Developed in 1990s by Ross Ihaka and Robert Gentleman, R is a language to be used for statistical computations, graphical representation of data and data analysis. According to 2107 Burtch Works Survey, out of all surveyed data scientist, 40% prefer R, 34% prefer SAS and 26% Python. Unarguably, it comes handy in case of machine learning and numeric analysis, and with that, here we enumerate some of the distinct advantages of R programming, which data scientists should know of. DexLab Analytics offers excellent R programming certification. Get the details. Data scientists very much prefer R programming owing to its ease of use and simple nature. Over a matter of time, R programming has not only gained grounds on matters of speed and efficiency as a programming language, it strived to be the best in offering seamless data analysis, which can be reproducible. In terms of work, it gives enough flexibility and power to the users. Especially, users who tackle big data. While working on R, statisticians derive a lot more in terms of built-in functionality. R is extensible and comes with an enriching set of functions that paves way for develops to construct their own set of tools for data analysis. To continue reading, click here - https://www.dexlabanalytics.com/blog/why-prefer-r-programming It’s important to get a hands-on R language training, if you want to excel on R. And for that, DexLab Analytics is here to satiate your every data need. For more information, visit the site. Interested in a career in Data Analyst? To learn more about Machine Learning Using Python and Spark To learn more about Data Analyst with Advanced excel course To learn more about Data Analyst with SAS Course To learn more about Data Analyst with R Course To learn more about Big Data Course
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Researchers at the University of California, Berkeley have developed an “intrinsic curiosity model” to make the learning algorithm of machines function even when there is a deficit in strong feedback signal. The model developed by this team visions the AI software controlling a virtual agent in video games to maximize its understanding of the environment and factors influencing the environment. Preceding researches have seen numerous attempts to provide AI agents’ curiosity, but this time the trick has proved rewarding.
This trick aims to solve the drawbacks in techniques pertaining to core machine learning and may even help us by making machines better equipped at solving obscure real world problems.
Among the numerous machine learning techniques developed in the past decade, Reinforcement Learning has brought an exceptional change in the manner in which machines accomplish their tasks. As a result this technique is now employed to instill machines with skills that might be impossible to code manually. However, Reinforcement Learning comes with limitations. As pointed out by a PhD student of UC Berkeley, it requires huge amount of training to be able to learn a task. Furthermore the program doesn’t work for computer games in which the benefits of certain behaviors aren’t immediately obvious. That’s where curiosity could come in.
Rigorous research on artificial curiosity is being carried out for quite some time now. What’s interesting is that these ideas are transitioning from “exotic’’ to ‘’mainstream’’ in both AI and neuroscience. To learn more about the current advances in the field of Artificial Intelligence follow us on Dexlab Analytics. We are a premier institute providing Machine Learning Certification in Gurgaon. Interested in a career in Data Analyst? To learn more about Machine Learning Using Python and Spark To learn more about Data Analyst with Advanced excel course To learn more about Data Analyst with SAS Course To learn more about Data Analyst with R Course To learn more about Big Data Course There are a number of choice debates raging in the internet lately, be it the choice between Samsung vs. Apple vs. HTC or the war between iOS vs. Android vs. Windows in the mobile Operating System to comparing candidates for upcoming elections and even selection of the captain of a football team. It seems discussions or comparisons keep our lives enriched. All you need to do if you love discussions is to pop up a relevant question among a passionate community and then watch as it explodes. And the best thing about such an event is that everyone in the room will walk away as a more knowledgeable person about those topics.
Here in this blog post we are lighting a match at sparking something similar, the debate between SAS vs. R vs. Python, in terms of which is the software that one must learn first. This has perhaps been the biggest debate in the whole industry. Read Also: The Dilemma of Data Science: SAS or R or Python We know what you might be thinking, hasn’t there already been a lot said about this topic? Yes, it probably has, but there is still a need for us to discuss these topics for the following reasons:
To be at the top of one’s game in the corporate world, especially related to technologies like IT, requires people to keep themselves updated with the latest news and developments within the relevant industry. But simply reading a few articles a day will not make it for you as a data scientist in these competitive times. Knowledge is the universal currency we deal in so, it is important that you share knowledge so, that everyone else has access to it. The best lesson to learn is that wisdom amplifies with distribution. And together with the sharing of knowledge we can create a smarter, more aware world for the better of future generations. Perhaps best put as Sherlock said recently, this world is nothing but intersecting crossovers of human interventions as a web connects us all, if we could interpret all the data then the whole of the future would come to us like a screening of a hijinks-replete mad movie! While we might have edited some of the great detectives original thoughts, but wouldn’t that be fun to know whatever danger is t come our way and be prepared?! Here’s how you can arm yourself with the knowledge of analytics and predictions backed by data with this year’s must read books!
Let us know which ones you liked the best or feel free to add in your own top favourites in the comments... The ability of analytics programming has developed altogether throughout the years, as recorded by the advanced excel VBA institute in Gurgaon. It is useful to have the capacity to contrive the development of every software package’s ability, yet such information is difficult to get. A number’s diagram of R packages on CRAN, discharged in every year, from 2002 to 2014, demonstrates a development curve that takes after a fast explanatory curve. To put this astounding development in context, let us contrast it with SAS, the most prevailing business package. SAS includes roughly 1,200 commands that are generally proportional to R functions (capacities, procs and so on in Stat, Base, HP Forecasting, ETS, Graph, Macro, IML, QC, OR). R included 1,357 bundles in 2014, together with just CRAN, or around 27,642 functions. Only during 2014, R included more capacities/procs than SAS Institute has written in its whole history. Obviously R commands and SAS take care of huge numbers of the same issues; they are absolutely not splendidly proportional. A few SAS strategies have numerous choices to organize their productivity than R functions would carry out, therefore one SAS strategy may be equal to numerous R functions. Then again, R functions can settle inside each other, making almost endless combinations. Whilst the correlation is a long way from flawless, it gives a fascinating point of view on the size and development rate of R. As fast as development of R has been, this information speaks to just the principle CRAN store. R has 8 other programming stores, for example, Bioconductor. A system, kept running on 5/22/2015, tallied 8,954 R bundles at all significant archives, of which 6,663 were at CRAN. Thus the development bend for the product at all stores would be roughly 34.4% higher on the y-hub. than the one appeared in the Figure. In this manner, for 2014 the evaluated absolute development in R functions was 37981 or 28,260 * 1.344. As per the demonstration of the R documentation site, the most recent tallies of both capacities and packages on CRAN, GitHub and Bioconductor. They demonstrate that there is a normal of 20.37 functions for each package. Since a project keeps running on 5/22/2015 checked 8,954 R packages at all significant vaults with the exception of GitHub, on that date there were around 182,393 aggregate functions in R. Altogether, R has more number of commands than SAS. |
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