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Introduction to Importing Data in Python
4.8(1,439)338k studentsBEGINNEREnglish
Korshub's take
Editor's Take
Before any data analysis or machine learning modeling can begin, data must be loaded into Python efficiently. This introductory course covers techniques for ingesting data across a spectrum of sources, ranging from standard flat files to proprietary software formats and relational database management systems.
Who It's For
Essential for beginner data analysts and prospective data scientists establishing Python data ingestion pipelines.
Key Takeaways
- Flat File Ingestion: Load text and CSV files seamlessly into Python analysis environments.
- Proprietary Formats: Import native files from Excel, Stata, SAS, and MATLAB software packages.
- Relational Database Connections: Query and load dataset tables from SQLite and PostgreSQL relational databases.
- CPE Assessment: Complete the qualified assessment with a score of 70% or higher to receive CPE credits.
About this course
As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.
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.
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