
Read data from HTML tables on Wikipedia pages by specifying the export details and reading the targeted tables, such as Olympic medal tables and World Heritage lists.
Apply filter to the sleep dataset to retain mammals with body weight above a threshold and conservation status endangered, then combine criteria with and or logic.
Group olympic data by sport to compute mean age and height, explore height–weight correlations, and master arranging and per-group operations with do and summarise in r using tidyr and dplyr.
Learn to use dplyr for data summarizing on air quality data, selecting ozone and month, filtering, mutating, and summarizing with group_by to compute mean values by month.
Introduce tidy worth and tidy worthy, the current system of packages for data manipulation, exploration, and visualization, and use the pipe operator with tables as a modern data-frame approach.
revisit long and wide data formats using tidyr and dplyr, convert air quality data from wide to long with gather, summarize by month, and reshape back to wide with spread.
Remove missing values in R using dplyr and tidyr with tidyverse workflows, employing distinct, drop, and drop_na, guided by practical examples on the mammal sleep data.
Learn to perform data imputation with dplyr by replacing missing Sleep REM values with the column mean, using mutate and pipes on the mammal sleep data.
Learn to create an intuitive CPI visualization in R by using dplyr and tidyr to gather and reshape data, then plot top and bottom 15 countries in 2016 with ggplot2.
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..
The course will mostly focus on helping you implement different techniques on real-life data such as Olympic medal winners
After each video, you will learn a new concept or technique which you may apply to your own projects immediately! Reinforce your knowledge through practical quizzes and assignments.
ON TOP OF THE COURSE, I’M ALSO OFFERING YOU:
Practice Activities To Reinforce Your Learning
My Continuous Support To Make Sure You Gain Complete Understanding & Proficiency
Access To Future Course Updates Free Of Charge
I’ll Even Go The Extra Mile & Cover Any Topics That Are Related To The Subject That You Need Help With (This is something you can’t get anywhere else).
& Access To A Community Of 25,000 Data Scientists (& growing) All Learning Together & Helping Each Other!
Now, go ahead & enrol in the course. I’m certain you’ll love it, but in case you don’t, you can always request a refund within 30 days. No hard feelings whatsoever. I look forward to seeing you inside!