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In this module we discuss ethical considerations related to data collection, privacy, and confidentiality.

In this module we introduce best practices for effective and reliable data collection.

In this module we summarize the key concepts and principles of data collection for future reference.

This module runs through an overview of what data preprocessing is, why you should consider data preprocessing, and understand the three steps of data preprocessing.
 

This module focuses on a case study of data preprocessing using the 2019 FIFA dataset to comprehend the process of data preprocessing using hands-on sessions. You will go through loading libraries and loading and exploring the data.
 

This module continues with the case study and provides a hands-on session on a basic summary of statistics like mean, median, etc., and their consequences.
 

This module walks you through the basics of feature engineering. You will go through a hands-on session on combining a few more statistics to reduce the dimension and splitting the work rate into two columns.
 

Through the case study, you will learn about standardizing continuous features. You will go through a hands-on session explaining how standard deviation plays its role and comprehend Z and T transformations.
 

This module focuses on log transformation. You will gain hands-on knowledge of how various functions are used for various transformations and how they make a difference.
 

This module focuses on missing values. There are many ways of handling missing values, but here you will start by understanding the pattern in the missing values and understand it through hands-on code demonstration.

This module gives you hands-on experience in implementing binning and lambda functions. You will understand how the bin function aids continuous features and go through the implementation of the cut function, changing units and making categorical into categorical types.
 

This module contains a hands-on session on correlation checks for bivariate data. Through the scatterplot implemented, you will see the representation of the bivariate data. 
 

This module contains a hands-on session focusing on handling outliers. This will help you understand how to replace or adjust the values of extreme outliers in a dataset. In return, it will help you make the data more accurate and prevent outliers from skewing results.
 

This module contains a hands-on session focusing on handling outliers. This will help you understand how to replace or adjust the values of extreme outliers in a dataset. In return, it will help you make the data more accurate and prevent outliers from skewing results.

This module helps you understand text processing in-depth through the implementation of various scenarios through the hands-on demonstration.
 

This module gives you an overview of encoding categorical models and helps you comprehend the process of transforming categorical data into numerical data so that machine learning algorithms can interpret the data and make predictions. You will understand the concept better through the dummy variable encoding technique hands-on implementation.

This module contains a hands-on session on processing columns to get a numeric data frame that can be ready for any modeling tasks.