R programming is a great tool to explore and investigate the data. Elaborate analysis like clustering, correlation, and data reduction are done with R. This is the most crucial part, without a good feature engineering and model, the deployment of the machine learning will not give meaningful results. Start learning with us ACTE R Programming Classroom & Online Training Course.
R Programming is very useful for career.Careers in R programming are associated with the data science and business analytics profession. ... R programmers are a good fit for the research-oriented industry for statistical model implementation for data analysis. professionals want to upgrade their career in data science R programming is a preferred choice.
R Programming, have great scope, Scope is high Basically, R is now considered as the most popular analytic tool.R Careers offers bright jobs for any data scientist he may be any fresher or experienced. Organizations expect many of the new hires with knowledge of R and they want them to be familiar with the R tool.
Even as a fresher, you can get a job in R Programming domain. R is the name of a popular programming language that has become the tool of choice for data scientists and statisticians around the world. Companies are using analytics to predict things like pricing of their products, how much to spend on ads, whether a drug will turn out to be successful or not etc. and R is helping them analyse historical data to make these predictions.
The business analytics field has been dominated by paid tools such as SAS, Statistica and SPSS (IBM). Even though some of these tools can be very expensive (with software licenses running into millions of dollars), the value coming out of their application is far more and hence companies did not mind spending so much.
We are happy and proud to say that we have strong relationship with over 700+ small, mid-sized and MNCs. Many of these companies have openings for R Programming. Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.
Coin package in R provides various options for re-randomization and permutations based on statistical tests. When test assumptions cannot be met then this package serves as the best alternative to classical methods as it does not assume random sampling from well-defined populations.
- Knowledge of statistics theory in mathematics.
- You should have solid understanding of statistics in mathematics.
- Understanding of various type of graphs for data representation.
- Prior knowledge of any programming.
Learn R. Can someone with no programming knowledge learn “R”? The answer is yes! ... Despite not having any previous programming experience , I analyzed my first data set of more than 20,000 data points in only a couple of months.
Our course ware is designed to give a hands-on approach to the students in R Programming. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
Yes Definitely! From my point of view learning, R language has a worth to learn. R is the best programming language to perform analytical operation. The number of applications such as healthcare, finance, media use R programming to analyze their data.
Advanced R Programming takes around 1 month to master to a level so that you can start writing analytics functions.
If you have experience in any programming language, it takes 7 days to learn R programming spending at least 3 hours a day. If you are a beginner, it takes 3 weeks to learn R programming. In the second week, learn concepts like how to create, append, subset datasets, lists, join.
- Backed by a Huge, Active Community.
- Comprehensive Library Support.
- Cross-Platform Compatibility.
- Data Visualization at its Best.
- Develop Interactive, Powerful Web Apps With Shiny.
- Go-to Option for Statistical Analysis and Data Science.
- High Market Demand With High-Paying Roles.
- Major Companies Trust R.
Features of R programming
1. Comprehensive Language
R is a comprehensive programming language, meaning that it provides services for statistical modeling as well as for software development. R is the primary language for Data Science as well as for developing web applications through its robust package RShiny. R is also an object-oriented programming language which is an addition to its procedure programming feature.
2. Provides a Wide Array of Packages
R is most widely used because of its wide availability of libraries. R has CRAN, which is a repository holding more than 10,0000 packages. These packages appeal to every functionality and different fields that deal with data. Based on user requirements and preferences, these packages provide different features to their users.
3. Possesses a Number of Graphical Libraries
The most important feature of R that sets it apart from other programming languages of data science is its massive collection of graphical libraries like ggplot2, plotly, etc. that are capable of making aesthetic and quality visualizations.
4. Open-source
R is an open-source programming language. This means that it is free of cost and requires no license. Furthermore, you can contribute towards the development of R, customize its packages and add more features.
5. Cross-Platform Compatibility
R supports cross-platform compatibility. It can be run on any OS in any software environment. It can also be run on any hardware configuration without any extra workarounds.
6. Facilities for Various Industries
Almost every industry that makes use of data, utilizes the R language. While only the academic areas made use of R in the past, it is now being heavily used in industries that require to mine insights from the data.
The health industry makes use of R for drug design and analyzing genomic strands. Manufacturing industries like Ford use it in their optimization procedures. Furthermore, Airbnb and social media companies like Twitter use it to analyze its users.
7. No Need for a Compiler
R language is interpreted instead of compiled. Therefore, it does not need a compiler to compile code into an executable program. The R code is interpreted one step at a time and directly converted into machine level calls. This makes running an R script much less time-consuming.
8. Performs Fast Calculations
Through R, you can perform a wide variety of complex operations on vectors, arrays, data frames and other data objects of varying sizes. Furthermore, all these operations operate at a lightning speed. It provides various suites of operators to perform these miscellaneous calculations.
9. Can Handle all Sorts of Data
R provides excellent data handling and storage facilities.In conjunction with data platforms like Hadoop, R facilitates the handling of structured as well as unstructured data that imparts a comprehensive data capability. Furthermore, R provides various data modeling and data operation facilities that are often a result of active interaction with the storage facility. It also provides extensions for SQL and Big Data.
10. Integration with Other Technologies
R can be integrated with a number of different technologies, frameworks software packages, and programming languages. It can be paired with Hadoop to use its distributed computing ability. It can also be integrated with programs in other programming languages like C, C++, Java, Python, and FORTRAN.
11. R has an Active Community
R is continuously evolving. The contribution is fuelled by the growing number of users who are using R on a daily basis. As mentioned above, R is an open-source library that is supported and maintained by a large user-base. Not only this, R has an engaging community that organizes seminars, boot camps and other training sessions of R. Once you start your journey in R, you will never feel alone.
12. Machine Learning with R
Earlier R had different packages for different.This may be considered inefficient and, therefore, the MLR package which stands for Machine Learning in R has become highly popular. This package is useful for all machine learning algorithms and provides other tools that help with machine learning as well.
