Clean Data: Tips, Tricks, and Techniques

Clean Data: Tips, Tricks, and Techniques

Updated Dec 24, 2019

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.  

Target Audience 

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.   

Business Outcomes 

  • Sift through your data to identify issues such as outliers, missing values, and duplicate rows 

  • Deal with unstructured data in the most effective ways and hone your skills in transforming and combining your data 

  • Use Python to check your data for consistency and get rid of any missing or duplicated data