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Data Cleaning in Python Essential Training [Updated: 10/10/2025]
![Data Cleaning in Python Essential Training [Updated: 10/10/2025]](https://pixhost.icu/avaxhome/b9/b93b/b93b52ec7d7c4dbf9d6ef3409c748f23-6974212247218570175.webp)
Data Cleaning in Python Essential Training [Updated: 10/10/2025]
.MP4, AVC, 1152x720, 30 fps | English, AAC, 2 Ch | 1h | 167 MB
Instructor: Miki Tebeka
.MP4, AVC, 1152x720, 30 fps | English, AAC, 2 Ch | 1h | 167 MB
Instructor: Miki Tebeka
If you’re looking for more efficient ways to prepare your data for analysis, it’s time to level up your skill set and reassess your approach to data cleaning. In this course, instructor Miki Tebeka shows you some of the most important features of productive data cleaning and acquisition, with practical coding examples using Python to test your skills. Learn about the organizational value of clean high-quality data, developing your ability to recognize common errors and quickly fix them as you go. Along the way, Miki offers cleaning strategies that can help optimize your workflow, including tips for causal analysis and easy-to-use tools for error prevention.
This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the “Using GitHub Codespaces with this course” video to learn how to get started.
Learning objectives
- Identify and describe common types of data errors such as missing values, bad values, and duplicated data.
- Apply methods to detect and handle missing values using techniques such as isnull, fillna, and forward filling.
- Analyze and address bad values using statistical methods, groupby, and custom functions.
- Explain the importance of domain knowledge in validating data and setting constraints for data columns.
- Implement data validation and cleaning techniques using libraries such as Pandera.
- Design effective UI and system schema to minimize data entry errors and ensure data integrity.
- Generate tidy data by reshaping wide-format data into long-format using pandas melt function.
- Develop strategies to detect and handle corrupted files using digital signatures and hashes.
- Construct robust data pipelines with validation and error reporting mechanisms.
- Formulate methods to handle missing or invalid data by either filling, ignoring, or transforming the data.
![Data Cleaning in Python Essential Training [Updated: 10/10/2025]](https://pixhost.icu/avaxhome/b9/b93b/b93b52ec7d7c4dbf9d6ef3409c748f23-8737827744473566255_medium.webp)
Data Cleaning in Python Essential Training [Updated: 10/10/2025]
![Data Cleaning in Python Essential Training [Updated: 10/10/2025]](https://pixhost.icu/avaxhome/b9/b93b/b93b52ec7d7c4dbf9d6ef3409c748f23-8737827744473566255.webp)