Skills you'll learn:
Data cleaningData storage
Advanced Data Wrangling
Course
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.
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.
Intermediate
3 weeks
Last Updated November 18, 2024
Prerequisites:
Basic PythonBasic descriptive statisticsPandas
Intermediate
3 weeks
Last Updated November 18, 2024
Skills you'll learn:
Data cleaning • Data storage • Data Tidiness Assessment • Data gathering
Prerequisites:
Basic Python • Basic descriptive statistics • Pandas
Course Lessons
Lesson 1
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.
Lesson 2
Gathering Data
You will learn to implement data gathering methods to obtain and extract data from various sources and in several popular data formats.
Lesson 3
Assessing Data
You will learn to identify different data quality and structural issues and apply visual and programmatic assessments to catch them.
Lesson 4
Cleaning Data
You will learn to remediate the issues you identified in the assessment stage and test that your data cleaning is successful.
Lesson 5 • Project
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 The Best
Ria Cheruvu
AI Software Architect
Ria is an AI Software Architect and technical lead at Intel. She has a master's in data science from Harvard University, and is an accomplished industry speaker and instructor. She formerly served as Intel NEX’s AI Ethics Lead Architect, leading trustworthy AI product creation, and as a Teaching Fellow for Harvard Data Science. Ria has multiple patents and publications on AI and ethics, and enjoys contributing to open-source communities to advance innovation.
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