R programming language as a standard tool for machine learning operations, statistics as well as data analysis. Objects, functions, and packages are easily created by R. Also, it’s platform-independent and free. Thus, anyone can install it in any organization for free. Moreover, it can be applied to all operating system.So proper knowledge is needed to work on this technology,which is actually provided in designed way by ACTE R programming Course. 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.
R for Data Science?
The advantages of R in data science and why it proves to be an ideal choice in this space. Here are 6 reasons of choosing R for your next data science project or to just begin your journey in this field:
Academia:
R is a very popular language in academia. Many researchers and scholars use R for experimenting with data science. Many popular books and learning resources on data science use R for statistical analysis as well. Since it is a language preferred by academicians, this creates a large pool of people who have a good working knowledge of R programming. Putting it differently, if many people study R programming in their academic years than this will create a large pool of skilled statisticians who can use this knowledge when the move to the industry. Thus, leading increased traction towards this language.
Data wrangling:
Data wrangling is the process of cleaning messy and complex data sets to enable convenient consumption and further analysis. This is a very important and time taking process in data science. R has an extensive library of tools for database manipulation and wrangling. Some of the popular packages for data manipulation in R include:
- dplyr Package – Created and maintained by Hadley Wickham, dplyr is best known for its data exploration and transformation capabilities and highly adaptive chaining syntax.
- table Package – It allows for faster manipulation of data set with minimum coding. It simplifies data aggregation and drastically reduces the compute time.
- readr Package – ‘readr’ helps in reading various forms of data into R. By not converting characters into factors it performs the task at 10x faster speed.
Data visualization:
Data visualization is the visual representation of data in graphical form. This allows analyzing data from angles which are not clear in unorganized or tabulated data. R has many tools that can help in data visualization, analysis, and representation. The R packages ggplot2 and ggedit for have become the standard plotting packages. While the ggplot2 package is focused on visualizing data, ggedit helps users bridge the gap between making a plot and getting all of those pesky plot aesthetics precisely correct.
Specificity:
R is a language designed especially for statistical analysis and data reconfiguration. All the R libraries focus on making one thing certain – to make data analysis easier, more approachable and detailed. Any new statistical method is first enabled through R libraries. This makes R a perfect choice for data analysis and projection. Members of the R community are very active and supporting and they have a great knowledge of statistics as well as programming. This all gives R a special edge, making it a perfect choice for data science projects.
Machine learning:
At some point in data science, a programmer may need to train the algorithm and bring in automation and learning capabilities to make predictions possible. R provides ample tools to developers to train and evaluate an algorithm and predict future events. Thus, R makes machine learning (a branch of data science) lot more easy and approachable. The list of R packages for machine learning is really extensive. R machine learning packages include MICE (to take care of missing values), rpart & PARTY (for creating data partitions), CARET (for classification and regression training), randomFOREST (for creating decision trees) and much more.
Availability:
R programming language is open source. This makes it highly cost effective for a project of any size. Since it is open source, developments in R happen at a rapid scale and the community of developers is huge. All of this, along with a tremendous amount of learning resources makes R programming a perfect choice to begin learning R programming for data science. Because there are many new developers exploring the landscape of R programming it is easier and cost-effective to recruit or outsource to R developers.
Thus, we have seen that R is worth its popularity and it is going to scale further. R allows practicing a wide variety of statistical and graphical techniques like linear and nonlinear modeling, time-series analysis, classification, classical statistical tests, clustering, etc. R is a highly extensible and easy to learn language. All of this makes R an ideal choice for data science, big data analysis, and machine learning.
