All rights reserved, A programming language is a formal language comprising a set of instructions that produce various kinds of output. Introduction to Data Science Languages. This technology is powerful for data analysis, image processing, and mathematical modeling. support and development services on a regular basis. First, modern programming languages are developed to take the full advantages of modern computer hardware (Multi-Core CPU, GPU, TPU), mobile devices, large-set of data, fast networking, Container, and Cloud.Also, most of the modern programming languages offer much higher developer Ergonomics as given below:. 42 Exciting Python Project Ideas & Topics for Beginners [2020], Top 9 Highest Paid Jobs in India for Freshers 2020 [A Complete Guide], PG Diploma in Data Science from IIIT-B - Duration 12 Months, Master of Science in Data Science from IIIT-B - Duration 18 Months, PG Certification in Big Data from IIIT-B - Duration 7 Months. A programming language is a formal language comprising a set of instructions that produce various kinds of output. in the world today. Improving memory operations means fewer opportunities for unauthorized access to data. In addition, SQL skills are one of the key requirements for a data science specialist. Deep Learning vs. Your email address will not be published. Scala is great for projects when the amount of data is sufficient to realize the full potential of the technology. . Types of Computer programming languages popularity with new digital creativity are most in demand trending programming language of 2020. This means that this technology is ideal for creating mobile applications that work with sensitive user data and are based on artificial intelligence. Standardization is one of the main advantages of the language; Simplicity and flexibility of the technology; Practicing programmers say that the analytical capabilities of SQL are limited by the functions of summing, aggregating, counting, and averaging data. Companies hiring specifically for Julia are definitely very low. Let us understand the features and advantages of some of these languages. Such high-level languages can create code that is independent of the computer type. Kotlin Taught by one of the best Data Science experts of 2020 Andrew Ng, this course teaches you how to build a successful machine learning project. This versatile language i… JavaScript 6. that can be quickly and easily scaled up for large applications. Low-level programming languages are faster and more memory efficient as compared to their high-level counterparts. Therefore, it is one of the most popular languages for data science as well. Continuing into 2020, expect leading names in tech to leverage their assets by bringing further consolidation to the data science market. Data scientists should weigh the pros and cons of the different types of. In addition, the scope of its application is not limited to working with data only. Basically, SQL is used for data management in online and offline apps. Required fields are marked *, PG DIPLOMA FROM IIIT-B, 100+ HRS OF CLASSROOM LEARNING, 400+ HRS OF ONLINE LEARNING & 360 DEGREES CAREER SUPPORT. These languages are used in computer programmes to implement algorithms and have multiple applications. The only difference between. And here is the study by Kdnuggets showing the most popular and frequently used of them. Scala 8. Swift is the main language for developing applications for operating systems such as iOS, macOS, watchOS, and tvOS. as well. R has multiple useful libraries for data science. In modern-day organizations, Scala supports object-oriented and functional programming as well as concurrent and synchronized processing. The multi-paradigm... 3. Hundreds of Java libraries are available today covering every kind of problem that a programmer may come across. The computation speed will decrease with a large amount of data; Scala combines an object-oriented and functional programming language, and this makes it one of the most suitable. There are some exceptional languages for creating dashboards and visualizing data. s, plus show the practical capabilities of each of them. The capabilities of Python allow you to write a program for machine learning tasks both from scratch and using various libraries and tools. This tool is not used for general-purpose programming, which makes it a highly-specialized language for working with big data. Since Scala is working on JWM, it provides access to the Java ecosystem. If you need to continue working with code created with Matlab using. For instance, it is possible to create a credit card fraud detection system using R or a sentiments analysis model to get insights on what users really think of a product or service. The only difference between SAS and R is that the first one is not open-sourced. Scala is difficult to learn, plus the community is not so wide. For over 15 years, we've published a list of the largest languages in the world – often cited by top-tier publications. The structured query language is one of the key tools for working with big data because it combines analytical capabilities with transactional ones. This article going to present the trends of top Programming Languages which will continue in the coming year 2020. There are some exceptional languages for creating dashboards and visualizing data. The open-source language and software are typically used for statistical computing and graphics. SAS, just as R, is a data analysis programming language, and its flexible possibilities of working with statistics are its main advantage. Since its inception, it has been very popular and one of the favorite general purpose languages among the data scientists. If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s PG Diploma in Data Science and upskill yourself for the future. Its applications range from web programming to machine learning. In this article, we decided to make a list of data science programming languages, plus show the practical capabilities of each of them. The appetite for third-party providers will grow. R. When talking about Data Science, it is impossible not to talk about R. In fact, it can be said that R is one of... 3. Data scientists should learn and master at least one language as it is an essential tool to realize various data science functions. However, today the capabilities of this technology are significantly expanded. Java is not suitable for highly specialized statistical solutions. Despite the fact that this is one of the oldest languages, developers have the opportunity to use a unique package of functions for advanced analytics, predictive modeling, and business analytics. The second type of programming languages provides a stronger abstraction of details and programming concepts. Top 5 Data Science Languages in 2020 | Data Science Tools analyticsvidhya.com • Data Science is one of the fastest-growing industries with an enormous number of tools to satiate your needs • Let’s talk about the different data … Most often, programmers are ardent supporters of either one or the other programming language. That provides greater flexibility for the developmental process. Being easy-to-learn, Python offers an easier entry into the world of AI development for programmers and data scientists alike.. Python is an experiment in how much … Julia can be used for both back-end and front-end programming, and its API can be embedded in programmes. Python 2. Thus, the project gets more flexibility and easy interchangeability when it is necessary to solve an atypical problem for one of the languages while using the other. JavaScript is another object-oriented programming language used by data scientists. Python is ideal for projects in which analytical and quantitative calculations should be a strength, for example, in the field of finance. I must confess that I don't agree totally with the info at the image. You do not need a license to use the product. It is also useful in embedding everything from electronics to desktop and web applications. 1. For instance, Python offers Django and Flask, popular libraries for web development and TensorFlow, Keras, and SciPy for data science applications. But if you ar e starting your programming career in 2020 or if you want to learn your first or second programming language, then it is wise to learn one of the mainstream and established programming languages.Here I will list programming languages based on the following criteria: Already mainstream and firmly established in the Software Development industry. There are numerous possibilities with MATLAB for data science. It first appeared back in 1993, and now it is widely used among data miners and statisticians for data analysis and developing statistical software. Best Online MBA Courses in India for 2020: Which One Should You Choose? Use our top talent pool to get your business to the next level. Similarly, some Python users dream of a wide range of statistical distributions available in R. This means that it is quite possible to combine the two leading technologies in one project to get a unique complemented set of functions. These include assembly language and machine language. Unpredictable behavior is minimized. Even though Visual Basic .NET continues to hold the sixth position as last year in the most popular programming languages list for 2020, there is an overall decline in the ratings of the language. Online Data Science Master’s – Guide to Choosing a Program. This technology is powerful for data analysis, image processing, and mathematical modeling. That is why it is the best data science language for the data scientist. Low-level languages are relatively less advanced and the most understandable languages used by computers to perform different operations. showing the most popular and frequently used of them. Swift Python Guido van Rossum designed Python in the year 1991. As the name implies, Matlab is the best programming language for data science when it comes to the need for the most profound mathematical operations. The open-source language and software are typically used for statistical computing and graphics. To predict the trend of the programming language in 2020 this article uses data from authentic surveys, various collected statistics, search results and salary trends according to programming languages. It is a fairly new, dynamic, and highly effective tool among. The capabilities of. And it is an excellent language to deal with matrices. So, if you want to give your business more fuel in the form of data, think about creating an appropriate solution and contact us for advice today! 11 data science languages to choose from 1. It is a fairly new technology, but this did not prevent it from becoming one of the favorite tools of iOS developers; It is possible to use Swift only for operating systems that were released after iOS7. Those aspiring to enter the field may consider specializing in a. to start their journey. Taking into account the evolving situation Yes, some experts believe that it will take a long time until this language takes an honorable place in the arsenal of data science experts, but now there are enough, to help solve various problems when working with big data and machine learning. It is an open-source, easy-to-use language that has been around since the year 1991. Julia 9. This eBook lists out the top 10 programming languages for Data Scientists, in the order of their importance and use. This domain-specific language is extremely convenient for storing, manipulating, and retrieving data in relational databases. If you are going to analyze a huge data array and make a lot of statistical calculations, then R is the best choice (sometimes in conjunction with Python); Java and Scala are suitable for the solutions that need the greatest performance with their further integration into the already existing apps. Java pays great attention to security, which is a key advantage when working with sensitive data. These languages are used in computer programmes to implement algorithms and have multiple applications. as well. There are two basic ways: Simply put, each of these languages ​​has a special package directory, some of which make it easy to use packages in another language. Matlab 7. However, it is worth recognizing that each of them has its strong points, as well as weaknesses. Ex-Google/ex-Facebook TechLead lists his top coding languages to know in 2020. For this reason, the data science specialist is considered the most sought-after profession of the next decade, and the best technological minds will continue to come up with new tools for more efficient work with data. JavaScript: Don’t judge me by my bad parts, learn the good stuff and stick with that! Before becoming an expert in data science, learning a programming language is a crucial requirement. Although not exclusively used for data science operations, knowledge of SQL tables and queries can help data scientists while dealing with database management systems. There’s Java, C#, Python, PHP, JavaScript, and many others. Popular processing frameworks like Hadoop run on Java. It comes with both command line interface as well as graphical front-end. It is a closed source software – however, it is offset by a large number of libraries and packages for statistical analysis and machine learning. Both the efficiency and the cost of the development project will depend on the chosen programming language or framework as well. Now you have seen some of the best programming languages for data science. Home > Data Science > Top Data Science / Machine Learning Languages & Tools to Master in 2020 Data Engineers and Machine Learning Engineers are witnessing a steep rise in their demand and career prospects, thanks to the widespread adoption of Big Data, AI, and ML. However, there are a lot of other useful tools that can be suitable for data science tasks, and they are discussed below as well. ForecastWatch analytics uses this language to work with weather data. Thus, this is the point you should pay attention to. About the E-book. In the astronomically growing cyberspace of the 21 st century, coding is (and will continue being) a hot skill. #2 JavaScript JavaScript is a popular language among web developers which gave rise to several frameworks that simplify your code. More efficient error handling implemented in Swift significantly reduces the number of crashes and the emergence of critical scenarios. Built-in functions allow you to perfectly visualize any data. Features and Applications of the Top 10 Programming Languages in Data Science; DOWNLOAD NOW. Even in today’s digital age, Data Science still requires a lot of manual work. Initially, Julia was designed as a language for scientific programming with speed sufficient to meet the needs in modeling in an interactive language, followed by the inevitable processing of code in a compiling language such as C or Fortran. Fortune 500 Companies and Data Science Programming Languages We can’t stress enough how important are Python and R for the data science field in 2020. Python allows for integration with TensorFlow, SQL, among other data science and machine learning libraries. The following are the 4 hottest Data Science trends for the year 2020. The structured query language is one of the. However, their strengths are their flaws, when it comes to big companies. Here is the analysis of data from indeed.com with respect to choice of programming language for machine learning and data science. The main problems of R are safety, speed, and the amount of memory spent. While assembly language deals with direct hardware manipulation and performance issues, a machine language is basically binaries read and executed by a computer. . Also, it is the most powerful tool for statistical analysis of the existing ones. In addition, the scope of its application is not limited to working with data only. Python. For this reason, the data science specialist is considered the most sought-after profession of the next decade, and the best technological minds will continue to come up with new tools for more efficient work with data. Machine Learning and NLP | PG Certificate, Full Stack Development (Hybrid) | PG Diploma, Full Stack Development | PG Certification, Blockchain Technology | Executive Program, Machine Learning & NLP | PG Certification, Low-level and High-level Programming Languages. If you’re new to the exciting (and occasionally frustrating)worldof mobile app development, there are two languages you need to learn ASAP:Kotlin and Swift. It allows you to perform operations on data processing, mathematical modeling, and work with graphics as well. You do not need a license to use this tool; Julia language works with data faster than Python, JavaScript, Matlab, R, and is slightly inferior in performance to Go, Lua, Fortran, and C; Numerical analysis is the strength of technology, but Julia also copes well with general-purpose programming. With significantly less data, Python or R is likely to be more efficient. Python, as always, keeps leading positions. Get awesome updates delivered directly to your inbox. It is an ideal language to start diving into data science. ... Top 6 Data Science Programming Languages for 2019; Python vs. R vs. SAS – which tool should I learn for Data Science? Accordingly, numerous Artificial Intelligence (AI) algorithms can be effectively executed in it. Data science is not the hype of recent years. These are trends that have gathered increasing interest this year and will continue to grow in 2020. All these programming languages have some pros and cons. ... 2020 at 12:24am . It is an open-source, easy-to-use language that has been around since the year 1991. Your email address will not be published. More implementations would be seen in the year 2020, which is an advantage for experienced python developers, as well as those that want to learn the programming language. Data science has been among the top technologies today and has become marketwide a strong buzzword. There are several. programming languages ​​for data analytics. Julia can be used for both back-end and front-end programming, and its API can be embedded in programmes. Modern data science specialists have a large selection of technologies for implementing a wide variety of tasks. This versatile language is capable of handling multiple tasks at once. SQL. It has a wide range of functions for statistics and machine learning. R is a high-level programming language built by statisticians. It is quite unexpected to see the most popular general-purpose programming language as the, for big data, isn’t it? Plus, it is perfectly possible to combine Java code with specialized data science tools. R is a high-level programming language built by statisticians. (If you’re already involved in mobile development, you’re notshocked at all by that statement.) The programming syntax and data structures of the python very simple and easily learned. It is also a scalable and effective language for handling big data. For example, R users sometimes crave object-oriented features built into the Python language. Like Matlab, Octave can be used in projects with a relatively small amount of data if strong arithmetic calculations are needed. Python is ideal for projects in which analytical and quantitative calculations should be a strength, for example, in the field of finance. Best top 10 programming languages of the future to learn in 2020. JavaScript. Dynamic typing complicates the search for some errors associated with the misappropriation of various data to the same variables. Moreover, they are portable, closer to human language, and immensely useful for problem-solving instructions. By using our website you agree to our, Ruby vs Python: Difference Between the Programming Languages, The Fundamental Differences Between Data Engineers vs Data Scientists, Top 11 Data Analytics Tools and Techniques: Comparison and Description, Guide on Machine Learning vs. Natural data processing and data learning become a cakewalk with the packages contained in Python. This technology is ideal for projects in the field of finance, plus there is great hope that Julia will be able to compete fully with Python and R when it becomes more mature. What is more, Python is used for artificial intelligence development, which is one of the most promising innovations used in the financial sector. Python. And it is an excellent language to deal with matrices. Thus, the choice of this tool as one of the best languages for data science will depend on the project specifics. and its flexible possibilities of working with statistics are its main advantage. Big data does not have to exist in the cloud – it can exist in user’s smartphones. SAS 10. There are some exceptional languages for creating dashboards and visualizing data. R is not just a language but a whole environment for statistical calculations. Over the years, Structured Query Language or SQL has become a popular programming language for managing data. It is a universal language that allows you to create any project – from simple applications to machine learning programs; Python is clear and intuitive – it’s the best choice for beginners; All necessary additional tools are in the public domain; Add-on modules and various libraries can solve almost any problem. ... 2020 16645. It also supports multiple paradigms, from functional to structured and procedural programming. And it is one of those. R is open-source and allows you to work with many operating systems, thanks to the fact that this tool is cross-platform; Statistics is the strength of this technology. We will contact you within one business day. Data scientists should learn and master at least one language as it is an essential tool to realize various data science functions. Top 10 Data Science Programming Languages for 2020 Python R SQL C (C++) Java Javascript MATLAB Scala Swift Julia top programming languages for data science. Therefore, many data scientists use high-level programming languages. Hundreds of programming languages dominate the data science and statistics market: Python, R, SAS and SQL are standouts. By Kat Campise, Data Scientist, Ph.D. An online master’s degree in data science is an interdisciplinary program geared to help students prepare for a career as a data scientist.Generally, coursework is focused in computer science, math, and statistics. Thus, it will be necessary to look for answers to many questions on your own in case of difficulties. Chances are, if you've seen a website or blog listing the "largest" or "most spoken" languages, it was from one iteration or another of our list. This tool is a good fit when a project is created at the intersection of the web and big data technologies. Let us understand the features and advantages of some of these languages. Therefore, many data scientists use high-level programming languages. The best feature of Scala is the ability to run parallel processes when working with large data arrays. It is a total rethinking of approaches and principles of working with data for the benefit of both individuals and companies and the whole of humanity. JavaScript is another object-oriented programming language used by data scientists. R can come handy for exploring data sets and conducting ad hoc analysis. Besides, this language is used by Google and YouTube to improve internal infrastructure. © 2015–2020 upGrad Education Private Limited. In this vast field, Python clearly emerges as a winner with over 70,000 libraries and about 8.2 million users worldwide. This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply. R is not just a language but a whole environment for statistical calculations. For example: Our team of data science experts has extensive experience in solving various problems. Hundreds of Java libraries are available today covering every kind of problem that a programmer may come across. It can quickly implement mathematical concepts like linear algebra. Basically, SQL is used for data management in online and offline apps. However, the loops have more than 1000 iterations, and it is more complex to learn than Python. It allows you to perform operations on data processing, mathematical modeling, and work with graphics as well. An assembler software converts the assembly language into machine code. Over the years, this language has been a leader in the frequency of use by programmers worldwide and in the number of tasks it allows to solve. best programming language for data science. Moreover, Python makes it easier for programmers to read the data in a spreadsheet by creating a CSV output. There are several programming languages for data science as well. When it comes to choosing programming language for Data Analytics projects or job prospects, people have different opinions depending on their career backgrounds and domains they worked in. And popular Tensorflow.js is one of them. Due to the fact that this is a fairly new tool, users note a narrow community, possible problems when searching for errors and malfunctions, as well as a limited set of options; Modeling is done using Python libraries, with logical losses in quality and performance; Partially implemented visualization: thanks to the PyPlot, Winston, and Gadfly libraries, data can be displayed in 2D graphics. JavaScript is another object-oriented programming language used by data scientists. Before becoming an expert in data science, learning a programming language is a crucial requirement. Python Programming by Unsplash. There are more than 250 programming languages in the world today. There are a lot of libraries for Scala that are suitable for data science tasks, for example, Breeze, Vegas, Smile. Python is the most widely used data science programming language in the world today. Julia is a data science programming language that has been purpose-developed for speedy numerical analysis and high-performance computational science. There are a lot of programming languages for data science. And popular. What is more, Scala is created in such a way that data science can perform a certain operation using several different methods. The analysis of huge data sets gives access to non-obvious insights that can be used for any purpose – from improving the efficiency of the HR department of your company to defeating global problems. Hundreds of Java libraries are available today covering every kind of problem that a programmer may come across. Octave 11. Developed in 1991, Python has been A poll that suggests over 57% of developers are more likely to pick Python over C++ as their programming language of choice for developing AI solutions. So, if you want to give your business more fuel in the form of data, think about creating an appropriate solution and, We use cookies to ensure you get the best experience. That is why the result of working with this language is ideally combined with the Python and C language libraries. Artificial Intelligence, Best Machine Learning as a Service Companies, Cassandra vs. MongoDB: How to Make the Right Choice, Node.js vs Python: What to Choose for Backend Development. that has been purpose-developed for speedy numerical analysis and high-performance computational science. He is responsible for the entire data pipeline from data collection, cleaning, and preprocessing, training models and deployment into production. Top Programming Languages for Data Science in 2020 1. Being a high-performance language, Java may be the right choice for writing machine learning algorithms. Our team of data science experts has extensive experience in solving various problems. Vitaliy worked on projects related to computer vision and Machine Learning, Data Science, IoT. Python-like syntaxis, but compared to Python, it is a more efficient, stable, and secure programming language; Since Swift is native to iOS, it is very easy to deploy the created application on mobile devices with this operating system; The open-source Swift internal compiler and static typing allow you to create custom AI chipsets at build time; It is possible to efficiently use C and C ++ libraries in combination with Swift. Python These companies usually mention Julia’s skill as an addition or organization working in the research domain. The Ethnologue 200. Though Python has been around for a while, it makes sense to learn this language in 2020 as it can help you get a job or a freelance project quickly, thereby accelerating your career growth. And it is one of those data science languages that can be quickly and easily scaled up for large applications. regarding the Covid-19 pandemic, we want to assure that Jelvix continues to deliver dedicated Scala was initially designed to address issues with Java. ForecastWatch analytics uses this language to work with weather data. However, there are a lot of other useful tools that can be suitable for data science tasks, and they are discussed below as well. By the way, SQL and Python mentioned above are on this list as well; 95% of companies use Java for web and mobile application development. in this video we will be discussing about the top 5 programming languages for Data Science. Python is one of the best programming languages for data science because of its capacity for statistical... 2. Many experts believe that JavaScript should remain in its place and not to pry into high technology. But, it has several applications in data science as well and. Here are the Top 10 Programming Languages for Data Science 1. 1. Conclusion. This modern and elegant programming language was created way more recently, in 2003. I have created a list of Top 10 programming languages for Data Science that you can learn in 2020 and also while there is still some time to hit back outdoors The languages made to the list on the basis of their popularity, number of Github mentions, the pros and the cons, and their relevancy to Data Science in 2020. With less than 1000 iterations, it is faster and a better option for data manipulations. R 3. In 2014, R was the highest-paid technology to possess; R has more than 2 million users across the globe. Read more about the most common software development strategies and take a look at their benefits and drawbacks. Python holds a vital place among the top tools for Data Science and is often the go-to choice for a range of... 2. Therefore, Swift can be used to create mobile applications for the aforementioned operating systems when there is a need to connect big data and artificial intelligence. However, there are no statistics on Java usage for data science and big data due to the relative novelty of these concepts. Last Update made on August 19,2016. With less than 1000 iterations, it is faster and a better option for data manipulations. In this article, we decided to make a list of. It is the main alternative to Matlab that we have already mentioned above. Those aspiring to enter the field may consider specializing in a data science language to start their journey. Yes, some experts believe that it will take a long time until this language takes an honorable place in the arsenal of data science experts, but now there are enough native libraries to help solve various problems when working with big data and machine learning. It is also useful in embedding everything from electronics to desktop and web applications. It is a fairly new, dynamic, and highly effective tool among programming languages ​​for data analytics. It is quite unexpected to see the most popular general-purpose programming language as the best programming language for big data, isn’t it? It can quickly implement mathematical concepts like linear algebra. Basic knowledge of Python also helps in picking up computing frameworks such as Apache Spark, famous for its data engineering and big data analysis tasks. Moreover, Python makes it easier for programmers to read the data in a spreadsheet by creating a CSV output. Top 10 Best Programming Languages for Data Science Top 10 Best Programming Languages for Data Science Last Updated: 07 Jun 2020. Thus, the choice of this tool as one of the, Due to its wide applicability, Java is one of the most frequently used programming languages worldwide, according to the. Natural data processing and data learning become a cakewalk with the packages contained in Python. Popular processing frameworks like Hadoop run on Java. Data scientists should weigh the pros and cons of the different types of programming languages for data science before making a decision. So how can this be done in practice? It also supports multiple paradigms, from functional to structured and procedural programming. Python and R are the most adopted open-source data science languages, startups are looking towards hiring professionals with these skillsets. Also, it is the most powerful tool for statistical analysis of the existing ones. It is an ideal language to start diving into data science. In general, both of these technologies do not have extremely fundamental differences, just some exceptions. SQL 4. But, it has several applications in data science as well and R has multiple useful libraries for data science. of data scientists are using Python daily; It is predicted that Python will keep its leading position. Data Science. Jelvix is available during COVID-19. There are a lot of packages for statistical analysis and machine learning; Tensorflow is able to help with the creation of web-based AI projects with simplified functions. There are two types of programming languages – low-level and high-level. Home > Full Stack Development > Top Languages For The Full Stack Developers in 2020 With so many full stack languages around, you can easily get confused, trying to figure out where to start. Concise and terse code (less boilerplate coding) The R programming language is not new, but it has become more popular in recent years, thanks to the big data revolution. Matlab is suitable for applications that need strong arithmetic support – for example, signal processing. 2. Python developer with 7+ years experience in CV, AI & ML, passionate about creating machine learning models and object detection systems. This general-purpose and dynamic language is inherently object-oriented. It can also be used for solutions from the educational and industrial sectors. This technology is suitable when there is an initial intention to integrate the created product with existing solutions. What is more, Python is used for artificial intelligence development, which is one of the most promising. You will understand the complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance. This general-purpose and dynamic language is inherently object-oriented. R is also one of the top programming languages for data science. Python, as always, keeps leading positions. © 2015–2020 upGrad Education Private Limited. Python: Python is viewed as in any case in the rundown of all Artificial Intelligence (AI) development programming languages because of the simplicity.. Besides, this language is used by Google and YouTube to improve internal infrastructure. Java 5. This versatile language is capable of handling multiple tasks at once. SAS is suitable for projects which have high demands for stability and security. when it comes to the need for the most profound mathematical operations. (1) Automated Data Science. Top Programming Languages for Data Science in 2020 1.
2020 top languages for data science 2020