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Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.
So you’ve got some interesting data - where do you begin your analysis? This course will cover the process of exploring and analyzing data, from understanding what’s included in a dataset to incorporating exploration findings into a data science workflow.
Using data on unemployment figures and plane ticket prices, you’ll leverage Python to summarize and validate data, calculate, identify and replace missing values, and clean both numerical and categorical values. Throughout the course, you’ll create beautiful Seaborn visualizations to understand variables and their relationships.
Finally, the course will show how exploratory findings feed into data science workflows by creating new features, balancing categorical features, and generating hypotheses from findings.
By the end of this course, you’ll have the confidence to perform your own exploratory data analysis (EDA) in Python.You’ll be able to explain your findings visually to others and suggest the next steps for gathering insights from your data!
The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.
So you’ve got some interesting data - where do you begin your analysis? This course will cover the process of exploring and analyzing data, from understanding what’s included in a dataset to incorporating exploration findings into a data science workflow.
Using data on unemployment figures and plane ticket prices, you’ll leverage Python to summarize and validate data, calculate, identify and replace missing values, and clean both numerical and categorical values. Throughout the course, you’ll create beautiful Seaborn visualizations to understand variables and their relationships.
Finally, the course will show how exploratory findings feed into data science workflows by creating new features, balancing categorical features, and generating hypotheses from findings.
By the end of this course, you’ll have the confidence to perform your own exploratory data analysis (EDA) in Python.You’ll be able to explain your findings visually to others and suggest the next steps for gathering insights from your data!
The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.
