “write once, run anywhere.” It’s yet another popular data science programming language employed by the world’s most successful companies to ensure long-term success. This data science programming language allows data scientists to build sophisticated applications from the ground up and deliver findings much more quickly than previous languages. Data analysis, data mining, and machine learning all tasks perform using Java. The trash collection feature of Java sets it apart from other programming languages, making it more efficient.

R – Programming Language

“R” is a powerful scripting language that can handle substantial complex data sets. It is attracting a lot of interest from data scientists these days, and it’s quickly becoming one of the most prominent data science programming languages. R is also simple to pick up for statistical computing and graphics. These reasons make R the best choice among data scientists working in data science, big data, and machine learning.

C/C++

Since it is one of the most primitive programming languages and C/C++ is their codebase, C is a fantastic data science programming language for studying data science programs. Due to their ability to leverage the codebase, most data scientists do not know C/C++. This programming language has a considerably greater range of applications to which you can apply it. The perks of using C/C++ is that it allows developers to delve deeper and fine-tune application areas that would otherwise be impossible.

SQL

SQL is an essential data science programming language for aspiring data scientists to learn. The ability to handle structured data necessitates this programming. SQL provides data and statistics access, making it a valuable resource for data science. Data science requires using a database, which involves the application of a database language such as SQL. To query databases, those working with big data need to have a strong knowledge of SQL.

MATLAB

When first launched in 1984, this programming language found early adopters at MIT and Stanford. It closely connects with academics and scientific research labs nearly 40 years later. It does have some commercial uses, such as data science in automotive, robotics, and aerospace. MATLAB combines a programming language and a work environment that allows you to create and test new algorithms. Another critical component is toolboxes, which are libraries of application-specific functions.

One disadvantage is that MATLAB is proprietary software. However, many of the functionalities that made the language famous years ago, such as intuitive plotting, are available in various free, open-source equivalents. While it’s still a niche topic for most working data scientists, its persistence, particularly in some higher-education research circles, means it shouldn’t be overlooked when assessing the landscape.

Scala

It is a high-end data science programming language specifically designed for data scientists. Scala is the best language for dealing with large data sets. It enables Java compatibility, which opens up many possibilities for data scientists. To handle enormous amounts of siloed data, you can use Scala with Spark. A large number of libraries are available for this data science programming language.

Julia

Julia is a general-purpose programming language designed for numerical analysis and scientific computing. Due to this, many high-profile businesses are focused on time-series analysis, space mission planning, and risk analysis. Julia can be used as a low-level programming language.

SAS

SAS is a statistical data analysis software program. The tool’s primary feature is retrieving, reporting, and statistical information processing. SAS is likely to open up many more doors in the coming days.

Swift

After making its public debut in 2014, this Apple-developed compiled language soon earned a reputation for speed. (Swift, like the similarly nascent languages Rust and Clang, is powered by the LLVM compiler infrastructure.) Swift is Python-compatible, and Google, which formerly aided Python’s popularity, is a significant supporter of Swift. (In 2017, the program’s creator joined a critical Google deep-learning research team).

Summing up

These programming languages are a must-know for every data scientist. So, it’s better to build proficiency in as many languages as possible before starting formal preparations in the domain. Get start with them now!