What Topics Should a Good R Analytics Course Cover
R analytics is an open source programming language for statistical computing and analysis of data. Open source meaning the language is free to use by anyone. Free to use as is, free to develop it further and free to share. Many developers have used R programming language over the years and developed it extensively making it a very powerful language with large libraries to perform frequently used complex calculations.
There is a very high demand for people who have a good grasp of R analytics in the job market at present. Statisticians and data miners use R language extensively for data analysis and in the development of statistical software. If you are looking for a robust career which pays attractively then you can learn R analytics course to boost your career prospects. A good option is to learn R analytics course online so that you can utilize your time effectively.
R analytics is not just a mathematical and statistical computation software. It has a wide range of applications and it is still evolving. R language can even implement machine learning algorithms quickly and in a very simple way. So, a good R analytics course should be constantly updated and cover the following important topics:
Installation and familiarization with R or R studio
R is an older version or a development kit for R whereas R studio provides a better and advanced development kit for R language. A good course should cover how to install the R studio and familiarize you with the various important areas like the R console, the R graphical output area, the R script box and R environment.
Installation and familiarization with R packages.
R packages are the libraries which contain programs or functions to perform many common as well as complex computations or other tasks. The R packages are the fuel that powers R language. Your course should teach you how to install the R package into your R studio, how to access the packages and make use of the functions in the packages to carry out the desired task.
The basics of R programming
Once you have learned about installing the R studio and R packages it is time to learn the basics of R language. R language is an object-oriented programming language which means every variable in R programming is treated as an object. If you are familiar with any programming language and particularly with any object-oriented programming language then understanding R programming language will be very easy for you. Even if you are not familiar with any programming language there is nothing to worry as R language is quite easy to learn but knowledge of algebra and statistics is mandatory for learning R analytics and if you are not familiar with it then you will have to learn algebra and statistics first.
Exploratory Data Analysis in R
You will need to learn how to understand the given data and plot it in a graphical format. Once you understand how to read the data and explore it then you will be able to predict the behavior of the data. This part requires in-depth knowledge of statistics for plotting of graphs and knowing which model to use. R language has certain packages that will make it easier to analyze the existing data, explore it and build predictive models. Make sure that the online course on R analytics you choose contains plotting of data using the various statistical models. It would be an added bonus if the R analytics course also covered a few statistical basics which are used often in R analytics.
Data Manipulation
Data manipulation is a very important feature of R analytics and can also be called as the advanced level of data exploration. Along with learning how to analyze the given data the ability imagine the given data in statistical models and predicting the future behavior is called predictive modeling in R.
A very important model used in data manipulation is feature engineering. Feature engineering can create statistical models based on given real-time data or modify an existing model as and when the real-time input is provided. This feature is a boon in many industries that deal with real-time data and decisions have to be taken quickly by analyzing the data. The ability to modify future predictions as and when new data is given, helps in informed decision making and is a skill which is very much sought after. Therefore ensure that your R analytics course has a data manipulation as part of its curriculum.
Machine learning for predictive modeling
Predictive modeling using Machine learning can be achieved using certain algorithms like Random Forest, Regression, and Decision Tree. Understanding and usage of these algorithms should form a part of the course curriculum along with the hands-on practical applications of the algorithms in predictive modeling.
The latest R language development environment is Jupyter Notebooks and if your course can cover Jupyter Notebooks then it will surely be an added advantage to you as many will be comfortable in using only R Studio for developing programs in R. If you know how to install and work on Jupyter Notebooks then you will be more employable.
White knight ventures offers an R analytics course which has been designed by experts in the field of R analytics wherein you can learn R analytics course online without having to pay a hefty fee for classroom training.
