Codecademy
Free Trial Available
English
Certificate Available
10 hours worth of material
selfpaced
Overview
Learn about exploratory data analysis (EDA) techniques.
In this course, you will learn about exploratory data analysis techniques in Python, including:
- EDA for data preparation
- Summary statistics
- Data visualization techniques
- EDA prior to building a machine learning model
Prior to taking this course, you should have some knowledge of base Python and experience with pandas DataFrames.
Exploratory data analysis is an important part of any Data Scientist or Analyst's workflow, so we highly recommend this course for anyone who is interested in working with data.
In this course, you will learn about exploratory data analysis techniques in Python, including:
- EDA for data preparation
- Summary statistics
- Data visualization techniques
- EDA prior to building a machine learning model
Prior to taking this course, you should have some knowledge of base Python and experience with pandas DataFrames.
Exploratory data analysis is an important part of any Data Scientist or Analyst's workflow, so we highly recommend this course for anyone who is interested in working with data.
Syllabus
- Introduction to EDA: Learn about exploratory data analysis and what it is used for.
- What is EDA?
- Inspect, Clean, and Validate a Dataset: Learn how to use exploratory data analysis (EDA) to inform data inspection, cleaning, and validation.
- EDA: Inspect, Clean, and Validate a Dataset
- EDA: Diagnosing Diabetes
- Summarizing a Single Feature: Learn how to explore a single feature in a dataset using summary statistics and simple data visualizations.
- Data Summaries
- EDA: Data Summaries
- Exploring Student Data
- Aggregates in Pandas: Learn how to use aggregate functions in pandas to calculate tables of summary statistics.
- Aggregates in Pandas
- Aggregates in Pandas
- A/B Testing for ShoeFly.com
- Summarizing the Relationship between Two Features: Learn how to investigate whether there is an association between two variables.
- Associations: Quantitative and Categorical Variables
- Associations: Two Quantitative Variables
- Associations: Two Categorical Variables
- Associations between Variables
- NBA Trends
- Advanced Data Visualization: Learn about advanced data visualization techniques for exploratory data analysis (EDA) in Python.
- Exploratory Data Analysis: Data Visualization
- Visualizing Multivariate Relationships
- Visualizing Time Series Data
- Data Visualizations for Messy Data
- Airline Analysis
- EDA for Machine Learning Models: Learn about exploratory data analysis techniques that are important prior to building a machine learning model.
- EDA Prior to Fitting a Regression Model
- EDA Prior to Fitting a Classification Model
- EDA Prior to Unsupervised Clustering
