Introduction to Data Preparation
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What’s Included
Introducing Data Preparation
Introducing Data Preparation
Data preparation helps you get your data ready for analysis. This lesson introduces the need for good data preparation, and outlines the course case study.
Benefits of Well-Prepared Data
Benefits of Well-Prepared Data
Preparing your data effectively improves the speed and quality of your data analysis. Learn about these benefits in this lesson.
The Data Preparation Process
The Data Preparation Process
There are five steps involved in preparing your data for analysis: data gathering, data exploration, data cleansing and transformation,data storage, and data use and maintenance. These steps are introduced in this lesson.
Gathering Data
Gathering Data
The first step in preparing data is to gather the relevant data for your project. This lesson introduces the important considerations when gathering your data.
Exploring Data
Exploring Data
After gathering data, you should explore it to understand the data and identify any issues that need to be fixed. This lesson explains how to do this.
Cleansing and Transforming Data
Cleansing and Transforming Data
Cleansing and transforming data is the main step in preparing your data for analysis. In this lesson, we'll introduce some of the tasks you need to get your dataset ready for analysis.
Managing Missing Data and Outliers
Managing Missing Data and Outliers
Real-world datasets often include missing data and unusual values. This lesson will show you how to deal with these issues to ensure your dataset is as accurate as possible.
Formatting Datasets Correctly
Formatting Datasets Correctly
Datasets usually need to be in a particular format for data analysis. In this lesson, we'll explain how your data should be formatted.
Storing Data
Storing Data
Once you've finished transforming your data, you need to store it somewhere. This lesson explains what factors you should consider when making this decision.
Using and Maintaining Data
Using and Maintaining Data
After preparing your data, you should consider how to maintain the dataset during and after your analytics project, in order to boost reusability. This lesson explains how to do this.
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