Data Analysis with R

Data wrangling, or data pre-processing, is an essential first step to achieving accurate and complete analysis of your data. This process transforms your raw data into a format that can be easily categorized or mapped to other data, creating predictable relationships between them, and making it easier to build the models you need to answer questions about your data. This module provides an introduction to data pre-processing in R and then provides you with the tools you need to identify and handle missing values in your dataset, transform data formats to align them with other data you may want to compare them to, normalize your data, create categories of information through data binning, and convert categorical variables into quantitative values that can then be used in numeric-based analyses.

Exploratory data analysis, or EDA, is an approach to analyzing data that summarizes its main characteristics and helps you gain a better understanding of the dataset, uncover relationships between different variables, and extract important variables for the problem you are trying to solve. The main question you are trying to answer in this module is: "What causes flight delays?" In this module, you’ll learn some useful exploratory data analysis techniques that will help answer this question.

You have identified the problem that you’re trying to solve and have pre-processed the dataset you’ll use in your analysis, and you have conducted some exploratory data analysis to answer some of your initial questions. Now, it’s time to develop your model and assess the strength of your assumptions. In this module, you will examine model development by trying to predict the arrival delay of a flight using the Airline dataset. You’ll learn regression techniques for determining the correlation between variables in your dataset, and evaluate the result both visually and through the calculation of metrics.

You have a firm understanding of your data and have pre-processed it to ensure the best possible outcomes. And you have conducted exploratory data analysis and developed your model. Everything looks good so far, but how can you be certain your model works in the real world and performs optimally? In this module, you’ll learn how to use the tidymodels framework to evaluate your model. Tidymodels is a collection of packages for modeling and machine learning using tidyverse principles. Using these packages, you’ll learn how to cross-validate your models, identify potential problems, like overfitting and underfitting, and handle overfitting problems using a technique called regularization. You’ll also learn how to tune your models using grid search.