21 Reasons You Should Learn R, Python, and Hadoop
As the Big Data Analytics domain continues to acquire greater prominence at SaaS (Software as a Service) companies, the rush to break into Big Data has reached unprecedented levels. With plenty of job opportunities and considerably high pay benefits, Big Data Analytics is a safe bet for any professional looking for a high-paying career that is also fulfilling. And you just can’t go wrong with a Data Science certification.
But what if you’re new to the field? Where do you begin? What do you need to learn to get started and find your footing in the Data Sciences?
We asked our panel of expert industry insiders, and pat came the answer – R, Hadoop, and Python. Learning these languages is absolutely essential to get anywhere in this industry. Why? Read on to find out why you should put them on your must-learn list for this year!
At heart, a good data scientist is a passionate coder-slash-statistician –and there’s no better programming language for a statistician to learn than R. THE standard among statistical programming languages, R is sometimes called the ‘golden child’ of data science. It’s a popular skill among big data analysts, and data scientists skilled in R are lapped up by some of the biggest names in business, including Google, Facebook, Bank of America, and the New York Times.
Sound interesting? R’s commercial applications are growing by the minute, and companies absolutely love its versatility. Here are more reasons why you should add R to your skillset:
R is freely available - Unlike SAS or Matlab, you can freely “install, use, update, clone, modify, redistribute, and even resell” R.
So not only is it a major cost-saver on projects, but it also allows for easy and constant upgradation of versions, which are useful features for a statistical programming language.
R is cross-platform compatible - R can be run on Windows, Mac OS X, or Linux (go here to learn more).
R is a heavy-duty language - As a powerful scripting language, R will help you handle large, complex data sets. It is a great programming language to compute Big Data. R is also the best language to use for heavy, resource intensive simulations. Furthermore, R can be used on high performance computer clusters which manage the processing capacity of huge numbers of processors.
R has widespread acclaim - Given all these benefits, it’s not surprising that R is gaining wide-spread acclaim – it is estimated that it has about 2 million users, and a recent poll suggests that it is the most popular language in data science.
R leads innovations in statistics - Many new developments in statistics first appear as R packages. This is because R is highly flexible and evolved.
R is loved by publishers - R is a language that integrates easily with document publishing. By integrating smoothly with the LaTeX document publishing system, statistical output and graphics from R can be embedded in word-processing documents.
It is also user-friendly while importing data from Microsoft Excel, Microsoft Access, MySQL, SQLite, Oracle, and so on.
Huge, vibrant community + resource bank- R has a large, global community of passionate users who regularly interact on discussion forums and attend conferences.
Apart from this, there are about 2000 free libraries available for your unlimited use that cover statistical areas of finance, cluster analysis, high performance computing and more.
For many beginners in data science, the first foray into programming starts with Python. Largely because it is easier to learn Python, which is syntactically a simpler language than R. This high level programming language is the preferred choice among web and game developers.
Read on for more reasons why Python should be on your learning list for 2016!
Python is easy to learn - Like Java, C, and Perl, the basics of Python are easier to grasp for newbies. A programmer coding in Python would be required to write less code owing to its beginner-friendly features like code readability, simple syntax, and ease-of-implementation.
Python is easier to debug – Finding and squashing bugs is every programmer’s worst nightmare, which is why Python’s unique design lends itself well to programmers starting out in data science. Writing less code means it is easier to debug, and programs compiled in Python are also prone to fewer issues than those written in some of the more popular languages out there.
Python finds widespread application - Just like R, the Python programming language finds application in a variety of software packages and industry areas. Python powers Google’s search engine, YouTube, DropBox, Reddit, Quora, Disqus, and FriendFeed. NASA, IBM, and Mozilla, too, rely heavily on Python. And as a skilled Python specialist, you could land a job at any of these big names.
Python is an Object Oriented Language - A strong grasp of the fundamentals of this object-oriented language means that you can then migrate to any other object-oriented language by having to learn only the syntax of the new language.
Python is open source - As an open-source programming language, Python is available free of cost. Startups and small-scale companies thus benefit hugely from this. Owing to the fact that the language is also simple by nature, it can be handled efficiently by a smaller team.
Python is a high-performance language - To build business critical applications that are quick and fast, Python has long been the language of choice. With its huge standard library and resources, the assistance required to stay productive is but a click away.
Python and the Rasberry Pi - Not only is it a preferred language among web and game developers, but if you are thinking of doing some amazing things with the Raspberry Pi, then Python is what you need to learn. From amateurs to expert programmers, anyone can now build a real world application with the use of Python.
If you are planning to make it in the Big Data field, you can’t afford to skimp on Hadoop. Here are the most important reasons why you should begin learning this framework, if you haven’t already.
Hadoop is powerful and open-source - Available as an open-source software framework, Hadoop is easily able to store and process huge amounts of data. Its sheer horsepower and capability has many impressed. Forrester has this to say about Hadoop - “It’s become a must-have for large enterprises, forming the cornerstone of any flexible future data platforms needed in the age of the customer”.
Hadoop is well-suited for marketing - Hadoop can make that big difference to organizations by aiding them in their marketing needs. This it does with retail analysis - finding more about customer behavior patterns on the web, providing personalized recommendations, aiding personalized targeting, and more.
Hadoop is one of the fastest growing techs out there - Hadoop and NoSQL are identified as the fastest growing technologies in the data market by Wikibon, a Technology Research Organization. All the more reason for you to learn Hadoop –unless you want to get left behind in the dust by your peers, that is!
Another report from Markets and Markets Research states that the Hadoop and Big Data Analytics market will reach $13.9 billion by 2017.
Hadoop opens up tremendous opportunities in a wide range of roles - Finding implementation in diverse industries, Hadoop professionals can find work in a wide range of roles such as Hadoop Architect, Hadoop Developer, Data Scientist, and Hadoop Administrator. In addition, implementing this technology is possible only when the professional has a deep-rooted understanding of the framework.
Hadoop pays well – Hadoop is among the most sought-after skills in the Big Data market, and certified Hadoop developers can expect to take home fat paychecks. Organizations are willing to pay a premium and invest in Hadoop-skilled professionals as long as the right talent is available.
Hadoop has a healthy future ahead - For any professional seeking to grow big in the Big Data domain, this skill is a must-learn at some point of their career. The Big Data market is growing exponentially and Hadoop as a framework is not going to disappear anytime.
Hadoop is finding increasing adoption at global MNCs - Top companies like Dell, Amazon Web Services, IBM, Yahoo, Microsoft, Google, eBay, Oracle are betting big on the implementation of this framework. Want to be a part of the game?
Take your career a level higher with our Big Data and Hadoop Administrator training course.
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