10 Reasons You Should Learn R, Python, and Hadoop

Data Analytics Domain continues to excel at Software as a Service, or SaaS companies, as we popularly know it. Everyone wants to break into Big Data and they have a lot of job opportunities on the rise. But making taking a step forward into Data Sciences it is imperative to understand what it is and which Data Science Certification to opt for. This is where R,  Python  and Hadoop come in and here are ten good reasons to know them. These are basically programming languages that need to be learnt to break into the data sciences industry, which includes top names like Google, Bank of America and The New York Times.

  1. Availability: How is a new user supposed to learn them? R, for example, is free to install and run and that gives the user the independence to sit and learn about it anywhere. Python, on the other hand, is easier to learn and some say it is the easiest of programming languages.  Hadoop Certification , is again, available on open source networks, which makes it easily available. Depending upon your convenience, the user can use any of them.


  2. Easy Upgrades: As far as data analysis is concerned, these three open- source programming languages are the most popular. Data import visualization, MapReduce and Parallel Processing can be best achieved with them, as a result of which the integrated analysis platforms have to be constantly upgraded, which is again made easier by them. 


  3. Cross Platform: The programming languages can all be used across multiple platforms, like Windows, Mac OS X, Linux and a few more, allowing the users to get their work done on any device. R and Python developers are now coming up with ways to deal with larger data sizes across larger platforms, and working on both SQL and NoSQL databases.


  4. Complexity made Simple: These three programming languages are used for handling large and complex data, otherwise known as Big Data. Heavier and complex simulations can be done in relative ease by using these languages, in high performance clusters or with multiple processors. Python reads data better than R but both communicated well with Hadoop, giving the users the option of relying on other factors to choose which one to go with.


  5. Great Acceptability: With so many benefits, the languages have gained widespread acclaim and about 2 million users use them worldwide while dealing in data science. Already R has gained widespread acceptability with  Oracle , SAP, Netezza and Teredata have started developing interfaces that uses R as an analytic support.


  6. Statistical innovations: Any new developments of software upgrades always take place in one of these three languages because they are the most evolved and flexible. With new innovations like ff and bigmemory, it is now possible to deal with datasets larger than memory. Python reads data much more efficiently and synchronization with Hadoop is an added bonus.


  7. Ease of Publishing: Since the programming languages integrate well with document publishing, they are the publisher’s favorite. Smooth assimilation with LaTeX documents publishing system as well as the feature of being embedded in word processing documents is a huge plus point. All the languages have pretty large ecosystems, making it easier to publish and handle large volumes of data.


  8. User Friendly: R, Hadoop and Python are user friendly and supports the import of data from Microsoft Excel, Access, MySQL, SQLite and Oracle, allowing any user with any software to function without hindrance. Python has been effectively used for Natural Language Processing and Apache Spark has made the data found in Hadoop clusters all the more easily accessible.


  9. Networking: Community links and networking is a vital part of any global organization and passionate users are always connecting over forms to discuss these languages more than anything else, ensuring a seamless exchange of positive information. The newly launched Anaconda parcel already has more than 300 plus packages that has garnered rave reviews from users worldwide in their forum, egging them on for future packages.


  10. Easy Debugging: Scanning and debugging is easier with these languages than others because most debugging tools are made in compliance with these languages, allowing users to set things right with greater efficiency. Every language has its own pros and cons and yet it can be said that R, Pyhton and Hadoop configurations are the best you can use to keep your systems safe and the best option if you have to go for a complete system overhaul.