The video tutorial covers the importance of data cleansing in data engineering, using examples from wind farms to illustrate the impact of bad data. It explains how to identify and remove bad data using Numpy and Pandas, including handling not-a-number values and outliers. The tutorial also demonstrates advanced filtering techniques and concludes with an overview of the next steps in data engineering, focusing on feature engineering.

Why is data cleansing considered important in data engineering?

What are some potential consequences of having bad data in a wind farm's data collection?

Describe the process of data assessment mentioned in the text.

What types of data might be considered bad data that need to be removed?

Explain the importance of removing outliers during the data cleansing process.

How can Numpy and Pandas be used to filter out bad data?

What are some methods to handle missing values in a dataset?

What is the difference between dropping rows with bad values and filling them with a mean value?

How can you filter data based on multiple conditions using Pandas?

What is the next topic to be covered after data cleansing in the course?