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HomeHomework Helpdata-scienceData Integration and Formatting

Data Integration and Formatting

The process of combining and formatting data from different sources, such as spreadsheets or databases, to produce a cohesive and error-free output, including techniques for matching fields, handling typos, and ensuring correct formatting

intermediate
3 hours
Data Science
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Overview

Data integration and formatting are essential skills in data science, enabling the combination of data from various sources into a coherent format for analysis. Understanding the different types of data sources, transformation techniques, and ensuring data quality are crucial steps in this process. ...

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Key Terms

Data Source
A location where data originates, such as databases or files.

Example: A CSV file containing sales data.

Data Transformation
The process of converting data from one format to another.

Example: Changing date formats from MM/DD/YYYY to DD/MM/YYYY.

Data Quality
The condition of data based on factors like accuracy and completeness.

Example: Data with missing values is considered low quality.

ETL
Extract, Transform, Load; a process for moving data from one system to another.

Example: ETL is used to move data from a database to a data warehouse.

Normalization
The process of organizing data to reduce redundancy.

Example: Splitting a customer table into separate tables for orders and customers.

Data Pipeline
A series of data processing steps to move data from source to destination.

Example: A pipeline that extracts data from a database, transforms it, and loads it into a data warehouse.

Related Topics

Data Visualization
The graphical representation of data to help understand trends and insights.
intermediate
Big Data Analytics
Analyzing large and complex data sets to uncover patterns and insights.
advanced
Machine Learning
A subset of AI that uses algorithms to analyze data and make predictions.
advanced

Key Concepts

Data SourcesData TransformationData QualityData Storage