Tidy Data: Updated Data Processing With tidyr and dplyr in R

THIS IS YOUR ROADMAP TO LEARNING & BECOMING HIGHLY PROFICIENT IN DATA PREPROCESSING, DATA WRANGLING, & DATA VISUALIZATION USING TWO OF THE MOST IN-DEMAND R DATA SCIENCE PACKAGES. Hello, My name is Minerva Singh. I am an Oxford University MPhil graduate in Geography & Environment & I finished a PhD at Cambridge University in Tropical Ecology & Conservation.

I have +5 of experience in analysing real-life data from different sources using statistical modelling and producing publications for international peer-reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you're going to love this course. I created this course to take you by hand and teach you all the concepts, and tackle the most fundamental building block on practical data science - data wrangling and visualisation.

THIS COURSE WILL TEACH YOU ALL YOU NEED AND PUT YOUR KNOWLEDGE TO PRACTICE NOW. This course is your sure-fire way of acquiring the knowledge and statistical data analysis wrangling and visualisation skills that I acquired from the rigorous training I received at 2 of the best universities in the world, the perusal of numerous books and publishing statistically rich papers in the renowned international journal like PLOS One. HERE IS WHAT THIS COURSE WILL DO FOR YOU:It will take you (even if you have no prior statistical modelling/analysis background) from a basic level of performing some of the most common data wrangling tasks in R- with two of the most happening R data science packages tidyverse and dplyr.

It will equip you to use some of the most important R data wrangling and visualisation packages such as dplyr and ggplot2. It will Introduce some of the most important data visualisation concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation. You will also be able to decide which wrangling and visualisation techniques are best suited to answer your research questions and applicable to your data and interpret the results.