Data wrangling is a set of processes for turning raw and messy data into a clean format to answer interesting questions from the data. In this course, you will learn the three phases of data wrangling: gathering, assessing, and cleaning data.
Overview
Syllabus
- Introduction to Data Wrangling
- You will learn what data wrangling is and why it matters. And you will see a real-world example of data wrangling and some common misconceptions about data wrangling.
- Gathering Data
- You will learn to implement data gathering methods to obtain and extract data from various sources and in several popular data formats.
- Assessing Data
- You will learn to identify different data quality and structural issues and apply visual and programmatic assessments to catch them.
- Cleaning Data
- You will learn to remediate the issues you identified in the assessment stage and test that your data cleaning is successful.
- Real World Data Wrangling with Python
- You will apply the skills you acquired in the course by gathering, assessing, and cleaning multiple real-world datasets of your choice.
Taught by
cd1827 Ria Cheruvu
