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Top 12 Programming Languages for Data Science in 2024

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18th Jun, 2024
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    Top 12 Programming Languages for Data Science in 2024

    Data science is a multidisciplinary field that requires a broad set of skills from mathematics and statistics to programming, machine learning, and data visualization. The world has been swept by the rise of data science and machine learning. Data scientists are in high demand, and the demand will only continue to rise. However, data scientists need to know certain programming languages and must have a specific set of skills. It can be daunting for someone new to data science. But if you know where to start, it’s not as hard as it might seem. 

    Data science programming languages allow you to quickly extract value from your data and help you create models that let you make predictions. That’s why it’s important to know which languages are best for different tasks. To ensure that you can pick the right tool for your job, this article will look at some of the most popular data science programming languages scientists use today. The choice becomes easy when you are aware of your data science career path

    List of Top Programming Languages for Data Science

    Here is a list of data science programming languages to know in 2024:

    • Python
    • SQL (Structured Query Language)
    • R
    • Julia
    • JavaScript
    • Scala
    • Java
    • Go
    • MATLAB
    • C/C++
    • Statistical Analytical System (SAS)
    • Swift

    Top Data Science Programming Languages

    Computers are powerful tools for data scientists. They allow us to manipulate, analyze, and visualize our data sets in ways that would be impossible by hand. Browse the Data Science online course to know all there is to learn regarding data science. In data science, programming is essential, but many programming languages are present. So, for data science which language is required. Here are the top nine programming languages that data scientists should know: 

    1. Python

    Python is a general-purpose programming language that can get used to develop any software. It is among the top programming languages for data science. Python is known for its simple syntax, easy readability, and code portability. It's also open-source and runs on all major platforms, making it popular among developers. Python is easy to learn and has a large community of developers behind it, so there are plenty of resources to help you get started. It's also powerful enough to be used by professional data scientists. 

    Python is a fantastic language for new programmers since it employs a simple English language and provides a variety of data structures. In addition, it is a machine-level language with a great public reputation. This language is the best option if a student is entering the field as a fresher in the company. Check out the best Python course to upgrade your career.

    2. SQL (Structured Query Language)

    SQL is one of the world's most widely used programming languages. It is a declarative language for interacting with databases and allows you to create queries to extract information from your data sets. SQL is used in almost every industry, so it's a good idea to learn it early in your data science journey. SQL commands can get executed interactively from a terminal window or through embedded scripts in other software programs such as web browsers or word processors. 

    Structured Query is a programming language used in data science that is specified to the domain. SQL in data science helps users collect data from the databases and later edit them if the situation demands it. Therefore, a student who wants to work as a data scientist must understand Structured Query Language and databases well. If one wants to excel in data science through SQL, one can consider Data Science online courses to become a professional data scientist.

    3. R


    R is a statistical programming language commonly used for statistical analysis, data visualization, and other forms of data manipulation. R has become increasingly popular among data scientists because of its ease of use and flexibility in handling complex analyses on large datasets. In addition, R language data science offers many packages for machine learning algorithms such as linear regression, k-nearest neighbour algorithm, random forest, neural networks, etc., making it a popular choice for many businesses looking to implement predictive analytics solutions into their business processes. For example, thousands of packages are available today for R, allowing you to analyze financial markets and easily forecast weather patterns! Learn more about R in Data Science course.

    4. Julia 

    Julia is an important language for data science that aims to be simple yet powerful, with a syntax similar to MATLAB or R. Julia also has an interactive shell that allows users to test code quickly without having to write entire programs simultaneously. In addition, it's fast and memory-efficient, making it well suited for large-scale datasets. This makes coding much faster and more intuitive since it allows you to focus on the problem without worrying about type declarations. Read more about Julia for Data Science in this blog!

    5. JavaScript

    JavaScript is a programming language used to develop web applications and websites. It has since become the most popular language for building client-side applications online. JavaScript is also known for its versatility, as it can get used for anything from simple animations to complex artificial intelligence applications. Keep reading to know more about the data science coding languages. To know more about this, check out the JavaScript certification course.

    6. Scala 

    Scala has become one of the most popular languages for AI and data science use cases. Because it is statically typed and object-oriented, Scala has often been considered a hybrid language used for data science between object-oriented languages like Java and functional ones like Haskell or Lisp. In addition, Scala has many features that make it an attractive choice for data scientists, including functional programming, concurrency, and high performance. Scala training equips individuals with the skills needed to leverage these capabilities effectively in various data analysis tasks. 

    7. Java

    Java is a general-purpose computer programming language that is concurrent, class-based, object-oriented, and specifically designed to have as few implementation dependencies as possible. As a result, Java is the best coding language for data science. It is intended to let application developers "write once, run anywhere" (WORA), meaning that compiled Java code can run on all platforms that support the Java virtual machine (JVM) or JavaScript engines. However, code that uses platform-dependent capabilities may not run on all JVMs because they are not required to implement those features. All these data science coding languages are important to becoming a data scientist. Enrolling in a Java course can provide essential skills to utilize its capabilities effectively. 

    8. Go

    Go is a programming language data science which is also referred to as GoLang. This programming language is gaining fame slowly and comes in handy in projects related to machine learning. It came out in 2009 when Google introduced it to the world. With a syntax quite similar to C language, people call this the next step in the hierarchy of C language.

    Go being a middle-level language helps users operate with ease. It is quite the flexible type, and within ten years since its release, it is rapidly coming into the light. When it comes to Data Science, this programming language helps massively in ML operations. However, because of the lack of usage, its reach boundaries are still very tiny compared to Java and Python.

    9. MATLAB 

    MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming. There are many languages required for data science. MATLAB allows matrix manipulations, plotting functions, and data, implementation of algorithms, creation of user interfaces, and extension of existing software. It makes MATLAB useful for developing applications that analyze large amounts of data. The name "MATLAB" is an abbreviation for matrix laboratory. Enrolling in a MATLAB training can significantly enhance one's skills. 

    10. C/C++

    C/C++ is a general-purpose programming languages data science used to develop computer applications. It is a low-level language used for high-performance applications like games, web browsers, and operating systems. C/C++ is also used for numerical computations, in addition to its widespread use in application development. 

    The best coding language for data science allows you to work with large volumes of data quickly and efficiently. They should also be easy to use and have a wide range of features that support the entire data science workflow, from exploration to modeling and visualization. Programming language is the most important for data science. Coding creates analytical models and algorithms that allow them to solve complex problems. 

    11. Statistical Analytical System (SAS)

    This data science programming language is specially built for business operations and complex arithmetic computerization. Having been around the data science industry for a considerable time, many companies have adopted SAS to carry out their tasks. The drawback of SAS is that it requires a license to put it to use, unlike Python and Java. Like MATLAB, SAS also loses the crown to Python and R language regarding accessibility. For new consumers and companies, this provides a barrier to access, making them more likely to choose easily accessible languages like Java or C++. Explore further about SAS for data science.

    12. Swift

    Swift, a relatively new programming language, offers faster performance compared to Python and is nearly on par with C. Its straightforward syntax, stability, and security make it an excellent choice, especially for iOS app development. Additionally, Swift training boasts strong support for automatic differentiation, crucial for deep learning research, making it a valuable skill for aspiring data scientists.

    How Is Programming Used in Data Science?

    Data Science Programming Languages spell productivity and the ability to store data in large chunks. The realm of data science comes with machine learning, geospatial analysis, and much more. All these domains require programming languages to carry out operations.

    No matter where one looks in the realm of data science, they will always find the need for programming skills. Programming is inevitable, and I cannot list all the places it is used. Some departments that demand data science languages would be Manipulating data along with extraction, analyzing data on a statistical basis, machine learning, and automation. Enrolling in a Data Science online training can prepare you well for this field.

    Different coding skills are language knowledge are required for each of these steps.

    • Problem Statement: The first step of data analysis is understanding the problem statement. In this stage, no such programming skills are required. Instead, in this step, one must figure out the required tools and software.
    • Obtaining Data: Now and then, someone fills the form and gives away their data. So, there is no shortage of data, but the issue is the quality of data to be retrieved. For this, programmers have to use coding skills like SQL and NoSQL. 
    • Cleaning Data: After gathering all essential data, the data must be cleansed. Data scientists can clean data using programming languages such as R and Python. Softwares like Trifecta Wrangler and OpenRefine also come in handy in these processes.
    • Analyzing Data: A dataset is ready to be studied if it is clean and properly prepared. Python is widely used in the data science field for data analysis. R and MATLAB are also popular since they were designed for data analysis. 
    • Visualizing Data: Visualizing data analysis results assists data scientists in communicating the significance of their labor and discoveries. This may be accomplished by using graphs, charts, and other easy-to-read visualizations, allowing larger audiences to comprehend a data scientist's work. Python is a popular language for this stage, and libraries like seaborn and Prettyplotlib may assist data scientists in creating graphics. Pandas is one of the data analysis and manipulation tools that assist in getting your visualization right. Get to know more about how to master Pandas for data science

    Best Programming Language for Data Science

    Programming languages in data science have recently gained great fame in the computer science industry. Python, JavaScript, and MATLAB are some top-drawer languages that can help a data scientist succeed in his career.

    On shortlisting those languages down to one, the top data science coding language would be Python. Python has huge demand, and according to Anaconda's 2024 survey, 34% of users claim Python to be the best programming language for data science. But to be honest, choosing the best language for data science does not depend on the public point of view. Instead, the scientist's experience, among other factors like the project at hand, comes into consideration. 

    Looking to enhance your business analysis skills for a unique learning experience? Unleash your potential mastering business analyst training online

    What Is Data Science?

    Data science is the application of scientific methods, processes, algorithms, and systems to analyze and interpret data in various forms. Data science focuses on synthesizing, predicting, and describing patterns found in large data sets to infer insights, root out the hidden meaning, and discover new knowledge. Data scientists are also often referred to as business analysts or statistical modelers. 

    Data scientists are thought leaders who apply their expertise in statistics and machine learning to extract useful information from data. They can work with various tools to analyze large datasets, including social media posts, medical records, transactional data, and more. The role requires extensive knowledge of data science languages like Python or R and tools like Hadoop, Spark, or SAS. 

    Also, data science can get used in various applications, including business intelligence (BI), predictive analytics, social media analytics, medical informatics and bioinformatics, financial risk management, fraud detection, and e-discovery applications.

    Although, data science is very different from the traditional conception of computer science. At the same time, computer science has traditionally focused on algorithms, coding, and software[development processes, data science focuses on extracting meaning from data using systematic methods. Data science focuses on extracting value from data to improve business processes and decision-making. You can also check the Data Science Bootcamp cost

    How do I get started in Data Science?

    Data science is a hot topic these days. Everyone is talking about Big Data and big trends in data science, but it's not always clear how to get started in this field. If you're interested in getting into data science, here are some tips that can help get you started on your journey: 

    • Learn the basics of programming: Although there are many types of data science jobs, almost every job requires some programming ability. Therefore, it's important to have at least a basic understanding of how to program before getting into this field. You don't need to be an expert programmer or know specific languages. Still, if you want to work as a programmer or analyst for most companies, it's good to know what programming involves and what types of problems programmers face when writing code for applications. Start by learning the best language for data science, such as Python. 
    • Get familiar with machine learning algorithms and techniques: Machine learning is one of the most popular areas within data science today. So if you want to stand out from other applicants when applying for jobs or internships, then make sure that you understand how machine learning works, which algorithms are popular, and why they're used. You don't need to be a statistician or machine learning expert. Still, it helps if you understand the fundamentals of these disciplines, including probability distributions, Bayesian statistics, linear regression, decision trees, logistic regression, etc. 
    • Learn from your peers: Data scientists are everywhere, so look around at what people in other departments are doing with data and find someone who can mentor you through your first steps. You might even uncover that they have complementary skills in the best programming language for data science, allowing them to teach you something new while teaching you something old! For example, if you're a business analyst and want to get data science jobs for freshers, look for opportunities to learn from others doing it. Maybe there's an open-source project that interests you, or maybe a company in your area offers classes for aspiring data scientists. 
    • Increase the difficulty: Once you've got a few basic concepts, try taking on harder problems and using more advanced techniques. For example, use your skills to analyze different data types or try out a new tool like R or Python. For example, if you're comfortable building simple linear regression models or natural language processing data science, try tackling logistic regression instead or maybe even deep neural networks! 

    Conclusion

    The rise of data science has been exceptionally fast and is a niche in huge demand. Every firm needs data scientists to gain a competitive advantage in the market. But if you are willing to pursue this field and are looking for top languages for data science to start your coding journey, this article has discussed everything for you. Explore KnowledgeHut's Data Science Bootcamp cost – you could have the best shot at excelling data science.

    Each listed programming language in data science has been designed with machine learning, big data, linear regression, and other statistical tools as part of their frameworks. As a result, programming skills are very important these days, and knowing the right data science language can do wonders for your career. 

    Frequently Asked Questions (FAQs)

    1What are the top four programming languages used by Data Scientists?

    A survey of nearly 24,000 data professionals by Kaggle revealed that Python, SQL, and R are the most popular programming languages.

    2Why is Coding Required in Data Science?

    There are different steps of data analysis. These include obtaining, cleaning, analyzing, and visualizing data. Different stages of data analysis require different programming skills. The extraction of data required MySQL, whereas analysis requires R language and python language. Similarly, for visualizing, Matplotlib is used.

    3What Jobs in Data Science Require Coding?

     All the fields of data science require coding. However, the depth of knowledge requirement varies. For example, a data scientist into artificial intelligence may require in-depth coding knowledge compared to other Jobs.

    4What are some reasons Python is popular for doing data science?

     Python is one of the interpreted programming, object-oriented, and high-level programming languages used in data science. The code is easy to learn and write and has better readability.

    5Which is the best data science programming language in 2022, Python or R?

    Python is a flexible, sophisticated programming language that may be used for various data and computer science activities. On the other hand, the R programming language is popular in data science for data assessment. Therefore, understanding R is essential if a user wishes to advance in data research.

    6Which programming language is best for data science?

    The most popular coding language for data science nowadays is Python. This dynamic, all-purpose language is by nature object-oriented. Additionally, it enables a variety of computing paradigms, including functional, organized, and iterative. Natural data processing and data learning become a cakewalk using the modules included in Python. Additionally, Python creates a CSV file that makes it simpler for coders to look at data in a worksheet. 

    7Is C and C++ used in data science?

    Knowing C and its near relative C++, two of the most efficient languages, can be quite helpful in tackling computationally demanding data science tasks. C and C++ are significantly quicker than other computer languages, so they are excellent choices for creating big data and machine learning apps. C and C++ are some of the hardest languages because of their low level. Thus, understanding them is a wise move that can greatly improve your CV, even though they might not be your first picks when entering the field of computer science. 

    8Which programming language should data scientists learn first?

    Python is frequently considered among the simplest programming languages for newcomers to understand and use because of its basic and accessible syntax. Python is among the greatest alternatives if you're new to data science and aren't sure which programming to start with. We can assist you if you wish to become an authority on Python. Begin your learning to become a professional data scientist by looking through the Python programs in our course catalog.

    Profile

    Kevin D.Davis

    Blog Author

    Kevin D. Davis is a seasoned and results-driven Program/Project Management Professional with a Master's Certificate in Advanced Project Management. With expertise in leading multi-million dollar projects, strategic planning, and sales operations, Kevin excels in maximizing solutions and building business cases. He possesses a deep understanding of methodologies such as PMBOK, Lean Six Sigma, and TQM to achieve business/technology alignment. With over 100 instructional training sessions and extensive experience as a PMP Exam Prep Instructor at KnowledgeHut, Kevin has a proven track record in project management training and consulting. His expertise has helped in driving successful project outcomes and fostering organizational growth.

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