The discipline of Big Data analytics is a safe pick for any professional searching for a rewarding, high-paying career, as Big Data continues to rise in relevance at Software as a Service (SaaS) organisations. If you're thinking about starting or advancing your career in Big Data and data science, we've listed three prominent programming languages you should master to help you get started: R, Hadoop, and Python are three programming languages.
Reasons to learn R
A smart data scientist is a passionate coder who also happens to be a statistician, and there's no better programming language to learn for a statistician than R. R is known as the "golden child" of data science since it is the industry standard among statistical computer languages. It's a popular ability among Big Data analysts, and data scientists who know R are in high demand from companies like Google, Facebook, Bank of America, and the New York Times. R's commercial applications are growing by the minute, and businesses value its versatility.
If you're still on the fence about learning R, here are a few more reasons why you should learn R:
- R has a large and active community as well as a resource bank -
With a large global community of enthusiastic users who routinely participate on discussion forums and attend conferences. Also, around 2000 free libraries are available for your unlimited usage, encompassing statistical areas of finance, cluster analysis, high-performance computing, and more.
- R is a free and open-source programming language -
You can freely install, use, update, clone, alter, redistribute, and resell R, unlike SAS or Matlab. This not only saves businesses money, but it also makes upgrades simple, which is ideal for a statistical programming language.
- R is a scripting language with a lot of power -
As a result, R is capable of handling huge, complex data collections. R is also the ideal language for running big, resource-intensive simulations on high-performance computer clusters.
- R is a cross-platform programming language -
R is a programming language that runs on Windows, Mac OS X, and Linux. It can also import data from programmes such as Microsoft Excel, Microsoft Access, MySQL, SQLite, Oracle, and others.
- R is a favourite among publishers -
R is simple to connect with document preparation systems such as LaTeX. As a result, R's statistical output and images can be incorporated in Word documents.
- R has received a lot of positive feedback -
R is one of the most popular programming languages in 2017, with an estimated 2 million users.
- R is a very adaptable and evolving language -
Many new statistical developments begin as R packages.
Reasons to learn Hadoop
Hadoop is another programming language you should master if you want to succeed in the Big Data area.
If you're unsure about Hadoop vs. Python, the following information may be useful.
- Hadoop is a powerful tool -
Hadoop is capable of storing and processing large amounts of data with ease. Many have been impressed by its sheer power and capacity. Hadoop has "become a must-have for large companies, creating the cornerstone of any flexible future data platforms required in the era of the customer," according to Forrester. - Multinational Corporations are Increasingly Using Hadoop -
This programming language is used by top firms such as Dell, Amazon Web Services, IBM, Yahoo, Microsoft, Google, eBay, and Oracle.
- Hadoop, like R is an open-source project -
As a result, Hadoop is a versatile solution.
- Hadoop's Future Is Bright -
Hadoop will be an essential skill set for everyone interested in a career in Big Data at some time.
- Hadoop is Lucrative -
Hadoop is one of the most in-demand skills in the Big Data industry, and qualified Hadoop developers may expect to earn good money.
- Hadoop is adaptable -
Hadoop is used for predictive analytics, data discovery, and ETL, in addition to warehousing data.
- Hadoop can help you in a variety of job opportunities -
Hadoop Architects, Hadoop Developers, Data Scientists, and Hadoop Administrators are some of the jobs available to Hadoop experts.
Reasons to learn Python
Python is another programming language that people who want to work in the Big Data or data science industries are advised to learn.
It is less difficult to learn than R, but it is a high-level programming language that web and gaming developers prefer.
- Python is an open-source programming language -
Python is a free open-source programming language, which appeals to startups and small businesses. Its simplicity appeals to smaller groups as well.
- Python is less difficult to debug -
Bugs are every programmer's greatest fear, which is why Python's unique nature makes it ideal for data scientists just getting started. Debugging is easier when there is less code to write. Python-compiled programmes are less prone to bugs than programmes developed in other languages.
- Python is compatible with the Raspberry Pi -
If you want to do incredible things with your Raspberry Pi, you'll need to learn Python. Anyone may now use Python to create real-world apps, from beginners to experts.
- Python is a simple language to learn -
Python's fundamentals, like those of Java, C, and Perl, are easier to comprehend for newcomers. Because of the language's user-friendly properties including code readability, easy syntax, and ease-of-implementation, a programmer writing in Python writes less code.
- Python is a popular programming language -
The Python programming language, like R, is utilised in a wide range of software and industries. Google's search engine, YouTube, DropBox, Reddit, Quora, Disqus, and FriendFeed are all powered by Python. Python is used extensively by NASA, IBM, and Mozilla. You might be able to get a job at one of these big-name organisations if you're a proficient Python specialist.
- Python is a Powerful Programming Language -
Python has long been the language of choice for creating mission-critical systems that are also quick.
- Python is an Object-Oriented Programming Language (OOP) -
Because you'll only need to master the syntax of the new language if you have a firm understanding of the foundations, you'll be able to migrate to any other object-oriented language.
Advantages of learning R
R's success is based on a number of benefits it delivers to both novices and experts. The following are some of the many advantages of R programming:
- R's tremendous community support has resulted in a massive library collection. The graphics libraries in R are well-known. The R development environment is supported and enhanced by these libraries. R offers a large number of libraries with a wide range of applications.
- R can use web scraping and other methods to collect data from the internet. It also has the ability to cleanse data. The practise of finding and removing/correcting erroneous or faulty records is known as data cleansing. R may also be used for data wrangling, which is the act of turning raw data into a format that can be consumed more easily.
- R is an interpretive programming language. It does not require the use of a compiler to convert the code into an executable programme. R, on the other hand, decodes the given code into lower-level calls and precompiled code.
- R can also be used for machine learning. R is used by Facebook for a lot of their machine learning research. R is used for sentiment analysis and mood prediction. When it comes to machine learning, the optimum use of R is for research or developing one-off models.
Advantages of learning Hadoop
It's a framework for analysing data. Apache Hadoop is a well-known example of a framework that businesses use today to analyse large amounts of data. As a result, Hadoop skills can assist you in forming corporate relationships and providing excellent corporate training. Do you know why it's important for us to learn it? Because it offers numerous benefits and is in high demand.
- From a business standpoint, Hadoop serves as a research tool. Businesses can use big data to uncover answers to questions they didn't know how to ask. It also aids research and development efforts.
- Within its framework, it has a wide range of uses, from posing novel new questions to data processing and interpretation. It also exposes solutions to common problems through data analysis of all available big data.
- To manage large amounts of unstructured data, learn Hadoop. Because there is more big data, the demand for using it for big data analysis and management is growing. Learning can be difficult at first. However, you can readily learn to manage Big data with professional training.
Advantages of learning Python
When studying a new language, such as Python, you must be aware of the language's advantages. This will assist you in better understanding how to make the most of the Python programming language.
- Until we run the code, Python has no idea what type of variable we're dealing with. During execution, it assigns the data type automatically. The programmer is not required to declare variables or their data types.
- Python is an extremely useful programming language. Python's simplicity allows developers to concentrate on the subject at hand. They don't need to spend a lot of time learning the programming language's syntax or behaviour. You write less code and accomplish more.
- You must update your code in various languages, such as C/C++, to run the programme on different systems. With Python, however, this is not the case. You only have to write it once and it may be used wherever.
Conclusion
In an increasingly data-driven society, the fields of Big Data and data science will only continue to flourish. Make sure your job grows along with you by enrolling in online courses that will improve your expertise and credibility. The article titled as “Major Reasons you should learn R Programming, Hadoop Programming, and Python Programming” - this elaborated article will help you to get nuances about these three programmes.
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