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Exploring and Analyzing Data in Python

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

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4 Hours14 Videos49 Exercises4150 XP

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Course Description

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.

For example, you’ll examine how alcohol use and student performance are related. 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!
  1. 1

    Getting to Know a Dataset

    Free

    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.

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    Initial exploration
    50 xp
    Functions for initial exploration
    100 xp
    Counting categorical values
    100 xp
    Global unemployment in 2021
    100 xp
    Data validation
    50 xp
    Detecting data types
    100 xp
    Validating continents
    100 xp
    Validating range
    100 xp
    Data summarization
    50 xp
    Summaries with .groupby() and .agg()
    100 xp
    Named aggregations
    100 xp
    Visualizing categorical summaries
    100 xp
  2. 3

    Relationships in data

    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.

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  3. 4

    Turning Exploratory Analysis into Action

    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!

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Datasets

unemployment.csvdata_science_salaries.csvbooks.csvdivorce.csv

Collaborators

amy-4121b590-cc52-442a-9779-03eb58089e08
Amy Peterson
mahamfaisalkhan
Maham Khan
George Boorman Headshot

George Boorman

Analytics and Data Science Curriculum Manager, DataCamp

George is an Analytics and Data Science Curriculum Manager at DataCamp. He holds a PGDip in Exercise for Health and BSc (Hons) in Sports Science and has experience in project management across public health, applied research, and not-for-profit sectors. George is passionate about sports, tech for good, and all things data science.
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Izzy Weber Headshot

Izzy Weber

Curriculum Manager, DataCamp

Izzy is a Curriculum Manager at DataCamp. She discovered a love for data during her seven years as an accounting professor at the University of Washington. She holds a masters degree in taxation and is a Certified Public Accountant. Her passion is making learning technical topics fun for students.
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