Most data science projects that use Python will require you to access and integrate different types of data from a variety of external sources. This course will give you experience identifying and integrating data from spreadsheets, text files, websites, and databases. To prepare for downstream analyses, you first need to integrate any external data sources into your Python program. You will utilize existing packages and develop your own code to read data from a variety of sources. You will also practice using Python to prepare disorganized, unstructured, or unwieldy datasets for analysis by other stakeholders.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Constructing Expressions in Python
- Writing Custom Python Functions, Classes, and Workflows
- Developing Data Science Applications
- Creating Data Arrays and Tables in Python
Key Course Takeaways
- Read data from files, spreadsheets, websites, and databases
- Create a standard for organizing data within a dataset
- Filter, integrate, and prepare data for analysis
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How It Works
3-5 hours per week
100% online, instructor-led
Cornell University Center for Advanced Computing
Senior Research Associate / Adjunct Professor Cornell University Center for Advanced Computing
Bio coming soon
Who Should Enroll
- Data analysts and business analysts
- Database managers
- Technical and systems analysts
- Programmers interested in data science
- Business managers
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