- Course
Manage Invalid, Duplicate, and Missing Data in Python
Cleaning data is one of those tasks that is not fancy, but key to any data application. This course will teach you the skills and knowledge of data cleaning in Pandas needed to convert your datasets from raw and useless to clean and useful.
Beginner
- Course
Manage Invalid, Duplicate, and Missing Data in Python
Cleaning data is one of those tasks that is not fancy, but key to any data application. This course will teach you the skills and knowledge of data cleaning in Pandas needed to convert your datasets from raw and useless to clean and useful.
Beginner
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This course is included in the libraries shown below:
- Data
What you'll learn
Regardless of your line of work; data is everywhere. Today, we generate more data per second than ever before; however, this data is usually raw, dirty, and frequently unusable.
In this course, Manage Invalid, Duplicate, and Missing Data in Python, you’ll gain the ability to clean your data to make it usable for any application you may need.
First, you’ll explore how to handle missing values and how to fill NaN columns.
Next, you’ll discover how to deal with duplicate rows on a subset of columns.
Finally, you’ll learn how to cope with invalid values and how to fix or remove them.
When you’re finished with this course, you’ll have the skills and knowledge of data cleaning in Pandas needed to convert your datasets from raw and useless to clean and useful.
Manage Invalid, Duplicate, and Missing Data in Python
Beginner
Table of contents
Axel Sirota has a Masters degree in Mathematics with a deep interest in Deep Learning and Machine Learning Operations. After researching in Probability, Statistics and Machine Learning optimization, he is currently working at JAMPP as a Machine Learning Research Engineer leveraging customer data for making accurate predictions at Real Time Bidding.