This is the code repository for Clean Data: Tips, Tricks, and Techniques [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
"Give me six hours to chop down a tree and I will spend the first four sharpening the axe"? Do you apply the same principle when doing Data Science?
Effective data cleaning is one of the most important aspects of good Data Science and involves acquiring raw data and preparing it for analysis, which, if not done effectively, will not give you the accuracy or results that you're looking to achieve, no matter how good your algorithm is. Data Cleaning is the hardest part of big data and ML. To address this matter, this course will equip you with all the skills you need to clean your data in Python, using tried and tested techniques. You'll find a plethora of tips and tricks that will help you get the job done, in a smart, easy, and efficient way.
- Learn to spot outliers in your data and analyze sensor data to find omissions
- Tokenize data and clean stop words to make it more robust
- Analyze and extract features from unstructured text data
- Clean and handle duplicates in your big data analytics and statistics
- Find and remove global row duplicates
- Learn to handle data cleaning for numbers
To fully benefit from the coverage included in this course, you will need:
Data scientists who want to get the most out of their data analysis and learn the best ways and techniques to ensure that their data is clean and ready for analysis.This course has the following software requirements:
A basic system with 4 GB of RAM and 10 GB of memory is ideal for this course