By Minerva Singh
Learn data preprocessing, data wrangling, and data visualization for hands-on data science and data analytics applications in R
- Perform some of the most common data wrangling tasks and important data visualization concepts in R at a basic level
- Make use of some of the most important R data wrangling and visualization packages such as dplyr and ggplot2
- Solve wrangling and visualization techniques best suited to answering your research questions and applicable to your data
What You Will Learn
- Read in data into the R environment from different sources
- Carry out basic data pre-processing & wrangling in R Studio
- Learn to identify which visualizations should be used in any given situation
- Build powerful visualizations and graphs from real data
- Apply data visualization concepts to practical data analysis & interpretation
This course is a sure-fire way to acquire the knowledge and statistical data analysis wrangling and visualization skills you need.
HERE IS WHAT THIS COURSE WILL DO FOR YOU:
• It will introduce some of the most important data visualization concepts to you in a practical manner so that you can apply these concepts to practical data analysis and interpretation.
• You will also be able to decide which wrangling and visualization techniques are best suited to answering your research questions and applicable to your data, and you’ll interpret the results.
• The course will mostly focus on helping you implement different techniques on real-life data such as Olympic and Nobel Prize winners
• After each video, you will learn a new concept or technique which you can apply to your own projects immediately! You’ll reinforce your knowledge through practical quizzes and assignments
All the code and supporting files for this course are available at – https://github.com/PacktPublishing/Complete-Data-Wrangling-and-Data-Visualization-In-R-
About the Author(s)
- Minerva Singh
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.