Advantages of Python over Java in Data Science | Expert’s Top Picks [ OverView ]

Advantages of Python over Java in Data Science | Expert’s Top Picks [ OverView ]

Last updated on 21st Dec 2021, Blog, General

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Nirvi (Python developer )

Nirvi is a Python developer with 7+ years of experience in the Hadoop ecosystem, Sqoop, Hive, Spark, Scala, HBase, MapReduce, and NoSQL databases, such as HBase, Cassandra, and MongoDB. She spends most of her time researching technology and startups.

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One of the main reasons why Python is widely used in the scientific and research communities, is because of its ease of use and simple syntax which makes it easy to adopt for people who do not have an engineering background. It is also more suited for quick prototyping.

    • Introduction to Data Science
    • Python or Java: The Eternal Question
    • Java vs Python for Data Science – A Comparison
    • Why advantages of Python over Java make it better for Data Science?
    • Are You Interested in Careers in Data Science?
    • Syntax with example
    • When to use which?
    • Why do data scientists love Python for Data Science?
    • Java for Data Science – Should data scientists learn Java?
    • Conclusion

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      Introduction to Data Science:

      Data science is a hot topic today, and the need for data scientists is even greater. A few years ago, Business Insider listed the data scientist as “America’s foremost work.” The position continues to be high on the latest job search list.

      Planning languages ​​are a very important tool, often used in the data scientist repertoire. So, what is the most popular data science language that gets the top spot? That would be Python, and we’ll show you why. Data Summary of Data Science Languages. There are many editing languages ​​(or data science languages, if you prefer) to choose from. Here is, for example, a list of the 10 most popular planning languages ​​from 2020.

    • Python
    • JavaScript
    • Java
    • C#
    • C
    • C ++
    • GO
    • R
    • Swift
    • PHP

    • However, not all planning languages ​​are permanent. Google’s quick search reveals a number of articles and blogs announcing the upcoming death of selected data science languages. For example, predicts that subsequent languages ​​will be extinct within a few years.

    • Ada
    • C
    • Haskell
    • LISP
    • Purpose-C
    • Perl
    • R

    • Warning readers will note that two possible languages ​​also appear in the top 10. Python and Java, however, remain popular. Python, however, has advanced in this controversy. According to Statistics, Python is more popular than Java since May 2021, although, according to the full disclosure requirements, C beat both of them.

      Python or Java: The Eternal Question:

      But we are here to talk about the choice of data science – Python or Java; so let’s move on.

    • Parameters
    • Java
    • Python
    • Neat / Portable

    • It is most popular in mobile and web applications. Suitable for desktop GUI applications, embedded systems, and web application applications. The popularity of artificial intelligence, machine learning, and the Internet of Things. Suitable for computer science and numeracy tasks.


    • He is very tall and verbose. It requires ten lines of code just to read it in a Java file.
    • It works well. Requires only two lines of code to read in a Python file.

    • Language Type

    • Object planning language. Combined language.
    • Writing language. Translated language.

    • Learning Curve

    • Compared to Python, it is a very important learning curve.
    • Easy to read, short learning curve.

    • Ifa

    • It has a long history in business. Java legacy systems are generally very broad and extensive.
    • It has a small legacy, although old in both languages, which was released in 1991.

    • Speed

    • As it is an integrated language, it takes less time to create code, which makes Java faster.
    • Python is relatively slow in comparison because it is a translated language that determines the type of data during operation.

      Java vs Python for Data Science – A Comparison:

      A good way to make decisions is sometimes to take a deeper look at the good and the bad that goes with both sides of the issue. If you are a beginner in data science or starting a new data science project and are confused about which language would best suit you, here is a deeper look at some of the key points to consider when deciding a programming language.

      Java vs Python Data Science- Syntax

      Python is a dynamic typed language, whereas Java is a strictly typed language. This means that in the Python case, the type of variance data is determined during operation and can change throughout the life of the system. In Java, the type of data must be given a variable while encoding, and this type of data remains the same throughout the life of the system unless explicitly changed. This allows for easy use in the Python case when it comes to programming. Powerful typing allows the program to be written in small lines of code. Python is very important because of its simplicity and ease of use. It is well known that it is easy to learn and use and is often the language of the program preferred by beginner designers. Python also does not follow rules for retransmission, binding instruments, or the requirement to use semicolons. Java, on the other hand, has strict syntax rules. If syntax rules are not followed, the code will provide an error during compilation and will not work.

      Java vs Python Data Science- Performance

      In terms of speed, Java is faster than Python. It takes less time to create source code than Python. Python is a translated language, which means that the code is read from line to line. This often results in slower performance depending on speed. Debug fix also only happens during operation, which can also cause problems when using codes. Another point to note is that the type of flexibility data should be determined during operation in the Python case. This, in turn, tends to slow down the process. Unlike Python, Java is also capable of handling multiple statistics simultaneously, which adds to its speed.

      Why advantages of Python over Java make it better for Data Science?

    • Both languages ​​are common, however we should always take into account that we have a tendency to be discussing knowledge scientists nowadays, and there’s a vital distinction. Knowledge scientists typically work with AI and machine learning, 2 invasive commands, and Python works best in each.

      Java is nice for coming up with sites, however if you’re an information person operating with thought machines or automatic tasks, you would like to use Python. These statistics on the utilization of coming up with language among knowledge scientists reinforce the purpose. Here are a number of the exciting tidbits that contribute to Python’s express applications over Java, creating it the most effective selection for knowledge scientists:

    • Ready for net development. Yes, Java dominates most net developers, however, Python is additionally an honest selection, creating it an honest tool with enough flexibility for each knowledge Scientists and net developer. So, if an information person needs to do his hand at net development, he does not have to be compelled to learn another programming language – Python has them! There also are several libraries and stacks choked with stacks dedicated to net applications, that greatly speed up secret writing and create the total development method a lot economical. Here are a number of the options full of the far-famed Python stacks:

    • It has several libraries. Python includes a giant assortment of many time-saving libraries and frameworks. several Python libraries concentrate on machine learning, big data, and knowledge analysis. These libraries embody NumPy, Pandas, and SciPy.

    • Python is rated. Quantifiability suggests flexibility, a feature needed by science. Python provides programmers with alternative troubleshooting choices, typically as well as new updates that are simply additional to them.

    • Python includes a giant community. intensive user communities are useful as they supply tips, feedback, workarounds, and new perks or content. Communities like Stackoverflow offer support to alternative Python users.

    • Can Python eventually replace Java and Java? The advantages of Python over Java in terms of its application to knowledge Science, AI and Machine Learning are clearly provided. Therefore, something will happen, though it is often difficult to alter from the same old to the new, particularly if the conventional will be an honest enough job. Why must you create any spare changes? At the moment, we all know that knowledge scientists like Python and its several edges. It’ll be exciting to visualize if the language will adequately penetrate net and application development and become the language utilized in all IT-related fields.

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      Are You Interested in Careers in Data Science?

    • Data science is an exciting field with many opportunities for growth and job security. If you like this field, ACTE has everything you need to start your data science career.

    • ACTE’s Post Graduate in Data Science program, co-hosted by Purdue University and partnered with IBM, is suitable for all practicing professionals, covering critical topics such as R, Python program, machine learning algorithms, NLP concepts, and Tableau data visualization on – great information on interactive learning model with live sessions conducted by international staff, working labs, IBM Hackathons, and industry projects.

    • But there is more! ACTE offers you SkillUp, a free learning platform that can help you improve your skills and help you achieve your career goals. You can become an awesome power in the world of data science if you take the Post Graduate program and complete it with some SkillUp offerings.

    • According to Indeed, data scientists in the United States earn an average annual salary of USD 120,101, while in India, data scientists average an annual average of ₹ 822,895, as reported by Payscale.

      Syntax with example:

      Represents real-world businesses as a programming language focused on an object. It is a randomly typed language and checks type during operation. Unlike Java, Python is a translated language that uses a line-by-code in Python IDE (Integrated Development Area).

        1) Hello world Example

      • Let’s take a simple example of the Hello world program to understand the fundamental differences between the two.
      • // create a HelloWorldExample class
      • HelloWorldExample community class
      • {
      • // main () first method
      • public static void main (String [] args)
      • {
      • // print hello world
      • System.out.println (“Hello World!”);
      • }
      • }
      • Print (“Hello World”)
      • From the above programs, it is clear that Python has a smaller line of code compared to Java.

      2) Syntax

    • As we have discussed above, Python is a dynamic typed language. It means we do not need to define a variation as it is automatically checked by the interpreter during operation. The Python code is readable and easy to use because of its “English-like” design.
    • Python does not use tools to make code look like pseudocode.
    • In Java, we have to declare the variable explicitly due to your variable nature. In Java, the code will not cover even if something goes wrong.
    • Therefore, Python syntax is very simple and readable.
    • Let’s understand some of the differences based on application, job opportunity, and salary.

    • 3) Based on the app

    • Let’s understand the differences between Python and Java on the basis of Machine Learning, Data Science, and Web Development.
    • Machine learning.
    • Python is very popular for machine learning. Python is a complete language for general purpose. People who try to bring AI power into their fields also draw on the use of Python and its importance in their fields.
    • Java programming is also a good Machine Learning option in a situation where the platform is based on Java, and the site is old.

    • 4) Based on Job Opportunity

      Both languages have a different set of job opportunities. But it is worth noting that Python made even greater progress than Java.

      5) Based on Salary

      In both languages, Python is the most widely used programming language. The average salary for Python developers may be higher, but it cannot explain all the features of Python selected by developers.

      When to use which?

      Python is very widespread due to its simplicity. easy to browse and use. However, if you wish to produce an Associate in Nursing app, you have to be compelled to analyze the strengths and weaknesses of languages ​​before making a range. If you are a beginner and want to seek out a programming language faster, Python has to be your choice. Python is well versed in data science and smart intelligence. AI developers like Python over Java due to its simplicity, simple use, and accessibility.

      However, the nice advantage of Java over Python works. With its Java virtual machine (JVM) java is the simplest language once it involves speed and efficiency. The excellence in performance between Java and Python is vital. Java uses JVM to perform timely integration therefore once speed is your goal; then Java has to be compelled to be your decision.

      Java handles compatibility on top of Python. The pliability of multiple codes to be used at a similar time is termed system compliance; Python is very sequential.

      The Java mantra of writing if it works anywhere makes it best suited to the event of the choice field. Python desires the smallest code and should embrace any bugs in your code. Python is straightforward to use / browse and provides code simplicity unremarkably.

      Another issue to ponder between these two languages ​​is typing. Python uses dynamic formats, whereas Java uses the default version. This greatly affects vogue, downside finding, and coding. Obviously, typed languages ​​are straightforward and pithy. See the code below showing the word “I am Associate in Nursing Engineer” in Python and Java

      • Python code
      • Items = [“I am Associate in Nursing engineer”, 8]
      • For me in things:
      • Print (i)
      • Java code
      • Community class check ownership (string args []) ;
      • Okwa (String I: array)
      • }
      • }

      The two are very similar. They have intensive libraries with an associate degree oversize community. They specialize in the issue, support encapsulation, and polymorphism. when you start a project, you’ve got to be compelled to opt for that language that works best for you. Python clearly contains a limit on simplifying and making your comes work whereas Java surpasses Python faster and plenty of with efficiency. If you’d wish to boost mobile applications, web applications, and web of things Java have to be compelled to be your decision. Python will even be used for a decent variety of applications, but its edge over Java is simple and applied to data science (Big data or data mining), machine-learning and machine learning. AI is that in the long run, and Python contains a better chance of pattern it inside the long run.

      Why do data scientists love Python for Data Science?

      Professionals working in the field of data science – be it data scientists, machine learning engineers, or data analysts who do not want to be confused with programming languages with complex syntax and limited libraries for handling large data when using complex mathematics and statistics. . This is the main reason why Python has proven itself to be the most popular Data Science option. There are always new data science libraries and machine learning libraries released to meet the needs of a separate data science.

      Python is a simple and easy-to-read editing language. It requires fewer lines of code than other programming languages ​​to perform similar tasks. Because of its simplicity, it is easier to maintain focus especially on mathematical management than to be preoccupied with details of system flow management. Python has excellent memory management capabilities, especially garbage collection. This makes it an excellent planning language for data scientists to control large amounts of data. Python is a cross-platform programming language, which means that the same code works in different environments without any changes to the code. This makes it easy to switch between locations if necessary.

      Java for Data Science – Should data scientists learn Java?

      Is Java right for your data science projects? There is no right or wrong answer to this but knowing Java is really beneficial because it offers a lot of other resources when working with data science applications. Many of the top companies like Spotify, Uber, continue to use Java and Python to host important business data science applications.

      Many data scientists tend to rely on Python and R to write data analysis and processing programs. However, agencies like Apache Spark, Kafka, Hadoop, Hive, Cassandra, and Flink all operate on JVM (Java Virtual Machine) and are very important in the Big Data field. JVM is also a cross-platform and allows the same code to be copied to multiple locations. Java is a very flexible programming language. Creating the most complex tasks is easy on Java as it makes it easy to upgrade or downgrade and provides excellent load balancing features. Java is strictly typed, meaning that the variant data type is determined at startup and the same variant data type must be maintained until the end of the program. Type can only change with transparent mechanical streaming.


      Using Python is just like a useful helper that manages your tools. It works as a glue language for beginners and professionals to come together and work together to advance science.

      Although Java certainly has its advantages and does wonders in coding, Python has just started to make more sense. Its offerings are very flexible, easy to use and create a pleasant coding experience.

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      Organized language wars are many excuses for people to develop a language they love and to enjoy trampling on people who use something else. So I want to make it clear that I have no interest in starting another online debate about Python compared to the R of data science.

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