Task: Preprocessing for Tabular Data

Type of course:

Digital learning, Lesson

Language:

Duration:

15 minutes

Proficiency:

Intermediate

Target:

Manager, Professionals, Workers

SUMMARY

Have you ever encountered messy, inconsistent data and wondered how to make it analysis-ready? This lesson focuses on the crucial steps that come after Exploratory Data Analysis (EDA): Data Preprocessing. Proper preprocessing is the foundation for accurate and reliable insights, making it an essential skill for supply chain professionals and data enthusiasts alike.

In this lesson, you will apply the preprocessing techniques that set up your data for success on a real tabular dataset.

About The Author

Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using IoT and ML techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.

Learning outcomes

By the end of this lesson, learners will be able to identify the preprocessing steps needed based on EDA results
By the end of this lesson, learners will be able to handle common tabular data issues such as missing values, outliers, and skewness.
By the end of this lesson, learners will be able to define and explain scaling and normalization techniques to standardize data.