Top Programming Languages for Data Science in 2023

Data Science in 2023

According to research, the profession of data scientist takes second place among the best jobs in the USA in 2021. Moreover, that includes open positions in different industries that are growing substantially. Based on the Forbes report, 59% of all Data Science and Analytics job demand belongs to Finance and Insurance, Professional Services, and IT.

Discover the importance of data science in finance!

What Does a Data Scientist Do?

Data scientists utilize their programming, analytical and statistical skills to study, analyze, and interpret large data sets. Then they use this information to develop data-driven solutions to a variety of business challenges. Data scientists have a wide range of technical competencies including machine learning and statistics, databases, reporting technologies, and of course the knowledge of data science programming languages

How to outsource machine learning? 

Common responsibilities for Data Scientist jobs

• Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques, and business strategies.
• Develop processes and tools to monitor and analyze model performance and data accuracy.
• Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
• Develop custom data models and algorithms to apply to data sets.
• Develop company A/B testing framework and test model quality.

Qualifications for Data Scientist jobs

• Experience in using data science coding languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
• Experience in working with and creating data architectures.
• Knowledge of a variety of machine learning techniques.
• Knowledge of advanced statistical techniques and concepts.

Thus data scientists require both the right tools and perfect skill set to enable you to produce better results with all the information. Now, let’s take a closer look at the top data science programming languages in 2023.

What’s the best technology stack for mobile app development?

Python

Python is one of the most popular and “most loved” programming languages in the world. It’s famous for efficiency, speed, reliability, and quality, which are on the same level of skill. An application can be created under any circumstances and end up with an amazing performance. It has the potential to develop a corporate standard for high-security applications using 128-bit encryption technology.

In addition, multi-channel security measures will be implemented in the application. The language is chosen to create all kinds of applications. e Both from scratch and using various libraries and tools. The variety of tools makes it a perfect solution, together with:

• extensive mathematical capabilities
• interactive mode
• cross-platform development
• high development speed
• variety of good libraries

Top Python Libraries for data scientists according to Analytics Insights are:

• TensorFlow
• Scikit-learn
• PyTorch
• Pandas
• NumPy
• SciPy
• Matplotlib

With such capabilities, Python allows you to write a program for machine learning tasks.

Choose the python platform for machine learning!

Java

Java is famous among developers as well as game lovers, who have seen its logo while games have been loading. In Data Science this language is used for tasks involving data mining, deep learning, data analysis, and machine learning. One of the advantages of this language is that the same code can be run on the operating systems Windows, Linux, FreeBSD, Solaris, iOS, etc. In addition, Java can boast of a high level of security, reliability, standard libraries, and a variety of application types.

JavaScript

JavaScript is also among the top programming languages for data science. It’s a client-side scripting language, mostly used for the creation of websites and games for the web. This language can be used straight away for producing websites, games, and mobile applications. Benefits of JavaScript:

• easy to debug and test, its code is interpreted line by line, the errors are indicated along with line number
• event-based programming, a different code segment is executed when a certain event occurs
• procedural capabilities, it provides condition checking, loops, and branching facilities that can be executed on a web page

There are enough native libraries to help work with big data and machine learning. Java Script is a good choice when a project is created at the intersection of the web and Big Data predictive analytics solutions.

.

R

R is made for numerical computation and sometimes called The “language of statistics”. This language is an open-source programming language, which is commonly used for statistical computations of bulk data (data mining and statistics) and building data models for accurate data analysis. R recently gained great popularity with the growing number of programmers using the possibilities of generating and analyzing data, in the framework of machine learning and other new data-dependent technologies. In fact, it has impressive features and functions:

• R is completely open and free
• it can boast of regular software updates
• it is available for Windows, Mac OS X and Linux and is capable of importing data from a variety of programs, including Microsoft Excel, MySQL, and Oracle
• R is able to handle an incredible amount of data

An analysis created by a Data Scientist, David Robinson, is a reflection of the popularity of R in an industry: R is outstandingly being used in Education and Healthcare.

SQL

The Structured Query Language is one of the most used programming languages for data science and tools to work with Big Data. It combines both analytical and transactional capabilities. It’s primarily used as a way to communicate with relational databases and also for data management in online and offline applications and managing large databases, reducing the turnaround time for online requests by its fast processing time.

Scala

Scala (scalable language) is a functional object hybrid language with several strengths, what is the reason why engineers choose it for different projects. Codes written in Scala can be used within a Java-based Big Data ecosystem because this language runs on the JVM. Sooner or later, Scala may replace Java, so it is worth keeping up with the front lines of development in the future. It is:

• functional in programming support
• compatible with Java libraries
• good for large development teams
• able to write applications for Android

Another useful feature of Scala is the ability to run parallel processes when working with large data. This language combines an object-oriented and functional programming language and is suitable for working with Big Data tools Apache Spark.

To understand more: read about the impact of artificial intelligence on healthcare 

To Conclude

So which language to choose? It depends on the specific product you’re developing. Java is the best fit for Big Data solutions using available frameworks. Scala can be a choice if you are developing streaming applications for your Big Data, R is the choice for Data analysis which involves statistical computing. Machine Learning and predictive models are working with Python.

Nowadays Data Science specialists have a large selection of technologies they are implementing in various tasks. Both the efficiency and high-performance results depend on chosen data science languages. Each of the mentioned above programming languages has a high chance to develop and gain even more popularity in the future. Inoxoft offers services of Big Data Analytics, Machine Learning, Predictive Modeling. If you’re ready to take advantage of data science for your business. Contact us to get strategic insights.