One of the most common problems that people face when approaching data science is severe lack of a structured path. The right path begins with learning R. There may be a load of good resources that are available for free over the internet, but it is important to know where to start, how to proceed and if at all these free resources are worth the while. All the information available on the internet can actually confuse you. This is why an instructor led training is of utmost importance. They can teach you concepts, syntax, work you through logic, watch you work, evaluate and pass on their industry experience right from the start. Here is a learning path chalked out for aspiring Data Scientists and why they should learn R earlier to getting into serious Data Science.
- R has a growing demand: Let us first understand why you should learn R and how it is useful in your path. R is one of the fastest growing open course contestants to any commercial software packages like SPSS, SAS, STATA. R skills demand in jobs has been on a sharp spike as a lingua franca of everything Data Science.
- Easy to acquire: R is free and can be easily accessed by downloading a copy of it a local machine. This can be done through the CRAN – the Comprehensive R Archive Network. One can pick between binaries for Windows, Linux and Mac. Even though one can consider working with basic R console, they can install complete R through IDE (Integrated Development Environment). RStudio is the most well-known IDE that makes coding easy and fast as it allows one to type multiple lines of code, install, handle plots and maintain packages, navigate programming environment in a way more productive way. Architect is another great alternative to RStudio. Making it easy to acquire, helps you get started on your path to Data Science much easier.
- R packages are very useful for Data Science: The reason for R to become so popular is because of its strong R package ecosystem. The packages that are needed can be easily downloaded from the R Archive network or from bitbucket, bioconductor, github. This will fit the needs of the task at hand. R has a huge community that will get you help anytime you need. There are aggregated R bloggers and forums that will help you with R, help you pick the right codes, especially when it comes to Data Science.
- Manipulation and importing of Data is easy with R: In the Data Science workflow, importing and manipulation of data are crucial steps. R allows for importing of all kinds of data formats with the use of specific packages that can make the job way easier.
- The readxl package is used for getting excel files to R
- To import flat files: readr
- For webscrapping: rvest
- The haven package: importing of STATA, SAS, SPSS data files into R
- For Databases: connection of packages like RpostgreSQL, RMySQL and manipulate and access through DBI.
As soon as data is available within the working environment, you are ready to begin manipulation with use of following packages:
- String manipulation with use of stringr package
- Tidying data with use of tidyr package
- Dplyr package for objects that are like data frames
- Data table package for heavy data wrangling tasks
- Xts, quantmod, zoo for time series analysis.
- Effective Data Visualization: effective data visualization is possible with smart use of R.
Finding an R course close to your location or with convenient timings can be rare. What is even rarer is finding an R course that has Data Science as the context. Learning via videos may not be the best option as you do not have an instructor to guide you personally. An interactive course is always a quicker, most effective way to learn. This is why, IIHT has come up with the blended learning method that reaches out to locations across the world with instructor led training. The R course offered has Data Science as the context, leading one up to advanced courses like Machine Learning and Deep Learning.
