Data Academy Subscription > Data with R > Common Data Pre-Processing Techniques in R
Course Description
Data pre-processing involves transforming data into a basic form that makes it easy to work with. In this course, you will learn common data pre-processing techniques to deal with missing data and to clean, manipulate, or summarize data. You will use reshape2 to transform data between long and wide formats, pipe operators to chain functions together; and Tidyverse to manipulate and explore data.
Requirements
  • This course assumes knowledge of basic statistical terminology.
Instructor:
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.