What's the best way to approach a new dataset? Learn to validate and summarize categorical and numerical data and create Seaborn visualizations to communicate your findings.

Initial exploration

Functions for initial exploration

Counting categorical values

Global unemployment in 2021

Data validation

Detecting data types

Validating continents

Validating range

Data summarization

Summaries with .groupby() and .agg()

Named aggregations

Visualizing categorical summaries

Getting to Know a Dataset

Exploring and analyzing data often means dealing with missing values, incorrect data types, and outliers. In this chapter, you’ll learn techniques to handle these issues and streamline your EDA processes!

Addressing missing data

Dealing with missing data

Strategies for remaining missing data

Imputing missing plane prices

Converting and analyzing categorical data

Finding the number of unique values

Flight duration categories

Adding duration categories

Working with numeric data

Flight duration

Adding descriptive statistics

Handling outliers

Options

Remove outlier(s)

Leave outliers in dataset

What to do with outliers

Identifying outliers

Removing outliers

Data Cleaning and Imputation

Variables in datasets don't exist in a vacuum; they have relationships with each other. In this chapter, you'll look at relationships across numerical, categorical, and even DateTime data, exploring the direction and strength of these relationships as well as ways to visualize them.

Patterns over time

Importing DateTime data

Updating data type to DateTime

Visualizing relationships over time

Correlation

Interpreting a heatmap

Visualizing variable relationships

Visualizing multiple variable relationships

Factor relationships and distributions

Categorical data in scatter plots

Exploring with KDE plots

Relationships in Data

Exploratory data analysis is a crucial step in the data science workflow, but it isn't the end! Now it's time to learn techniques and considerations you can use to successfully move forward with your projects after you've finished exploring!

Considerations for categorical data

Checking for class imbalance

Cross-tabulation

Generating new features

Extracting features for correlation

Calculating salary percentiles

Categorizing salaries

Generating hypotheses

Comparing salaries

Choosing a hypothesis

Congratulations

Turning Exploratory Analysis into Action

unemployment.csv

data_science_salaries.csv

books.csv

divorce.csv

planes.csv

Course Glossary

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.

Introduction to Statistics in Python

Introduction to Data Visualization with Seaborn

Learn about exploratory data analysis in Python with this four-hour course. Use real-world data to clean, explore, visualize, and extract insights.

Exploratory Data Analysis in Python

Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.

Associate Data Scientist  in Python

Data Analyst  in Python

Python Data Fundamentals

A temperature sensor has a recording of 100 degrees Celsius, but the sensor only functions correctly at temperatures of up to 80 degrees.

A participant in a study has an age of minus 35 years old.

Countries have an average total area of 667,143 sq. km, but one country is recorded as 1,637,687 sq. km.

The heights of different animals are recorded and one is more than 1.5 times the IQR plus the 75th percentile.

A basketball player averages 35 points per game when the average across the league is only 10 points per game.

What to do with outliers

Exploratory Data Analysis in Python

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