Data Cleaning and Processing for Data Scientists
Data Scientists spend most of their time cleaning and processing their data before they can be leveraged for future predictions. This course will teach you the various data cleaning and processing techniques and how to leverage the cloud services and AI tools to accomplish them.
What you'll learn
Properly cleaning and processing the data is crucial to ensure that the subsequent data modeling produces accurate, meaningful, and reliable data.
In this course, Data Cleaning and Processing for Data Scientists, you’ll gain the ability to learn the various techniques to pre-process the data that can be used to generate accurate analysis, which will lead to effective decision-making.
First, you’ll explore the various data-cleaning techniques and address data with missing values, duplicate data, and outliers.
Next, you’ll discover some of the transformation techniques like min-max scaler, standard scaler, one-hot encoding, and dimensionality reduction.
Finally, you’ll learn how to leverage the cloud services and AI tools and automate these tasks to achieve results quickly.
When you’re finished with this course, you’ll have the skills and knowledge of cleaning and processing the data needed to generate high-quality data for enhanced decision-making.
Table of contents
About the author
I have worked in IT design, development, and architecture for over a decade for some of the top fortune 100 companies. I have designed and architected enterprise applications and developed scalable and portable software. I am a Google Certified Professional Architect. Critical areas where I have worked are architecture and design using Java, ESB, Tomcat, ReactJS, JavaScript, Linux, Oracle, SVN, GIT, and so on, and cloud technologies, including AWS and GCP.