Clean Data: Tips, Tricks, and Techniques - Udemy

"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.

About the Author

Tomasz Lelek is a software engineer who programs mostly in Java and Scala. He is a fan of microservice architectures and functional programming. He dedicates considerable time and effort to being better every day. Recently, he's been delving into big data technologies such as Apache Spark and Hadoop. He is passionate about nearly everything associated with software development.

Tomasz thinks that we should always try to consider different solutions and approaches before solving a problem. Recently, he was a speaker at several conferences in Poland—Confitura and JDD (Java Developer's Day)—and also at Krakow Scala User Group. You can find his JDD video ML Spark talk. He also conducted a live coding session at Geecon Conference. He is currently working with TypeScript. The following links showcase his work: