Top Programming Languages For Data Scientists [202]

In the field of data science, the usage of the correct programming language has a significant impact on data analysis and innovation. Each programming language has its collection of strengths and specialties, which serve particular elements of data analysis and manipulation.

If you’re looking to master these programming languages and advance your career in data science, consider enrolling in Scaler’s Data Science Course to gain hands-on experience and expert guidance.

In this article, we will take a look at various programming languages, and their importance in data science, including their distinct features and the impact they have on finding insights and generating advancement. At last, we will take a closer look at the selection of the right programming language for data science.

Why do you Need Programming in Data Science?

In data science, programming provides the foundation that allows analysts and scientists to valuable discoveries. But why is programming such an important tool in the data science toolkit?

Let us look at why it plays such an important role.

1. Data Handling and Manipulation

Data science relies heavily on data. Python and R programming languages have strong libraries (such as Pandas in Python and dplyr in R ) that make data management and manipulation simple. Whether you’re cleaning up a dirty dataset or combining several sources, programming makes the complex process with data easier.

2. Efficient Analysis with Libraries

Programming languages provide a bunch of libraries (NumPy, SciPy, and others in Python) that enhance your analytical ability. These libraries include pre-built functions and tools, allowing you to do difficult statistical analyses and machine-learning tasks with a few lines of code.

3. Customization for Unique Challenges

Programming allows you to create tailor-made answers to unique situations. From developing customized algorithms to designing one-of-a-kind visualizations, coding allows for innovation and problem-solving that goes beyond traditional off-the-shelf solutions.

4. Reproducibility and Collaboration

Have you ever worked on a fantastic analysis and then struggled to replicate the desired result? Programming enhances repeatability. By scripting your analysis, you can ensure consistency while also streamlining communication with peers. Share your code so that others can duplicate, validate, and expand on your discoveries.

5. Scalability for Big Data

With a growing volume of data, scalability is important. Python, when used with frameworks like Apache Spark, allows for the easy management of huge datasets. This scalability ensures that your analyses stay successful as data volumes increase.

6. Integrating with Data Sources

Data is generally gathered from several sources. Programming enables you to work smoothly with databases, APIs, and many file types. Whether your data resides in a CSV file or a cloud-based database, programming bridges the gap, bringing disparate data sources together for comprehensive analysis.

In data science, programming takes center stage, allowing analysts to deal with the complexities with ease. From efficient data processing to creating tailored solutions, programming’s value resides in its capacity to convert raw data into meaningful insights.

Top Programming Languages for Data Science

Opting for the right programming language is a must as it determines your performance. As we go into the basics, the ones and zeros, a few computer languages stand out as the best for data scientists. Let’s take a deeper look at the leading programming languages used in the world of data science.

1. Python

Python is ranked as the 1st among several programming languages by several indices including the TIOBE and PYPL index. Python is a high-level, interpreted programming language known for its simplicity, readability, and adaptability. Guido van Rossum developed it.

Let us now see the reasons for Python’s popularity.

• Python’s syntax promotes readability which makes it an ideal choice for both new and experienced programmers.
• Python provides a variety of libraries and frameworks oriented toward data science, processing, analysis, and visualization.
• Libraries like NumPy, Pandas, and Matplotlib offer powerful features for working with huge datasets, carrying out mathematical calculations, and creating insightful visualizations.

Python’s popularity in data science is also due to its strong support for machine learning and deep learning algorithms. Data scientists can easily design and train complex models for classification, regression, clustering, and other tasks utilizing frameworks such as NumPy, Pandas, Matplotlib, Keras, TensorFlow, PyTorch, and scikit-learn.

Additionally, Python’s versatility and ease of integration with other languages and technologies make it an excellent choice for data science apps. It connects smoothly with databases, online APIs, and other technologies typically used in data science workflows.

Python’s robust ecosystem and extensive documentation add to its popularity among data scientists. There are several tutorials, forums, and online resources available for learning and practising Python, making it accessible to people of all backgrounds and skill levels.

You can always refer to the Python course on Scaler for a well-structured course.

2. R

R is a powerful open-source programming language and environment specialized in statistical computing and graphics. R language was developed by statisticians and data miners and has grown in popularity among data scientists due to its versatility and stability in data analysis.

Here is how R language’s popularity has grown over time

R is primarily developed as the first choice for the data science field. This fact is also supported by the popularity index ranking.

One of the main reasons why R is so popular in data science is its huge library of packages and libraries designed for statistical analysis, machine learning, and data visualization. These packages include an extensive range of tools and functions for efficiently performing different data manipulation, exploration, and modelling operations.

Some of the best R libraries for data science include:

• The ggplot2 package is well-known for its visually appealing and flexible graphical features. It allows users to create highly customized and publishable charts for demonstrating data patterns and relationships.
• The dplyr is a powerful package for data manipulation, including filtering, sorting, summarizing, and combining datasets. It provides a clear and simple vocabulary for carrying out these tasks, making data management activities more efficient.
• Another useful package for data science is tidyr. It helps users to transform cluttered datasets into a clean format suitable for analysis and visualization.
• The caret (Classification And REgression Training) is a complete package for machine learning and data science in R language. It offers one interface for training and reviewing numerous machine learning models, allowing users to compare algorithms and choose the best-performing ones for their data.
• The randomForest package implements the random forest algorithm, which is a popular collective learning technique for classification and regression tasks. It has become known for its durability and its ability to handle high-dimensional data with complex connections.
• The glmnet is a package that trains generalized linear models with regularization. It is especially effective for handling high-dimensional datasets to prevent overfitting.
• The tidyverse is a collection of R programs, including ggplot2, dplyr, tidyr, and others, that were created to operate together effortlessly for data science applications. It promotes a standardized and structured approach to data analysis and visualization.

3. SQL

Structured Query Language (SQL) is a powerful language used for managing and querying relational databases. It provides a common interface for interacting with databases, enabling users to execute a wide range of tasks such as data creation, modification, and retrieval. SQL is frequently used in data administration, analysis, and management and operates across numerous sectors. 

SQL has not lost its popularity ever since it was launched.

One of the primary reasons that SQL is popular in data science is its ease of use and versatility. It has a simple syntax that is easy to understand and apply, even for beginners. Additionally, SQL is extremely efficient at managing huge datasets, making it suitable for processing and analyzing massive volumes of data usually encountered in data science projects.

SQL’s compatibility with relational databases makes it an excellent choice for data scientists who need to utilize and manage structured data.

SQL has several extra features and capabilities that make it more effective in data science applications. These include support for complex queries, transactions, indexing, and security procedures, among other things. SQL’s extensive feature set enables data scientists to perform complex data analysis jobs and gain useful insights from their datasets.

When it comes to data science libraries and tools, SQL is frequently utilized with other programming languages and frameworks. Some of the top libraries and tools that connect with SQL for data science are:

• SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) module that provides a high-level interface for communicating with databases via SQL. It allows database access and maintenance in Python programs, allowing data scientists to interact more easily with SQL databases.
• Apache Spark is a strong distributed computing platform for handling enormous amounts of data. It features a built-in SQL query capacity, allowing data scientists to use SQL syntax for the analysis and processing of data in distributed environments.
• Apache Hive is a data warehouse infrastructure that runs on top of Apache Hadoop. It offers a SQL-like interface for querying and managing huge databases stored in Hadoop Distributed File System (HDFS), making it a vital resource for big data analytics and data science projects.

4. Java

Java is a high-level object-oriented programming language that was developed in 1995. Its popularity has decreased in recent years because of Python and other languages’ popularity but it has still ranged 2nd and 3rd in the PYPL and TIOBE index respectively.

Java is designed to be platform-independent, which means that it can operate on any device having a Java Virtual Machine (JVM), making it highly flexible and widely utilized across various operating systems and hardware platforms.

Java is gaining popularity in data science because of its robustness, scalability, and performance, although not specifically built for the purpose, as Python or R. Java’s strong typing system and vast libraries make it ideal for developing large-scale, enterprise-level applications, which are frequently used in data science projects.

One of the primary reasons Java is so popular in data science is its capacity to interact easily with big data platforms like Apache Hadoop and Apache Spark. These frameworks are often utilized to process and analyze huge quantities of data, and Java’s compatibility with them makes it a perfect fit for developing data-intensive applications.

Additionally to being compatible with big data frameworks, Java has several libraries and tools that can be used for data science operations. Some of the top libraries and frameworks used in Java for data science are:

• Weka is a set of machine learning algorithms designed for data mining tasks. It includes resources for data preprocessing, classification, regression, and clustering.
• Deeplearning4j is a Java-based deep learning package that uses a virtual machine called the Java Virtual Machine (JVM). It allows developers to create and train deep neural networks for purposes like as image recognition, natural language processing, and time series analysis.
• Apache Mahout is a scalable machine-learning library built on Apache Hadoop. It includes several machine-learning techniques and tools for collaborative filtering, classification, clustering, and recommendation.
• MOA (Massive Online Analysis) is a popular open-source framework for data stream mining. It includes methods for classification, grouping, regression, and frequent pattern mining, etc.
• Java Data Mining (JDM) is a standard Java API to develop data mining applications. It provides an integrated interface for connecting with various data mining methods and tools, making it easier to create and deploy data mining applications in Java.

5. Visual Basic for Applications (VBA)

Visual Basic for Applications (VBA) is a programming language created by Microsoft to be used within its applications. It is mostly used to automate jobs and increase functionality. It is a version of the Visual Basic programming language that has been optimized for utilization in the Microsoft Office suite of products, which includes Excel, Word, PowerPoint, and Access.

There are multiple reasons why VBA is popular for data science tasks:

• VBA provides direct interaction with Excel objects which makes it simple to modify data, build custom functions, and automate repetitive activities within the environment.
• VBA is very simple to learn, particularly for people who are already familiar with Excel. The syntax used is simple and intuitive, making it appropriate for users with different levels of programming experience.
• VBA enables users to utilize Excel’s capabilities beyond the built-in operations and features. VBA allows data scientists to construct custom solutions that are customized to their individual needs, such as importing and cleaning data, carrying out complex computations, and generating reports.
• Excel has a tool called “Macro Recorder,” which allows users to record their activities in the Excel interface and automatically produce VBA code. This type of function is very handy for beginners who may be uncomfortable with VBA syntax but want to automate operations.

VBA is not as powerful as other programming languages frequently utilized in data science, such as Python or R, but it is still popular among Excel users, particularly those who are already familiar with the Excel environment and prefer a simpler solution for their data analysis demands.

Some popular VBA libraries and tools for data science include:

• Power Query (not strictly a VBA library) is a powerful tool for data translation and manipulation in Excel that can be automated and improved using VBA.

6. Julia

Julia is a high-level, high-performance programming language for numerical and scientific computations. It was designed in 2011 (quite new) to overcome the limitations of other programming languages in terms of speed and accessibility for data science and computational tasks. Julia is rapidly gaining traction, becoming increasingly popular among developers and researchers alike.

Julia’s outstanding execution speeds are one of the key reasons it has grown in favour of the data science community. Julia’s just-in-time (JIT) compilation enables it to perform similarly to low-level languages such as C and Fortran while preserving a high level of abstraction and accessibility. This makes it ideal for working with massive datasets and executing complex numerical computations efficiently.

Another factor contributing to Julia’s popularity in data science is its syntax, which is intended to be easy to understand. Julia’s syntax is similar to that of other high-level languages, such as Python, making it accessible to programmers from various backgrounds.

In addition to its speed and ease of use, Julia has an extensive collection of libraries and packages built explicitly for data analysis and scientific computing. Some of the top libraries in the Julia ecosystem for data science are:

• JuliaStats is a package of statistical analysis, covering data manipulation, regression analysis, hypothesis testing, and machine learning.
• DataFrames.jl provides tools for working with tabular data that are similar to Python’s Pandas ability.
• JuliaDB is a program for working with large, distributed datasets that offers parallel processing and out-of-core computation capabilities.
• Flux.jl is a comprehensive framework for creating and training neural networks, such as support for computerized differentiation and GPU acceleration.
• Optim.jl is a package for optimization and mathematical programming that comprises a variety of algorithms for managing both unconstrained and constrained optimization problems.

7. JavaScript

JavaScript is a dominant programming language that is largely used for web development but has numerous other uses. It was designed to make webpages interactive and dynamic, allowing developers to alter data, validate forms, and create animations.

However, JavaScript has recently acquired popularity in data science due to its versatility and the availability of advanced tools and frameworks. It has ranked 3rd and 7th in the PYPL and TIOBE index respectively.

JavaScript’s widespread implementation is one of the primary reasons for its popularity in data research. Almost every modern web browser supports JavaScript, making it available to diverse developers. Furthermore, many engineers have prior knowledge of JavaScript from their web development work, making understanding and implementing JS in data science projects easier.

JavaScript’s dynamic nature and event-driven design make it excellent for managing large amounts of data and performing complicated computations rapidly. Its non-blocking I/O technique enables parallel operations, which may significantly decrease processing times, especially for jobs like web scraping and real-time data analysis.

Some of the important libraries and frameworks that have inspired JavaScript in data science include:

• D3.js is a complex JavaScript toolkit that lets you create interactive data visualisations in web browsers. It provides a flexible and declarative method of binding data to DOM components, allowing developers to easily construct complicated charts, graphs, and interactive maps.
• TensorFlow.js is a JavaScript toolkit that allows you to construct and train machine learning models directly in the browser or with Node.JS. It has APIs for both high-level model development and low-level tensor operations, making it suitable for a wide range of machine-learning applications, including image classification and natural language processing.
• Plotly.js is a popular JavaScript toolkit for making interactive charts and graphs. It supports a broad range of chart styles, including scatter plots, bar charts, and heatmaps, as well as interactive capabilities such as zooming, panning, and hovering.
• Brain.js is a JavaScript toolkit that allows you to design and train neural networks for a variety of machine-learning tasks. It offers easy APIs for generating feedforward and recurrent neural networks, as well as tools for data preparation.

8. C/C++

C and C++ are widely recognized as strong and optimized programming languages that provide low-level access to system resources, making them suitable for creating efficient and high-performance software applications. C and C++, which were developed in the 1970s and 1980s, respectively, have withstood the test of time and are still widely used in a variety of fields, including data science. They are quite fast, low-level, and robust. They are the backbone of some of the most popular machine learning libraries such as TensorFlow and PyTorch

Despite the emergence of newer languages in data science, such as Python and R, C, and C++ are still relevant and popular for particular jobs due to their speed and efficiency. C and C++ excel in data-intensive applications and high-performance computing, where rapid processing of big datasets is important.

One of the primary reasons C and C++ are used for data science is their ability to manage memory effectively. Unlike Python, which uses automated garbage collection, C and C++ need human memory management, giving developers more control over how memory is allocated and deallocated. This can result in more efficient memory utilisation and speedier execution times, particularly when working with huge datasets.

Furthermore, C and C++ provide sophisticated libraries and frameworks that are required for data science projects. Some of the best libraries for data science in C and C++ are:

• Eigen is a high-level C++ library that supports linear algebra, matrix and vector operations, numerical calculations, and other mathematical functions. It is widely utilised in a variety of applications, including machine learning and computer vision.
• Armadillo is another C++ library that supports linear algebra and scientific computing. It provides a simple interface for manipulating matrices, solving linear problems, and constructing numerical algorithms.
• Boost.Compute is a C++ library that supports general-purpose GPU programming. It offers a high-level interface for parallel computing on GPUs, making it ideal for boosting data processing workloads.
• Dlib is a C++ library that supports machine learning, computer vision, and image processing. It provides a diverse set of techniques and tools for applications including classification, regression, object identification, and facial recognition.
• MLpack is a scalable machine-learning Library built in C++. It implements many machine learning methods, including as clustering, dimensionality reduction, and supervised learning.

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9. Scala

Scala is a new, statically typed programming language that smoothly combines the object-oriented and functional programming paradigms. Although it is not that popular in terms of programming languages, it has ranked 19th and 38th in the PYPL and TIOBE index respectively.

Scala, which was designed by Martin Odersky, has grown in popularity because of its expressive syntax, robust type system, and powerful features. It runs on the Java Virtual Machine (JVM), allowing it to make use of the extensive Java library and tool ecosystem.

One of the primary reasons Scala is popular in data science is its ability to effectively manage large-scale data processing jobs. Its functional programming properties, including immutability and higher-order functions, make it ideal for processing and analysing large amounts of data. Furthermore, Scala’s support for parallel and concurrent programming enables data scientists to make use of multi-core computers and distributed computing platforms.

When it comes to data science, Scala has numerous top libraries and frameworks that are extensively utilized by practitioners.

• Scala is the core programming language for Apache Spark, a fast and general-purpose cluster computing system. Spark has high-level APIs in Scala, Java, and Python, but Scala’s clear syntax and functional programming characteristics make it ideal for developing Spark applications. Spark’s capacity to handle enormous datasets dispersed across several servers makes it a critical component of big data processing and analytics.
• Breeze is a numerical processing package for Scala that was inspired by NumPy. It supports linear algebra, numerical computation, and machine learning methods. Breeze’s integration with Scala’s type system enables fast and type-safe numerical operations, making it a popular choice for data processing.
• Apache Flink supports a variety of programming languages, including Java and Python. Scala is popular among developers owing to its conciseness and expressiveness. Flink is an open-source stream processing framework that allows for event-driven processing, stateful computations, and exactly-once semantics. It is widely used in real-time analytics, event-driven systems, and batch processing of huge datasets.
• Scalalgorithms is a collection of algorithms and data structures written in Scala. While not limited to data science, several of these techniques are widely employed in machine learning and data analysis activities. The library offers implementations of well-known techniques including k-means clustering, decision trees, and gradient descent optimisation.

10. SAS

SAS (Statistical Analysis System) is a software package created by the SAS Institute for advanced analytics, corporate intelligence, data management, and predictive analytics. It offers a complete platform for data analysis, statistical modelling, machine learning, and data visualisation.

• SAS is extensively used in data science and analytics because of its comprehensive statistical functions, data management tools, and machine learning capabilities, making it suited for a variety of jobs.
• SAS has been on the market for several decades and has established itself as a dependable and trustworthy solution for data analysis and modeling. It is frequently utilised in areas that require data integrity and dependability, including banking, healthcare, retail, and government.
• SAS can manage huge quantities of data and is ideal for enterprise-level analytics applications. It provides parallel processing and distributed computing, allowing users to analyse large datasets effectively.
• Despite its tremendous capabilities, SAS is noted for its user-friendly interface and clear syntax, making it accessible to users of all levels of technical skill.
• While SAS provides a wide range of data science tools, it also includes several specialized libraries and modules for specific needs. Some of the top libraries for data science in SAS are:
• The SAS/STAT library includes a variety of statistical analytic algorithms for exploring, analyzing, and modelling data. It contains techniques for regression analysis, variance analysis, cluster analysis, and other tasks.
• SAS/ETS (Econometrics and Time Series) is a library for analyzing and predicting time series data. It contains techniques for time series modelling, forecasting, and econometric analysis.
• The SAS/IML (Interactive Matrix Language) module enables users to execute matrix computations as well as create bespoke algorithms for data analysis and modelling. It is very beneficial for sophisticated statistical analysis and machine learning.
• SAS Enterprise Miner is an extremely effective tool for developing and deploying predictive models. It has a graphical interface for data mining and predictive modelling, as well as automated modelling tools for quick model generation.

11. MATLAB

MATLAB, or Matrix Laboratory, is a high-level programming language and interactive environment designed largely for numerical computing and visualisation. MATLAB, created by MathWorks, combines a complex programming language with outstanding computational capabilities, making it a useful tool in a variety of industries like engineering, physics, finance, and, of course, data science.

It has ranked 14th and 12th in the PYPL and TIOBE index respectively.

One of the reasons MATLAB is popular in data science is its extensive range of built-in functions and toolboxes dedicated to numerical analysis, data manipulation, and visualisation. These features enable data scientists to effectively analyse and visualise big datasets, do statistical analysis, and easily construct machine learning algorithms.

Some of the top libraries and toolboxes in MATLAB for data science include:

• The Statistics and Machine Learning Toolbox includes a variety of functions for statistical analysis, machine learning, and predictive modelling. It contains techniques for classification, regression, clustering, dimensionality reduction, and others.
• Signal Processing Toolbox is helpful for processing and analysing signals and time series data. It includes tools for filtering, spectral analysis, waveform synthesis, and feature extraction.
• Image Processing Toolbox is intended for image analysis and computer vision tasks. It includes functions for picture enhancement, segmentation, registration, and object detection.
• The Deep Learning Toolbox includes methods and algorithms for training and deploying deep neural networks. It has pre-trained models, transfer learning capabilities, and tools for constructing and visualising neural networks.
• Curve Fitting Toolbox is ideal for fitting curves and surfaces to data, this toolbox offers functions for nonlinear regression, interpolation, smoothing, and parameter estimation.

12. GO

Go (Golang) is a statically typed, compiled programming language created by Google in 2009. It is intended for simplicity, efficiency, and concurrency. Go has ranked 12th and 10th in the PYPL and TIOBE index respectively.

Go’s syntax is simple and understandable, making it a popular choice for developing scalable and dependable software systems.

Go’s excellent concurrency support is one of the main reasons it is so popular for data science applications. Concurrency is the capacity to execute many operations at the same time, which is critical for effectively processing huge datasets and complicated computations. Go’s built-in concurrency primitives, such as goroutines and channels, allow you to develop concurrent code without worrying about low-level aspects like thread management.

Furthermore, Go’s fast compilation and execution times make it ideal for handling data-intensive jobs. Its fast garbage collection and memory management add to its performance, making it a dependable choice for developing high-performance data science applications.

Some of the best libraries for data science in Go are:

• Gorgonia is a machine-learning library written in Go. It includes primitives for creating and training neural networks, as well as tools for optimizing and assessing models.
• Gota is a Go data frame and data wrangling toolkit that was inspired by Python’s pandas. It supports importing, modifying, and analyzing tabular data.
• Gosl is a scientific computing library for Go. It includes a diverse set of numerical algorithms and mathematical functions for applications like optimization, interpolation, and linear algebra.
• Goml is a machine-learning library in Go. It implements a variety of machine-learning techniques, such as classification, regression, and clustering.

13. Swift

Swift is a general-purpose programming language created by Apple Inc. It was initially launched in 2014 and has since grown in favour among developers because of its current syntax, safety features, and speed. It has ranked 9th and 20th in the PYPL and TIOBE index respectively.

Swift was created for iOS, macOS, watchOS, and tvOS development, but it has since grown to be utilised for a wide range of applications, including server-side programming, web development, and data science.

While Swift is not as well-known for data science as languages like Python or R, it does contain a few properties that make it an appealing candidate for some data science jobs. One of the primary reasons for its prominence in data science is its speed and efficiency. Swift was created with efficiency in mind, and its compiled structure enables efficient execution of code, making it well-suited for handling large datasets and computationally intensive tasks.

Furthermore, Swift’s type safety and current syntax help developers produce clean and maintainable code, which is critical for data science projects that frequently entail collaboration and iteration. Its compatibility with Objective-C and C lets developers use existing libraries and frameworks, boosting its data science capabilities.

While Swift may not have as many libraries dedicated to data science as languages such as Python, it does include several significant libraries that can be useful for data analysis and machine learning applications. Swift for TensorFlow is one such framework that combines TensorFlow’s capabilities with Swift’s simplicity of use, allowing developers to easily design and train machine learning models.

Other libraries, such as Surge and Swift AI, offer additional tools and utilities for data processing, visualisation, and model training. While Swift’s environment may not be as mature as those of other languages, its rising popularity and active community ensure that it continues to progress and improve for data science applications.

14. Hadoop

Apache Hadoop is a Java-based open-source platform for distributing and processing big information across commodity hardware clusters. Doug Cutting and Mike Cafarella created it in 2005, and the Apache Software Foundation now maintains it.

Hadoop became famous in the field of data science owing to its capacity to handle huge volumes of data effectively. It provides a scalable and fault-tolerant solution for storing and processing big data, making it excellent for organisations that work with enormous datasets.

One of the primary reasons for Hadoop’s reach in data science is its use of the MapReduce programming paradigm. MapReduce enables developers to create parallelizable algorithms that may process data simultaneously across several nodes in a Hadoop cluster. This allows for the effective processing of huge datasets by dividing the burden throughout the cluster.

In addition to MapReduce, Hadoop includes additional fundamental components such as Hadoop Distributed File System (HDFS) for distributed storage, YARN (Yet Another Resource Negotiator) for resource management, and Hadoop Common for utilities and libraries used by Hadoop modules.

Some of the top libraries and tools for data science that are commonly used with Hadoop include:

• Spark is a fast and versatile cluster computing technology that is frequently used with Hadoop for big data processing. It offers a more flexible and high-level programming approach than MapReduce, making it easier to create complicated data processing processes.
• Hive is a data warehouse infrastructure based on Hadoop that provides a SQL-like interface for searching and analysing Hadoop data. It enables data scientists to create SQL queries to interact with Hadoop data without having to master sophisticated MapReduce or Spark code.
• HBase is a distributed, scalable, NoSQL database built on top of Hadoop. It supports real-time read/write access to massive datasets, making it ideal for applications that require low-latency access to enormous data.
• Mahout is a Hadoop-based machine learning toolkit that allows for scalable implementations of different machine learning methods. It enables data scientists to train and deploy machine learning models on huge datasets via Hadoop clusters.

15. Kotlin

Kotlin is a present-day programming language that uses the Java Virtual Machine (JVM) and is intended to be completely compatible with Java. JetBrains, the makers of popular programming tools such as IntelliJ IDEA, created it as a Java alternative that prioritizes simplicity, conciseness, and safety.

According to the TIOBE index, the popularity of Kotlin has increased.

One of the main reasons Kotlin is popular in data science is that it integrates well with current Java libraries and frameworks. This interoperability enables data scientists to exploit Java’s broad ecosystem while simultaneously utilising Kotlin’s advantages.

In addition, Kotlin’s compact syntax and expressive characteristics make it ideal for data processing, analysis, and visualisation applications. Its current language features, like as type inference, extension functions, and coroutines, allow developers to build code that is clean, clear, and easy to maintain and test.

Some of the best libraries and frameworks for data science in Kotlin are:

• Kotlin Statistics is a library that contains statistical functions and techniques for data analysis. It has functions for computing standard statistical measures like mean, median, variance, and correlation, as well as methods for statistical tests and distributions.
• krangl is a Kotlin library for data manipulation and analysis based on the famous R package dplyr. It has a straightforward API for doing standard data manipulation activities including filtering, converting, summarising, and merging data frames.
• kplot is a Kotlin plotting toolkit with a simple API for producing data-driven charts and visualisations. It supports a variety of plot styles, including scatter plots, line plots, bar plots, and histograms, and allows you to customise the look and design of your graphs.
• KotlinDL is a high-level deep learning framework for Kotlin that offers a simple API for creating and training neural networks. It is developed on top of TensorFlow and Keras and provides abstractions for typical deep learning processes such as model definition, compilation, and training.

Choosing the Right Data Science Programming Language

The choice of the right programming language is based on factors such as compatibility with existing libraries and tools, its ease of use, readability, performance, scalability, and support for data manipulation and analysis.

Apart from these, the availability of specialized packages and frameworks for statistical analysis and machine learning, community support and documentation, and integration with other data science technologies are also quite important.

How to Improve your Data Science Programming Skills?

Improving your data science abilities is not just a nice idea, but also necessary in today’s ever-changing technological scene. Whether you’re just starting or an experienced programmer, there’s always space for development. In this section, we’ll look at some practical and easy strategies to improve your data science skills.

Master the foundation

First, master the foundations of data science before moving on to more complex topics. Understand fundamental concepts such as statistical analysis, linear algebra, and probability theory. This foundation will serve as the base for your advanced data science projects.

Learn a Programming Language

A data scientist’s toolkit is incomplete without proficiency in a programming language. Python and R are popular because of their ease of use and vast libraries for data manipulation and analysis. Invest your time in mastering one of these languages to boost both your efficiency and versatility in handling data.

Learn Data Structures and Algorithms

To strengthen your data science programming skills, learning data structures and algorithms enhances problem-solving skills, optimizes code efficiency, and enables a better understanding and implementation of complex machine learning and data analysis algorithms, all of which are necessary to address real-world data science problems with precision and efficiency.

Hands-on Practice

Theory only gets you so far; experience is where the true learning occurs. Participate in hands-on projects to apply your knowledge. Platforms such as Kaggle provide datasets and challenges, allowing users to address real-world issues while also collaborating with the worldwide data science community.

Participate in Coding Challenges

Participate in code challenges focused on data manipulation, analysis, and machine learning to improve your data science code skills. These tasks give you experience and help you strengthen your problem-solving skills. Participate in hands-on tasks, study advanced topics like algorithms and data structures, and collaborate with others to learn new techniques and approaches to data science programming.

You can use several coding challenge platforms like InterviewBit, Codechef, Leetcode, etc.

Network and Collaborate with Peers

Network and collaborate by connecting with other data enthusiasts through online forums, meetings, and conferences. Networking enables you to exchange knowledge, get insights, and even collaborate on initiatives. The data science community is large and supportive, making it a great place to learn and grow.

Develop Data Visualization Skills

Data scientists must be able to properly convey their results. Develop your data visualization abilities with Python tools such as Matplotlib or Seaborn. Clear and appealing visuals help stakeholders grasp complicated insights.

Share your work and receive comments from peers or mentors. Constructive criticism is an effective tool for development. Accept the mentality of continual learning, developing your talents based on feedback from others.

Improving your data science abilities is a continual effort, and implementing these practical recommendations into your learning process can put you on the path to becoming a more skilled and productive data scientist. Remember that consistency is important, so keep learning, trying, and pushing the boundaries of your data science expertise.

Check out the Scaler Data Science program to kick-start your career in Data Science.

Conclusion

• Top programming languages in data science offer a broad toolbox to meet diverse demands and preferences. Python’s simplicity and rich libraries make it an indispensable tool for data scientists, promoting efficient development and analysis.
• R’s statistical analysis capabilities are a valuable asset for deep data research. Julia’s rise is distinguished by its extraordinary numerical computation speed, making it an attractive solution for data-intensive jobs.
• The combination of SQL for effective database administration and Java’s adaptability strengthens the data scientist’s toolkit.
• Finally, the combination of these top programming languages enables data scientists to create significant solutions, promoting innovation in the ever-changing field of data science.