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Top data science programming language

If you are interested in the field of data science, you need to be familiar with several programming languages ​​because one language cannot solve problems in all fields. If you don’t master certain things that are often used in data science, your skill set will be incomplete. The demand for these languages, such as Python, began to skyrocket in the 2010s with the rise of data science.In fact, according to a study by surelyFrom 2014 to 2019, data science and Python skills became a key factor in securing a solid foundation for the 2020 IT career.

Many of these demands are directly related to the prosperous set of technologies that are currently being adopted in the mainstream. Momentum from the cloud, augmented reality (AR), virtual reality (VR), artificial intelligence (AI), machine learning (ML), and deep learning is driving demand for specific languages. In addition, certain languages ​​complement a variety of data science tasks, including business analysts, data engineers, data architects, and machine learning (ML) engineers.

Ultimately, it’s the data science environment, platform frameworks, interests, organizations, and career paths that will become specialized in a particular programming language. However, data scientists need to be willing to learn more to adapt to the latest developments and trends in this rapidly evolving industry.

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Things to consider when choosing the best programming language for your data science career path

Before choosing a programming language, there are a few things to consider.

● What data science tasks do you need to perform?

● How does your organization use data science?

● What is the purpose of your company?

● What are your career interests?

● Which programming language do you already know?

● How difficult are you ready to tackle?

● What is your educational ambition?

In Demand Data Science Programming Language

The following are the major data science programming languages.

Python

For at least the next five years, Python proficiency will exceed the skill sets required in data science.By knowing PythonCombined with a strong aptitude for quantitative reasoning and experimental analysis, you can earn money in the industry.

One of the factors that sets Python apart from other products is its flexibility. If your toolset includes Python, you can build solutions for different use cases. Currently, Python is primarily used for the following purposes:

✔ Perform data mining using modules such as NumPy, SciPy

✔ Create a web service using Django and the Flask framework

✔ Data classification, sorting, classification

✔ Develop ML algorithms such as decision trees and random forests

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R programming

In a short period of time, R has surpassed several programming languages ​​and has become one of the best languages. A prominent language in data science..

R allows the design of many statistical models. The Public R Package Archive consists of packages provided by approximately 8,000 networks. Statisticians use it to perform regression tasks. R also provides data visualization that supports different types of graphs.

To Machine learning, Gmodels, RODBC, TM, and Class are used to create smart applications. R is considered suitable for research treatises and reports.

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Java

For the past 30 years Java It continues to be popular with desktop, web, and mobile developers. It runs behind a very sophisticated environment called the JVM (Java Virtual Machine).

Java Widely used by businesses in favor of other modern languages, primarily because of the degree of scalability it provides. Once the project is launched in Java, it can be extended without sacrificing performance. Therefore, it is considered a common option for creating large-scale machine learning systems. Popular Java libraries for machine learning include:

✔ DL4J – Engage in deep learning

✔ ADAMS – Perform data mining

✔ JavaML – Implement machine learning algorithms

✔ Neuroph – Create and train neural networks

✔ Stanford Core NLP – Perform NLP (Natural Language Processing) tasks

JavaScript

JavaScript Is an object-oriented language that was used by the 2000s, primarily in front-end development, to design interactive web pages. However, it has evolved significantly throughout the 2010s with the advent of ReactJS, AngularJS, VueJS, NodeJS, and many other frameworks. As a result, creating both front-end and back-end websites, often using the MEAN and MERN stacks, has become a respected option.

JavaScript is easy to use because ambitious data scientists can access models and algorithms in a web browser. Similarly, users can build interactive data visualizations from datasets on web-based dashboards.

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SAS (Statistical Analysis System)

SAS Is a suite of software commonly used to perform statistical modeling in areas such as data management, business intelligence, multivariate analysis, and predictive analytics. First released in 1976, SAS has solidified its position as a leader in the analytical industry. You can use SAS to access multiple formats of data, manage and manipulate data, split and merge datasets, and perform statistical techniques for data analysis.

Scala

Scala is one of the most popular functional languages. It runs on the JVM. This is an ideal option if you need to process large datasets frequently. Originating in the JVM, it’s easy to use in Java for data science. Keep in mind that Scala was used to create Apache Spark, a well-known cluster computing framework. Therefore, if your data science tasks revolve around Spark, Scala is a good choice.

TensorFlow

TensorFlow Is one of the main libraries for numerical calculations. This is an ML-based framework used to work on large datasets. TensorFlow works very well in distributed computing. TensorFlow allows you to split your graph into chunks and run them in parallel on different CPUs and GPUs. Therefore, it is useful for quickly training complex and large neural networks.

C #

Developed by Microsoft C #, It is now one of the most widely used programming languages ​​in the last 20 years. C # is inspired by Java and adds a modern touch for further sophistication. To make data science feasible in C #, Microsoft has published the Hadoop framework on Windows. You can also use the ML.NET Framework to create cross-platform machine learning applications.

Ruby

Ruby Often used to perform text processing. Developers also used it to experiment with prototypes, create servers, and engage in other common activities. In data science with Ruby, you can use:

✔ Jupyter’s iruby kernel

✔ connect to Rserve (R binary server) rserve-client

✔ Jongleur Workflow Manager for data manipulation

✔ Rb-gsl to access GNU Scientific Library

Running programming language

Before choosing a particular data science language, you need to consider your work requirements. For example, R is used in the financial industry to build stock market models and forecast stock prices. In the retail sector, programmers use Python to build a recommendation engine and provide customer-related suggestions.

If you are Data mining For financial companies, R is the right choice, but if you’re building an app that gives your customers access to financial details from multiple devices, Python is a better choice. In any industry, Python works well with a variety of machine learning initiatives to study structured and link it with unstructured data.

the current, IoT application Increased has. If you belong to the IoT industry and want to write code for gateways or edge devices, you should choose C and C ++. The low level of these languages ​​makes them a great technology for programming different types of hardware. However, with a kit like the Raspberry Pi, Python offers advantages in the IoT space.

If you are more interested in developing GUIs or creating games, Microsoft C # is for you. This is because you can use C # to create games in Unity, one of the top game engines.

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Final idea

As Data scientist, You need to learn the right programming language for a smooth and successful career. To do this right, you need to take the time to think about what you are passionate about and what you want to specialize in. If you are currently working, evaluate which language is most valuable. Data science application In your organization. Keep improving your programming skills and focus on knowing what your employer or industry wants from your data scientists. This can be done simply by looking at the job listings on the popular job boards.

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