The arrival of the programming language R has narrowed the scope of other programming languages since it is widely preferred by most data scientists & researchers and statisticians. R came to view in late 1993 and is a GNU package that empowers statistical computing. Over the past few years, R’s popularity has grown manifold, especially in Data analytics field. It’s an era of data science, and business analytics is a cornerstone of it. Competition is growing like never before and one cannot afford to lose dollars in lieu of using a wrong tool.
With the availability of so many tools, techies, especially beginners can be confused with which programming tool to opt for. If you are miring yourself in finding the best programming language, stay tuned to this post and get to know why R programming is the lynchpin of Data Science Online Training.
Sorting through high-end data science tools will introduce you to the top two tools R or Python. Software engineers with knowledge of math, stats, and Machine Learning prefer Python to any other language, but the problem arises when a developer needs library support for subjects such as Econometrics. Considering the non-technical background of most data science specialists, learning Python is one of the significant challenges for them. Moreover, weaker support of Python for Econometrics which is essential for businesses and finances for communication in form of reports adds one more point to its limitations. In the light of the above points, it is clear that Python is not a reliable solution, and we need to consider the next option-R.
R is used for statistical programming. It supports ML, Stats, and data science libraries to streamline your programming. R and data science share a good relationship that ultimately helps business as the language supports topic-specific packages and, moreover, the infrastructure of its communication is highly specific. Therefore, data scientists show a great interest in R as its libraries support Finance, Econometrics, etc., which makes sense for business analytics.
The birth of R brings along its complexity. As structuring and formality were not the first concern in the beginning of programming, R was considered highly inconsistent to learn. However, the advent of Tidyverse changed the scenario completely. Being a pack of packages and tools, Tidyverse provides you with a consistent structural programming interface. Moreover, dplyr and ggplot2 has reduced learning curve complexities greatly. Today, R has achieved the highest level of consistency, all thanks in part to the evolving nature of R. From visualization to iteration, to manipulation, Vidyverse supports everything, which makes R an easy language to learn. To get in-depth knowledge, enroll for live free demo on Data Science Cetification
What attracts data scientists towards R is its potential for providing business with ready reports and infographics, and ML powered web development. No other language can stand in front R on the grounds of ease and effectiveness. Let’s take an example of RMARKDOWN and shiny. RMARKDOWN is a framework, which enables you to create reconstructable reports to build blogs, presentations, websites, books journals, etc. Organizations embrace this tool not only to prepare a business analytics report but also commercialize what this framework provides them with. Shiny is an R empowered framework that helps you create interactive web applications. It is a handy tool, and companies rely on it to achieve web development goals.
R is a powerful business infrastructure that Excel on Steroids from a business perspective. It is capable of implementing various algorithms, for example, high-end Machine learning package (H2O), TensorFlow deep learning packages, xgboost the top Kaggle algorithm, etc., that is probably not possible for other languages to do. Tidyverse is the backbone of the language R. It structural approach triggers consistency during application development no matter how much complex it is. It encompasses an array of libraries like dpylr, tidyr, stringr, lubridate, forecast, etc., that takes development to the next level.
Having a great community support is highly critical for any Programming language or interface to excel. R has a huge fan following due to tech enthusiasts that learn and provide beginners with the latest updates. The data science field has already acknowledged the importance of R for developing exclusive reports and streamlining communication.
Thus, we can see how R programming language is influencing modern development trends in data science. Complex business operations from a critical decision making to process optimization are achieved through the collected data from the historical data sets. R with the vast number of packages allows us to accomplish advanced computing tasks like regression, classification, and other scientific computations in just a few minutes. These algorithms lead to accurate results for predictions, modeling, pattern analysis, graphical or statistical representation. Maybe these cool features are the main reason behind the popular friendship between R and data science.
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For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.
“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.
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Data types are kept easy.
Data types of R are quite different when we compare with other programming languages. Here, we’ll outline the data types of R.
Integers are numbers without a decimal point. Unlike other programming languages, R represents all integers as a “double” data type. But the main difference is, you need to write “L” to represent integers in arithmetic operations. For example 9L.
Instead of floats, R has a double data type to represent both for making arithmetic calculations simpler. And when you look for integers and floats in programming languages, you’ll see double after writing the function to specify the data type. As an example: 9 and 3,4.
The most basic data type of R that is the lifeblood of its operations because R works with vectorized operations. When we define vectors in R, they’re a sequence of the data element of the same basic time. It only contains the element of the same data type. If not, R will try to convert it into the most dominant data type.
The complex data type is only used to represent complex (imaginary) numbers in R. Unlike most data types, the complex data type is not used commonly in R. We can give a+bi as an example.
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.
IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.
With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.
Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.
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Data Science becomes an important part of today industry. It use for transforming business data into assets that help organizations improve revenue, seize business opportunities, improve customer experience, reduce costs, and more. Data science became the trending course to learn in the industries these days.
Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In online Data science course you learn how Data Science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.
Advantages of Data Science:- In today’s world, data is being generated at an alarming rate in all time lots of data is generated; from the users of social networking site, or from the calls that one makes, or the data which is being generated from different business. Because of that reason the huge amount of data the value of the field of Data Science has many advantages.
Some Of The Advantages Are Mentioned Below:-
Multiple Job Options :- Because of its high demand it provides large number of career opportunities in its various fields like Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.
Business benefits: - By Data Science Online Course you learn how data science helps organizations knowing how and when their products sell well and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.
Highly Paid jobs and career opportunities: - As Data Scientist continues working in that profile and the salaries of different position are grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year as we consider data.
Hiring Benefits:- If you have skills then don’t worry this comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment group.
Also Read: How Data Science Programs Become The Reason Of Your Success
Disadvantages of Data Science: - If there are pros then cons also so here we discuss both pros and cons which make you easy to choose Data Science Course without any doubts. Let’s check some of the disadvantages of Data Science:-
Data Privacy: - As we know Data is used to increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data may be misused against any organization.
Cost:- The tools used for data science and analytics can cost tons to a corporation as a number of the tools are complex and need the people to undergo a knowledge Science training to use them. Also, it’s very difficult to pick the right tools consistent with the circumstances because their selection is predicated on the proper knowledge of the tools also as their accuracy in analyzing the info and extracting information.
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