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Choose R for excelling in Data science

Things like how much time you are going to spend in coding or how far can you scale your work will depend on this particular decision. And more than two million data scientists chose R with great satisfaction!

If you are currently seeking a career in data science then choosing the right set of skills is extremely essential for success.

While there are a number of tools and software platforms that you must master to excel in data science, the most important decision for you would be choosing the best programming language for the various statistical computations and data analysis tasks you will be required to undertake.

Things like how much time you are going to spend in coding or how far can you scale your work will depend on this particular decision. And more than two million data scientists chose R with great satisfaction!

Why is R preferred by so many data scientists?

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R has a number of advantages:

• R is not only a high-level object-orientedstatistical programming language but also a powerful data analysis software platform, provides the best environment for statistical analysis and one of the leading tools for Machine learning.

• While many argue that python is easier to learn and use than R, it is important to remember that R is a special purpose programming language to deal with special purpose problems.

• It also supports interaction with multiple databases and data wrangling is fairly easy in R as R possesses a number of packages. For instance, Dplyr is an R package utilized by data scientists for data organization and data wrangling. Tidyr is another popular R package which allows data professionals to clean data efficiently. And yet another R package is ggplot2 which is adored by the data science world for its data visualization capabilities.

• R machine learning packages like MICE, rpart& PARTY, CARET and randomFOREST are very powerful and are loved by data scientists.

• Being an open-source software package, R can be utilized by anyone for any purpose and thus, it has a great support community across the globe.

• R is very much reliable for a variety of data science tasks for which it is being utilized by many leading organizations. The Bank of America uses R for data visualization, Facebook utilizes R packages to explore data and Twitter uses R for monitoring user experience.

Is R training beneficial in India?

The analytics industry of India is growing at a rapid pace and is expected to reach 6billion USD within 2025. Given the progress of digital transformation of India and the increasing implementation of data driven strategies by companies in India, no doubt the field of data science and analytics has emerged a top source for lucrative employments.

However, India suffers from an acute talent shortage and many high paying positions remain unfulfilled due to lack of skills in Indian engineers. R being one the best for data science, with an R training course you can easily land on a lucrative job in India.



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Needless to say, Industrial revolution started way back in the 18th century which gave rise to engine-driven processes and a host of automated methods. However, it has been more than 200 years and we finally have the newest era of industrial revolution to account for i.e. Industry 4.0 and a host of Big Data Challenges to mitigate, alongside the same. That said, before understanding the role of Big Data in the newest industrial landscape, we need to come to terms with the Industry 4.0 and what all can be expect from the recent turn of events.

Up Close and Personal with Industry 4.0

The most obvious fact is that Industry 4.0 is all about achieving higher levels of automation by making way for networked systems, real-time IT components, and close proximity with AI algorithms and Machine Learning principles. Therefore, Big Data plays a pivotal role when it comes to deciphering Industry 4.0 and the technical components pertaining to the same.

Why Big Data is Necessary at this Juncture?

Industry 4.0 is and will be generating massive sets of data which need to be processed in a desirable manner. Therefore, if the diverse data reserves aren’t tapped into, industries will lose out on a lot of actionable information which would surely impact productivity, quality and other aspects, in a negative manner. Proper implementation of Big Data principles make sure that all the pain points are taken care of. However, Industry 4.0 doesn’t come without its set of implementation and extraction challenges which concern Big Data in general.

Big Data Challenges for Industry 4.0

The biggest challenge for industries synonymous to the 4.0 era is to keep a track of the diverse sources and data reserves, from which the insights are to be procured. The challenges therefore include:

Logistics data courtesy of the third-party sources

Concrete customer feedback and usage data

Fault- detection results

Data from Manufacturing execution systems

Process and product quality data sets

Machine and operational data from control systems

Threshold specifications

The trickiest aspect here is that while some data sources are structured in nature there are quite a few that are unstructured. This means leveraging data is difficult due to the poor levels of component interoperability, lack of compatibility, and even the inability of the existing IT systems to manipulate and store data based on the higher processing velocity.

Big Data for Industry 4.0: The Vision

The best way to mitigate the existing Big Data Challenges in the current industry 4.0 scenario is to achieve accurate business intelligence by analyzing, collecting, and even sharing data across diverse functional domains. The only approach that would help is to respond and adapt depending on the transforming business requirements.

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From a more technical purview, it is all about data collection followed by the inclusion of streaming analytics. Once, the mentioned strategies are taken care of, the process inches towards Big Data Storage, via databases and frameworks which are then followed by the inclusion of batch analytics and even data integration across diverse enterprises or ecosystems. The only way the challenges are mitigated for specific businesses, is if the streaming and analytics output are distributed strategically as actionable information for optimizing the manufacturing process.

In simpler words, companies which have a specific working module in place must look to interact with the audiences online and must try to gauge the customer behavioral data via clicks and forms. One such example is the company offering Wine Cooler Reviews to the customers and understanding the popularity and interaction levels based on the knowledge sets on offer. Therefore, one approach for mitigating the incompatibility and poor interoperability issue is by taking the enterprise online.

Use Cases

The most heartening aspect of Industry 4.0 is that it can be applied across a wide-range of sectors, including defense, aerospace, electronics, and industrial manufacturing. Moreover, if reports are to be believed, by 2020 we can expect a cumulative savings of $421 billion if implementations are handled using Big Data analytics.

Therefore, the most relevant use cases synonymous to the actual Big Data vision for the Industry 4.0 include:

Empowered Customers

This aspect gets the most amount of recognition as it would be possible for the companies to stay connected with the consumers across diverse verticals.

Reduced Downtime

Improved Big Data Analytics in regard to Industry 4.0 makes sure that failures and patterns are gauged in advance which would then minimize the unplanned and unwanted downtime.

Inference

The modern era is best known for the proliferation of sensors and IoT devices which are paving way for advancements and the newer version of Industrial Revolution. However, at present there are certain Big Data challenges which are making it difficult for companies to put all the analytics in play. Once the actual Big Data vision is put in place and the preferred solutions are put to use, it would become easier to integrate diverse processes seamless across diverse verticals, precisely for achiving required business objectives. 

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