Clean Data: Tips, Tricks, and Techniques

Use Python to check your data consistency and get rid of any missing or duplicate data

"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.

All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Clean-Data-Tips-Tricks-and-Techniques

Style and Approach

Each section teaches one particular aspect of the overall topic and its section title reflects that. Each video teaches a subtopic in a hands-on way with a practical demonstration, along with explanation and a discussion of how it works and how to use it.

What You Will Learn

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.