深受数千家公司学习者的喜爱
培训2人或更多？
试用DataCamp for Business课程描述
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.
先决条件
Introduction to Statistics in PythonIntroduction to Data Visualization with Seaborn1
Getting to Know a Dataset
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.
2
Data Cleaning and Imputation
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!
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.
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!
Exploratory Data Analysis in Python
课程完成 通过 DataCamp for Mobile 提升您的数据技能
随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。