Data science with R: tidyverse

Data Science skills are still one of the most in–demand skills on the job market today. Many people see only the fun part of data science, tasks like:  search for data insight , eveal the hidden truth behind the data , build predictive models , apply machine learning algorithms , and so on. The reality, which is known to most data scientists, is, that when you deal with real data, the most time–consuming operations of any data science project are: data importing , data cleaning , data wrangling , data exploring and so on. So it is necessary to have an adequate tool for addressing given data–related tasks. What if I say, there is a freely accessible tool, that falls into the provided description above!

R is one of the most in–demand programming languages when it comes to applied statistics, data science, data exploration, etc. If you combine R with R’s collection of libraries called tidyverse, you get one of the deadliest tools, which was designed for data science–related tasks. All tidyverse libraries share a unique philosophy, grammar, and data types. Therefore libraries can be used side by side, and enable you to write efficient and more optimized R code, which will help you finish projects faster.