TIM-6500: Principles of Data Science

Lee, W. (2019). Python machine learning. ProQuest Ebook Central.
Read Chapter 5, Getting Started with Scikit-learn for Machine Learning, Normalizing Columns (pp. 112-117)
Chapter 5 (specifically these pages) discusses using the Scikit-learn library to remove outliers from data and normalize columns to a standard scale.

Rençberoğlu, E. (2019). Fundamental techniques of feature engineering for machine learning.
This website demonstrates Pandas library methods for data preprocessing.

Odegua, R. (2018). Exploratory data analysis, feature engineering, and modeling using supermarket sales data-part 1.
This article is part 1 of 2 and shows how to engage in exploratory data analysis to determine what features are relevant to a machine learning algorithm.

Odegua, R. (2019). Exploratory data analysis, feature engineering, and modeling using supermarket sales data-part 2.
This article is part 2 of 2 and shows how to use one-hot encoding to create new features that handle categorical data relevant to a machine learning algorithm.

Sucky, R. N. (2020). Understand the data with univariate and multivariate charts and plots in python.
This article demonstrates the univariate and bivariate analysis process.